SC Langnau Tigers: An In-Depth Analysis for Sports Bettors
Overview of SC Langnau Tigers
The SC Langnau Tigers are a professional ice hockey team based in Langnau im Emmental, Switzerland. They compete in the Swiss League (NL), which is the top-tier league in Swiss ice hockey. Founded in 1934, the team has a storied history and is managed by head coach [Current Coach Name]. Known for their passionate fanbase and strategic gameplay, the Tigers have become a significant presence in Swiss hockey.
Team History and Achievements
Over the years, the SC Langnau Tigers have secured several titles and awards. They have won multiple NL championships and have consistently been among the top teams in league standings. Notable seasons include their championship runs and record-breaking performances. The team has also produced numerous players who have gone on to play in international leagues.
Current Squad and Key Players
The current squad boasts a mix of experienced veterans and promising young talent. Key players include [Star Player 1], known for his exceptional scoring ability, and [Star Player 2], a defensive stalwart. The team’s roster features positions such as forwards, defensemen, and goaltenders, each contributing to the team’s overall success.
Team Playing Style and Tactics
The SC Langnau Tigers employ a dynamic playing style characterized by aggressive offense and solid defense. Their formation often emphasizes quick transitions from defense to attack, leveraging speed and skill. Strengths include strong teamwork and adaptability, while weaknesses may involve occasional lapses in defensive coverage.
Interesting Facts and Unique Traits
The Tigers are affectionately nicknamed “The Lions” by their fans. They have a dedicated fanbase known for their vibrant support during home games at the Sportzentrum Litterna arena. Rivalries with teams like EHC Biel add excitement to their matches, while traditions such as pre-game rituals enhance the fan experience.
Lists & Rankings of Players, Stats, or Performance Metrics
- Top Scorer: [Player Name] – ✅ Consistent goal-scoring leader
- Best Defenseman: [Player Name] – 🎰 Key player in defensive strategies
- All-Star Team: Featuring standout performers from various positions – 💡 Highlighting key contributors
Comparisons with Other Teams in the League or Division
In comparison to other teams in the NL, the SC Langnau Tigers are known for their balanced approach between offense and defense. While some teams may focus more on power play strategies, the Tigers excel in maintaining control during even-strength situations.
Case Studies or Notable Matches
A notable match that highlights the team’s potential was their victory against [Opponent Team] where they showcased exceptional teamwork and strategic execution. This game is often cited as a breakthrough performance that demonstrated their capability to dominate top-tier opponents.
Tables Summarizing Team Stats, Recent Form, Head-to-Head Records, or Odds
| Statistic | Last Season | This Season (to date) |
|---|---|---|
| Total Wins | [Number] | [Number] |
| Total Goals Scored | [Number] | [Number] |
Tips & Recommendations for Analyzing the Team or Betting Insights
- Analyze recent form trends to gauge momentum before placing bets.
- Consider head-to-head records against upcoming opponents for insights into potential outcomes.
- Maintain awareness of key player performances as they can significantly influence game results.
Frequently Asked Questions (FAQ)
What are some key strengths of SC Langnau Tigers?
Their strengths lie in strong teamwork, strategic gameplay, and a balanced roster capable of both offensive prowess and defensive resilience.
Who are some notable players to watch?</h3
[Star Player 1] is renowned for his scoring ability, while [Star Player 2] is pivotal in defense. Both players are crucial to understanding betting dynamics involving this team.
How does SC Langnau perform against its rivals?</h3
The Tigers have had mixed results against rivals like EHC Biel but generally perform well due to strategic adaptability.
Betting on SC Langnau: What should I consider?</h3
Evaluate recent form, head-to-head matchups against upcoming opponents, and individual player performances when betting on this team.
Pros & Cons of Current Form or Performance</h3
- Prominent Pros:
- Momentum from recent victories ✅ Strong leadership from coaching staff ✅ High morale among players ✅ Effective use of tactical formations 🎰
- Prominent Cons:</lDimitrisBouras/ML_2020/assignment_4/mnist.py
import numpy as np
import torch.nn.functional as F
from assignment_4.mnist_dataset import MNISTDataset
from sklearn.decomposition import PCA
class MNIST:
def __init__(self):
self.train = MNISTDataset(train=True)
self.test = MNISTDataset(train=False)
self.pca = PCA(n_components=64)
self.pca.fit(self.train.X)
def get_pca_data(self):
X_train_pca = self.pca.transform(self.train.X)
X_test_pca = self.pca.transform(self.test.X)
return X_train_pca.astype(np.float32), X_test_pca.astype(np.float32)
def get_one_hot_labels(self):
y_train_one_hot = np.zeros((len(self.train.y), 10))
y_test_one_hot = np.zeros((len(self.test.y), 10))
for i,y_i in enumerate(self.train.y):
y_train_one_hot[i][y_i] = 1.
for i,y_i in enumerate(self.test.y):
y_test_one_hot[i][y_i] = 1.
return y_train_one_hot.astype(np.float32), y_test_one_hot.astype(np.float32)
def get_minibatches(self,batch_size):
for start_idx in range(0,len(self.train.X)-batch_size+1,batch_size):
mid_idx = min(start_idx+batch_size,len(self.train.X))
yield self.train.X[start_idx:mid_idx],self.train.y[start_idx:mid_idx]
def get_minibatches_(self,batch_size):
for start_idx in range(0,len(self.test.X)-batch_size+1,batch_size):
mid_idx = min(start_idx+batch_size,len(self.test.X))
yield self.test.X[start_idx:mid_idx],self.test.y[start_idx:mid_idx]DimitrisBouras/ML_2020<|file_sepDimitrisBouras/ML_2020<|file_sep# Machine Learning Fall 2020
## Assignment Description:
**Assignment 01:**
In this assignment you will implement your own version of stochastic gradient descent (SGD) using PyTorch.
You will then test your implementation on two different tasks:
* Classification task using logistic regression model.
* Regression task using linear regression model.
**Assignment 02:**
In this assignment you will implement three different neural networks using PyTorch.
You will then test your implementation on two different tasks:
* Classification task using multilayer perceptron (MLP).
* Regression task using convolutional neural network (CNN).
**Assignment 03:**
In this assignment you will implement an autoencoder using PyTorch.
You will then test your implementation on two different datasets:
* Handwritten digits dataset.
* Fashion-MNIST dataset.
**Assignment 04:**
In this assignment you will implement three different neural networks using PyTorch.
You will then test your implementation on two different tasks:
* Classification task using multilayer perceptron (MLP).
* Classification task using convolutional neural network (CNN).DimitrisBouras/ML_2020<|file_sep
import numpy as np
import torch
import torch.nn.functional as F
from assignment_4.mnist_dataset import MNISTDataset
class MLP:
def __init__(self,input_dim,output_dim,num_hidden_units):
self.input_dim = input_dim
self.output_dim = output_dim
self.num_hidden_units = num_hidden_units
def forward_propagation_(self,x):
def train(model,X,y,num_epochs=20000,batch_size=100,alpha=0.01,lambd=0.):
num_examples=X.shape[0]
cost_history=np.zeros(num_epochs)
model.set_mode_train()
for epoch_i in range(num_epochs):
return cost_history
def predict(model,X):
model.set_mode_eval()
return y_pred
def compute_cost(model,X,y,lambd=0.):
return cost
if __name__ == "__main__":
mnist=MNIST()
X_train,y_train=mnist.get_pca_data()
y_train=y_train.reshape(-1)
X_test,y_test=mnist.get_pca_data()
y_test=y_test.reshape(-1)
num_examples=X.shape[0]
input_dim=X.shape[1]
output_dim=len(set(y))
model=MLP(input_dim=input_dim,output_dim=output_dim,num_hidden_units=[256])
cost_history=train(model,X,y,num_epochs=20000,batch_size=100,alpha=0.,lambd=0.)
plt.plot(cost_history)
plt.xlabel("Epoch")
plt.ylabel("$mathcal{L}(X;Y)$")
plt.title("Cost vs Epoch")
plt.show()> Assignment description:
In this assignment you will implement an autoencoder using PyTorch.
You will then test your implementation on two different datasets:
Handwritten digits dataset.
Fashion-MNIST dataset.DimitrisBouras/ML_2020<|file_sep sbt clean compile assembly package <|file_sep * {
margin: 0;
padding: 0;
}
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background-color: #f5f5f5;
font-family: 'Lato', sans-serif;
}
header {
width:100%;
height:auto;
background-color:#ffffff;
padding-top:20px;
padding-bottom:20px;
border-bottom-style:solid;
border-bottom-width:thin;
}
#container {
width:auto;
max-width:960px;
margin-left:auto;
margin-right:auto;
}
nav {
float:right;
}
nav ul li {
display:inline-block;
}
nav ul li:hover {
background-color:#eeeeee;
}
a {
text-decoration:none !important;
}
article {
width:auto;
max-width:960px;
min-height:auto;
margin-left:auto;
margin-right:auto;
background-color:#ffffff;
padding-top:20px;
padding-bottom:20px;
}
section hgroup h1 {
color:#333333;
}
section hgroup p {
color:#666666;
}
section img {
max-width:100%;
}
footer {
width:auto;
max-width:960px;
min-height:auto;
margin-left:auto;
margin-right:auto;
background-color:#ffffff;
padding-top:20px;
padding-bottom:20px;
}
footer p {
color:#666666;
}<|file_sep# Machine Learning Fall 2019
## Assignment Description:
**Assignment #01:**
In this assignment you need to implement stochastic gradient descent algorithm from scratch.
Then you need to use it to train a logistic regression classifier.
**Assignment #02:**
In this assignment you need to implement backpropagation algorithm from scratch.
Then you need to use it to train both linear regressor as well as multilayer perceptron classifier.DimitrisBouras/ML_2020> Assignment description:
In this assignment you will implement three different neural networks using PyTorch.
You will then test your implementation on two different tasks:
Classification task using multilayer perceptron (MLP).
Regression task using convolutional neural network (CNN).DimitrisBouras/ML_2020<|file_sep unclear how many hidden layers we want
need hyperparameter search
nonlinear activation functions
different weight initialization schemes
different weight update rules
regularization
dropout
use validation set instead of training set
add early stopping
add learning rate decay
create plots
add progress bar
what if we don't know how many hidden layers we want?
how do we know what activation function(s) should we use?
what weight initialization scheme should we use?
which weight update rule should we use?
how do we regularize our model? what kind of regularization should we use?DimitrisBouras/ML_2020 unsupervised learning
with labels -> supervised learning
classification problem -> categorical variables -> discrete labels -> classification loss functions -> cross entropy loss function -> softmax function -> categorical cross entropy loss function
regression problem -> continuous variables -> continuous labels -> regression loss functions -> mean squared error loss function
linear model + sigmoid activation function + binary cross entropy loss function => logistic regression model => binary classification problem
linear model + softmax activation function + categorical cross entropy loss function => multinomial logistic regression model => multi-class classification problem
linear model + identity activation function + mean squared error loss function => linear regression model => regression problem
layers w/o nonlinearities => linear models
fully connected layer followed by nonlinearities => deep neural network
convolutional layer followed by nonlinearities => convolutional neural network
pooling layer followed by nonlinearities => pooling layer
flatten layer followed by fully connected layer followed by nonlinearities => fully connected layer
convolutional layer followed by pooling layer followed by flatten layer followed by fully connected layer followed by nonlinearities => convolutional neural network
fully connected layers only ?????
convolutional layers only ?????
Show / Hide Solution Code For Exercise #01:
python
import numpy as np
class LinearModel():
def __init__(self,input_dim,output_dim):
self.input_dim=input_dim
self.output_dim=output_dim
python
class LogisticRegression(LinearModel):
python
python
python
python
python
python
python
### Example Usage:
python
python
### Output:
details open=””>
Show / Hide Solution Code For Exercise #02:
python
python
### Example Usage:
python
python
### Output:
details open=””>
Show / Hide Solution Code For Exercise #03:
python
### Example Usage:
python
python
### Output:
details open=””>
Show / Hide Solution Code For Exercise #04:
python
### Example Usage:
python
python
### Output:
details open=””>
Show / Hide Solution Code For Exercise #05:
`r”””
This exercise is not part of our original homework assignments,
but was provided later after I realized that my students were struggling with implementing backpropagation algorithm from scratch,
so I decided it would be useful if they had access to sample code that implements backpropagation algorithm so that they could better understand how it works.
“””`
#### Forward Propagation Algorithm:
##### Linear Layer:
##### Activation Functions:
##### Loss Functions:
##### Backward Propagation Algorithm:
##### Linear Layer:
##### Activation Functions:
##### Loss Functions:
#### Backward Propagation Algorithm Implemented Using Python Classes And Methods:
#### Backward Propagation Algorithm Implemented Using Numpy Operations Only:
#### Example Usage Of Backpropagation Algorithm To Train A Multilayer Perceptron Classifier On The Iris Dataset: details open=””>
Show / Hide Solution Code For Exercise #06:
`r”””
This exercise is not part of our original homework assignments,
but was provided later after I realized that my students were struggling with implementing backpropagation algorithm from scratch,
so I decided it would be useful if they had access to sample code that implements backpropagation algorithm so that they could better understand how it works.
“””`
#### Forward Propagation Algorithm:
##### Linear Layer:
##### Activation Functions:
##### Loss Functions:
#### Backward Propagation Algorithm:
##### Linear Layer:
##### Activation Functions:
##### Loss Functions:
#### Backward Propagation Algorithm Implemented Using Python Classes And Methods: details open=””>
Show / Hide Solution Code For Exercise #07:
`r”””
This exercise is not part of our original homework assignments,
but was provided later after I realized that my students were struggling with implementing backpropagation algorithm from scratch,
so I decided it would be useful if they had access to sample code that implements backpropagation algorithm so that they could better understand how it works.
“””`
#### Forward Propagation Algorithm:
##### Linear Layer:
##### Activation Functions:
##### Loss Functions:
#### Backward Propagation Algorithm:
##### Linear Layer:
##### Activation Functions:
##### Loss Functions:
#### Backward Propagation Algorithm Implemented Using Numpy Operations Only: details open=””>
Show / Hide Solution Code For Exercise #08:
`r”””
This exercise is not part of our original homework assignments,
but was provided later after I realized that my students were struggling with implementing backpropagation algorithm from scratch,
so I decided it would be useful if they had access to sample code that implements backpropagation algorithm so that they could better understand how it works.
“””`
#### Forward Propagation Algorithm:
###### Dense Layers Without Nonlinear Activations Are Just Linear Models.
###### Fully Connected Layers Followed By Nonlinear Activations Are Deep Neural Networks.
###### Convolutional Layers Followed By Nonlinear Activations Are Convolutional Neural Networks.
###### Pooling Layers Followed By Nonlinear Activations Are Pooling Layers.
###### Flatten Layers Followed By Fully Connected Layers Followed By Nonlinear Activations Are Fully Connected Layers.
###### Convolutional Layers Followed By Pooling Layers Followed By Flatten Layers Followed By Fully Connected Layers Followed By Nonlinear Activations Are Convolutional Neural Networks.
###### Dense Layers Without Nonlinear Activations Are Just Linear Models.
###### Fully Connected Layers Followed By Nonlinear Activations Are Deep Neural Networks.
###### Convolutional Layers Followed By Nonlinear Activations Are Convolutional Neural Networks.
###### Pooling Layers Followed By Nonlinear Activations Are Pooling Layers.
###### Flatten Layers Followed By Fully Connected Layers Followed By Nonlinear Activations Are Fully Connected Layers.
###### Convolutional Layers Followed By Pooling Layer(s) Followed By Flatten Layer(s) Followed By Fully Connected Layer(s) FollowedByNonLinearActivationsAreConvolutionNeuralNetworks.
## Sigmoid Function And Binary Cross Entropy Loss Function Used Together Define Logistic Regression Model Which Is A Binary Classifier.
## Softmax Function And Categorical Cross Entropy Loss Function Used Together Define Multinomial Logistic Regression Model Which Is A Multi-Class Classifier.
## Identity Function And Mean Squared Error Loss Function Used Together Define Linear Regression Model Which Is A Regressor.
## How Do We Decide What Kind Of Model We Should Use?
## How Do We Decide How Many Hidden Units Our Network Should Have?
## How Do We Decide How Many Hidden Units Each Hidden Layer Should Have?
## How Do We Decide What Kind Of Activation Function Each Hidden Unit Should Use?
## How Do We Decide Which Weight Initialization Scheme We Should Use?
## How Do We Decide Which Weight Update Rule We Should Use?
## Why Do We Need To Regularize Our Model?
## What Kind Of Regularization Technique Should We Use?
## Why Do We Need Dropout?
# References:
[Goodfellow et al., Chapter Sixteen](http://www.deeplearningbook.org)
[Goodfellow et al., Chapter Fifteen](http://www.deeplearningbook.org)
[Goodfellow et al., Chapter Fourteen](http://www.deeplearningbook.org)
[Goodfellow et al., Chapter Thirteen](http://www.deeplearningbook.org)
[Goodfellow et al., Chapter Twelve](http://www.deeplearningbook.org)
[Goodfellow et al., Chapter Eleven](http://www.deeplearningbook.org)
[Goodfellow et al., Chapter Ten](http://www.deeplearningbook.org)
[A Gentle Introduction To Artificial Neural Networks Part One — From Perceptrons To Multilayer Perceptrons](https://medium.com/datadriveninvestor/a-gentle-introduction-to-artificial-neural-networks-part-one-from-perceptrons-to-multilayer-perceptrons-9a7c8b70c6b6)
[A Gentle Introduction To Artificial Neural Networks Part Two — From Multilayer Perceptrons To Deep Learning Frameworks](https://medium.com/datadriveninvestor/a-gentle-introduction-to-artificial-neural-networks-part-two-from-multilayer-perceptrons-to-deep-learning-frameworks-fd470646cbe8)
[Solved Assignments From Andrew Ng’s Coursera Course On Machine Learning Specialization At Stanford University — Part One — Week One Exercises Solutions In Python Implementations With Numpy Only And No Libraries Or Frameworks Involved!](https://medium.com/@dmitri.bourakivskyi/solved-assignments-from-andrew-ng-s-coursera-course-on-machine-learning-specialization-at-stanford-university-part-one-week-one-exercises-solutions-in-python-implementati-b7bf12de35b68)
[Solved Assignments From Andrew Ng’s Coursera Course On Machine Learning Specialization At Stanford University — Part Two — Week Two Exercises Solutions In Python Implementations With Numpy Only And No Libraries Or Frameworks Involved!](https://medium.com/@dmitri.bourakivskyi/solved-assignments-from-andrew-ng-s-coursera-course-on-machine-learning-specialization-at-stanford-university-part-two-week-two-exercises-solutions-in-python-implementati-d59b76bc7eb7)
[Solved Assignments From Andrew Ng’s Coursera Course On Machine Learning Specialization At Stanford University — Part Three — Week Three Exercises Solutions In Python Implementations With Numpy Only And No Libraries Or Frameworks Involved!](https://medium.com/@dmitri.bourakivskyi/solved-assignments-from-andrew-ng-s-coursera-course-on-machine-learning-specialization-at-stanford-university-part-three-week-three-exercises-solutions-in-pytho-44c22cd28c9c)
[Solved Assignments From Andrew Ng’s Coursera Course On Machine Learning Specialization At Stanford University — Part Four — Week Four Exercises Solutions In Python Implementations With Numpy Only And No Libraries Or Frameworks Involved!](https://medium.com/@dmitri.bourakivskyi/solved-assignments-from-andrew-ng-s-coursera-course-on-machine-learning-specialization-at-stanford-university-part-four-week-four-exercises-solutions-in-pytho-e16595db6b47)
[Solved Assignments From Andrew Ng’s Coursera Course On Machine Learning Specialization At Stanford University — Part Five — Week Five Exercises Solutions In Python Implementations With Numpy Only And No Libraries Or Frameworks Involved!](https://medium.com/@dmitri.bourakivskyi/solved-assignments-from-andrew-ng-s-courservia-course-on-machine-learning-specialization-at-stanford-university-part-five-week-five-exercises-solutions-in-python-implementati-a09cbba23fe9)
[Solving The XOR Problem Using A Single Neuron Without Any Hidden Units Is Impossible Because It Cannot Solve Problems That Aren’t Linearly Separable But Solving It Using A Single Neuron With One Hidden Unit Is Possible Because Now Our Model Can Solve Problems That Aren’t Linearly Separable As Well! This Video Will Show You Exactly How It Works Step-by-step In Detail So That You Can Understand It Thoroughly! https://youtu.be/Xz8QmUZ5KwM ](https:/youtu.be/Xz8QmUZ5KwM)
[Solving The XOR Problem Using A Single Neuron Without Any Hidden Units Is Impossible Because It Cannot Solve Problems That Aren’t Linearly Separable But Solving It Using A Single Neuron With One Hidden Unit Is Possible Because Now Our Model Can Solve Problems That Aren’t Linearly Separable As Well! This Video Will Show You Exactly How It Works Step-by-step In Detail So That You Can Understand It Thoroughly! https://youtu.be/Xz8QmUZ5KwM ](https:/youtu.be/Xz8QmUZ5KwM)
[Solving The XOR Problem Using A Single Neuron Without Any Hidden Units Is Impossible Because It Cannot Solve Problems That Aren’t Linearly Separable But Solving It Using A Single Neuron With One Hidden Unit Is Possible Because Now Our Model Can Solve Problems That Aren’t Linearly Separable As Well! This Video Will Show You Exactly How It Works Step-by-step In Detail So That You Can Understand It Thoroughly! https://youtu.be/Xz8QmUZ5KwM ](https:/youtu.be/Xz8QmUZ5KwM)
[Making Sense Of Recurrent Neural Networks – Understanding LSTM Inside Out – Step-by-step Explanation – https://youtu.be/gGkR6EJGJWs ](https:/youtu.be/gGkR6EJGJWs)
[Making Sense Of Recurrent Neural Networks – Understanding LSTM Inside Out – Step-by-step Explanation – https://youtu.be/gGkR6EJGJWs ](https:/youtu.be/gGkR6EJGJWs)
[Making Sense Of Recurrent Neural Networks – Understanding LSTM Inside Out – Step-by-step Explanation – https://youtu.be/gGkR6EJGJWs ](https:/youtu.be/gGkR6EJGJWs) /assignment_03/exercise.py | file_sep=’rn’ > import numpy as np
class AutoEncoder():
if __name__ == “__main__”:
pass
<|file_sep|introduction.mdwn — |
title:A Brief Introduction into Haskell Programming Language |
author:Dennis O'Sullivan |
date:today{} |
A Brief Introduction into Haskell Programming Language {#sec:introduction}
=====================================================
Introduction {#sec:intro}
————-
Haskell is one example out there amongst many functional programming languages which has evolved over time since its conception around twenty years ago cite{wiki:haskell}. Haskell borrows many concepts from other languages including Lisp cite{wiki:lisp}, ML cite{wiki:sml} ,and Miranda cite{wiki:miranda}. Although originally developed independently Haskell has now gained popularity through its inclusion into Glasgow Haskell Compiler cite{wiki:gch} which is used widely throughout industry cite{wiki:haskell-industry}.
The language itself uses lazy evaluation which means all expressions are evaluated when required rather than immediately when first encountered during compilation cite{wiki:haskell-lazy}. This feature allows developers greater flexibility when writing programs but comes at a price because although values may be calculated only once per program run these values must still be stored somewhere until no longer required at which point memory can be freed up again cite{wiki:haskell-lazy}. However thanks largely due again due largely due Glasgow Haskell Compiler’s efficient garbage collection mechanisms much more memory can be reclaimed than would otherwise be possible leading ultimately towards faster execution times compared those obtained via eager evaluation strategies employed elsewhere e.g OCaml etc.. Another benefit associated here too arises because lazy evaluation enables easier reasoning about programs given expressions remain unevaluated until explicitly requested allowing programmers focus solely upon desired outputs rather than worrying about side effects caused within intermediate calculations themselves thus making debugging simpler too!
Haskell also provides extensive support through type inference enabling programmers write concise code without having specify explicit types everywhere making programs easier read maintain modify further down line especially useful when dealing complex data structures e.g lists trees graphs etc.. Furthermore thanks again Glasgow Haskell Compiler’s sophisticated compiler infrastructure including optimisations performed automatically behind scenes e.g constant folding dead code elimination tail call optimisation etc.. leads towards highly efficient compiled binaries producing fast executing applications able run smoothly even under heavy loads!
Finally worth noting final advantage comes via powerful abstraction mechanisms available within language itself such modularity achieved via modules allowing programmers group related functionality together whilst keeping unrelated pieces separate thus avoiding clutter across large projects where organisation becomes increasingly important factor especially relevant today given growing complexity found modern software development practices such agile methodologies DevOps CI CD pipelines etc..
All things considered above therefore suggests clearly why Haskell remains popular choice amongst researchers practitioners alike particularly those interested exploring functional programming paradigms further developing new ideas building upon existing foundations laid down before them already providing fertile ground fertile ground continue pushing boundaries frontiers computing science today tomorrow onwards!
Basic Syntax {#sec:syntax}
————-
Haskell syntax closely resembles mathematical notation making programs easy read understand follow along even beginners familiarise themselves quickly basic concepts involved language usage itself moreover thanks comprehensive documentation tutorials available online helping newcomers learn effectively efficiently build practical experience working hands actual projects soon enough!
Functions {#sec:function}
———-
Functions represent core building blocks within Haskell programming language defined simply pair inputs outputs where each input mapped uniquely corresponding output value returned upon invocation according specified behaviour expressed through body containing expressions computations performed derive result finally returned caller invoking same initially defined manner previously discussed earlier section introduction overview general concepts underlying fundamentals principles driving design philosophy behind entire ecosystem surrounding toolchains technologies encompassing wide array diverse components ranging compilers interpreters libraries frameworks tools utilities various kinds supporting development activities carried out daily basis across multitude domains industries sectors worldwide today tomorrow onwards!
Data Types {#sec:data-type}
———-
Haskell provides rich collection built-in data types including primitive ones integers floating point numbers characters strings booleans lists tuples arrays vectors sets maps dictionaries hash tables bit vectors bitsets bit arrays bitmaps bit strings bit fields bits flags masks boolean matrices tensors vectors spaces coordinates points angles distances sizes shapes dimensions lengths widths heights depths scales factors ratios proportions percentages rates speeds velocities accelerations forces energies powers temperatures pressures densities masses volumes areas perimeters circumferences diameters radii circumferences arcs sectors segments chords secants tangents normals bisectors medians altitudes heights bases legs hypotenuses diagonals diagonals conjugates complements supplements orthocenters centroids incenters excenters circumcenters nine-point centers Fermat points Gergonne points isotomic conjugates isotomic points isotomic conjugates isotomic conjugates isotomic conjugates isotomic conjugates isotomic conjugates isotomic conjugates isotomic conjugates etcetera ad infinitum!
Modules {#sec,module}
——–
Modules provide mechanism grouping related functionality together keeping unrelated pieces separate avoiding clutter large projects organisation becoming increasingly important factor especially relevant today given growing complexity found modern software development practices such agile methodologies DevOps CI CD pipelines etcetera ad infinitum!
Type Classes {#sec:type-class}
————-
Type classes provide mechanism defining generic interfaces specifying behaviours expected types conforming same must adhere obey following similar patterns structure adherences enforced compile-time ensuring correctness reliability robustness dependability security trustworthiness safety stability consistency predictability verifiability provability soundness completeness decidability computability solvability tractability simplicity elegance beauty harmony unity perfection totality absoluteness omnipresence omnipotence omniscience omnipresence omniscience omnipotence omnipresence omniscience omnipotence!
Pattern Matching {#sec:patt-match}
——————
Pattern matching provides mechanism deconstructing data structures extracting individual components elements parts pieces fragments segments slices chunks chunkslets slivers splinters splinters splinters splinters splinters splinters splinters splinters splinters splinters splinters splinters chunkslets slivers fragments parts pieces elements components structures data extracting deconstructing mechanism pattern matching provides!
Recursion {#sec:rccur}
———-
Recursion provides mechanism defining functions calling themselves repeatedly performing computations iteratively until base case reached terminating recursion cycle returning final result computed derived accumulated aggregated collected gathered assembled constructed built created synthesized generated produced formulated devised invented contrived crafted designed engineered fabricated manufactured assembled constructed built created synthesized generated produced formulated devised invented contrived crafted designed engineered fabricated manufactured assembled constructed built created synthesized generated produced formulated devised invented contrived crafted designed engineered fabricated manufactured assembled constructed built created synthesized generated produced formulated devised invented contrived crafted designed engineered fabricated manufactured assembled constructed built created synthesized generated produced formulated devised invented contrived crafted designed engineered fabricated manufactured assembled constructed built created synthesized generated produced formulated devised invented contrived crafted designed engineered fabricated manufactured assembled constructed built created synthesized generated produced formulated devised invented contrived crafted designed engineered fabricated manufactured assembled constructed built!
Lazy Evaluation {#sec:lzy-eval}
—————
Lazy evaluation provides mechanism delaying computation until required evaluating expressions only once necessary reducing unnecessary work saving resources improving performance efficiency effectiveness productivity throughput scalability extensibility maintainability portability reusability interoperability compatibility interoperabili…
Higher-order functions {#sec:hofunc}
———————-
Higher-order functions provide mechanism passing functions arguments receiving functions return values allowing composition abstraction encapsulation decoupling separation concerns modularisation decomposition refinement specification refinement refinement refinement refinement refinement refinement refinement refinement refinement refinement…
Monads {#sec:mnd}
——-
Monads provide mechanism encapsulating side-effects managing stateful computations sequencing operations chaining actions composing workflows modelling interactions systems environments contexts scenarios situations circumstances conditions cases events occurrences happenings incidents happenstances instances exemplars archetypes paradigms prototypes models standards norms conventions traditions customs practices habits routines protocols procedures methods techniques processes approaches styles fashions modes trends vogue voguish voguishness voguishnessness voguishnessnessness voguishnessessences essences essencesences essencesessesences essencesencesessences…
Conclusion {#conclusion}
———–
Haskell remains popular choice amongst researchers practitioners alike particularly those interested exploring functional programming paradigms further developing new ideas building upon existing foundations laid down before them already providing fertile ground fertile ground continue pushing boundaries frontiers computing science today tomorrow onwards!
**get**
Return type:*str*
Sets *get*.
© Copyright IBM Corporation 2017
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
[`http://www.apache.org/licenses/LICENSE-2.0`](http://www.apache.org/licenses/LICENSE-2.0)
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
—
Transaction Class Reference
===========================
Class Transaction
An object representing an ISAM transaction object handles transactions between ISAM database files
Constructor Summary
——————-
`new(dbenv[, isolation_level])`
`newWithAutoCommit(dbenv[, isolation_level])`
`newWithReadonly(dbenv[, isolation_level])`
`newWithReadOnlyAndAutoCommit(dbenv[, isolation_level])`
Method Summary
————–
`begin()`Begin transaction
`commit()`Commit transaction
<span id="
- Momentum from recent victories ✅ Strong leadership from coaching staff ✅ High morale among players ✅ Effective use of tactical formations 🎰
import numpy as np
import torch.nn.functional as F
from assignment_4.mnist_dataset import MNISTDataset
from sklearn.decomposition import PCA
class MNIST:
def __init__(self):
self.train = MNISTDataset(train=True)
self.test = MNISTDataset(train=False)
self.pca = PCA(n_components=64)
self.pca.fit(self.train.X)
def get_pca_data(self):
X_train_pca = self.pca.transform(self.train.X)
X_test_pca = self.pca.transform(self.test.X)
return X_train_pca.astype(np.float32), X_test_pca.astype(np.float32)
def get_one_hot_labels(self):
y_train_one_hot = np.zeros((len(self.train.y), 10))
y_test_one_hot = np.zeros((len(self.test.y), 10))
for i,y_i in enumerate(self.train.y):
y_train_one_hot[i][y_i] = 1.
for i,y_i in enumerate(self.test.y):
y_test_one_hot[i][y_i] = 1.
return y_train_one_hot.astype(np.float32), y_test_one_hot.astype(np.float32)
def get_minibatches(self,batch_size):
for start_idx in range(0,len(self.train.X)-batch_size+1,batch_size):
mid_idx = min(start_idx+batch_size,len(self.train.X))
yield self.train.X[start_idx:mid_idx],self.train.y[start_idx:mid_idx]
def get_minibatches_(self,batch_size):
for start_idx in range(0,len(self.test.X)-batch_size+1,batch_size):
mid_idx = min(start_idx+batch_size,len(self.test.X))
yield self.test.X[start_idx:mid_idx],self.test.y[start_idx:mid_idx]DimitrisBouras/ML_2020<|file_sepDimitrisBouras/ML_2020<|file_sep# Machine Learning Fall 2020
## Assignment Description:
**Assignment 01:**
In this assignment you will implement your own version of stochastic gradient descent (SGD) using PyTorch.
You will then test your implementation on two different tasks:
* Classification task using logistic regression model.
* Regression task using linear regression model.
**Assignment 02:**
In this assignment you will implement three different neural networks using PyTorch.
You will then test your implementation on two different tasks:
* Classification task using multilayer perceptron (MLP).
* Regression task using convolutional neural network (CNN).
**Assignment 03:**
In this assignment you will implement an autoencoder using PyTorch.
You will then test your implementation on two different datasets:
* Handwritten digits dataset.
* Fashion-MNIST dataset.
**Assignment 04:**
In this assignment you will implement three different neural networks using PyTorch.
You will then test your implementation on two different tasks:
* Classification task using multilayer perceptron (MLP).
* Classification task using convolutional neural network (CNN).DimitrisBouras/ML_2020<|file_sep
import numpy as np
import torch
import torch.nn.functional as F
from assignment_4.mnist_dataset import MNISTDataset
class MLP:
def __init__(self,input_dim,output_dim,num_hidden_units):
self.input_dim = input_dim
self.output_dim = output_dim
self.num_hidden_units = num_hidden_units
def forward_propagation_(self,x):
def train(model,X,y,num_epochs=20000,batch_size=100,alpha=0.01,lambd=0.):
num_examples=X.shape[0]
cost_history=np.zeros(num_epochs)
model.set_mode_train()
for epoch_i in range(num_epochs):
return cost_history
def predict(model,X):
model.set_mode_eval()
return y_pred
def compute_cost(model,X,y,lambd=0.):
return cost
if __name__ == "__main__":
mnist=MNIST()
X_train,y_train=mnist.get_pca_data()
y_train=y_train.reshape(-1)
X_test,y_test=mnist.get_pca_data()
y_test=y_test.reshape(-1)
num_examples=X.shape[0]
input_dim=X.shape[1]
output_dim=len(set(y))
model=MLP(input_dim=input_dim,output_dim=output_dim,num_hidden_units=[256])
cost_history=train(model,X,y,num_epochs=20000,batch_size=100,alpha=0.,lambd=0.)
plt.plot(cost_history)
plt.xlabel("Epoch")
plt.ylabel("$mathcal{L}(X;Y)$")
plt.title("Cost vs Epoch")
plt.show()> Assignment description:
In this assignment you will implement an autoencoder using PyTorch.
You will then test your implementation on two different datasets:
Handwritten digits dataset.
Fashion-MNIST dataset.DimitrisBouras/ML_2020<|file_sep sbt clean compile assembly package <|file_sep * {
margin: 0;
padding: 0;
}
body {
background-color: #f5f5f5;
font-family: 'Lato', sans-serif;
}
header {
width:100%;
height:auto;
background-color:#ffffff;
padding-top:20px;
padding-bottom:20px;
border-bottom-style:solid;
border-bottom-width:thin;
}
#container {
width:auto;
max-width:960px;
margin-left:auto;
margin-right:auto;
}
nav {
float:right;
}
nav ul li {
display:inline-block;
}
nav ul li:hover {
background-color:#eeeeee;
}
a {
text-decoration:none !important;
}
article {
width:auto;
max-width:960px;
min-height:auto;
margin-left:auto;
margin-right:auto;
background-color:#ffffff;
padding-top:20px;
padding-bottom:20px;
}
section hgroup h1 {
color:#333333;
}
section hgroup p {
color:#666666;
}
section img {
max-width:100%;
}
footer {
width:auto;
max-width:960px;
min-height:auto;
margin-left:auto;
margin-right:auto;
background-color:#ffffff;
padding-top:20px;
padding-bottom:20px;
}
footer p {
color:#666666;
}<|file_sep# Machine Learning Fall 2019
## Assignment Description:
**Assignment #01:**
In this assignment you need to implement stochastic gradient descent algorithm from scratch.
Then you need to use it to train a logistic regression classifier.
**Assignment #02:**
In this assignment you need to implement backpropagation algorithm from scratch.
Then you need to use it to train both linear regressor as well as multilayer perceptron classifier.DimitrisBouras/ML_2020> Assignment description:
In this assignment you will implement three different neural networks using PyTorch.
You will then test your implementation on two different tasks:
Classification task using multilayer perceptron (MLP).
Regression task using convolutional neural network (CNN).DimitrisBouras/ML_2020<|file_sep unclear how many hidden layers we want
need hyperparameter search
nonlinear activation functions
different weight initialization schemes
different weight update rules
regularization
dropout
use validation set instead of training set
add early stopping
add learning rate decay
create plots
add progress bar
what if we don't know how many hidden layers we want?
how do we know what activation function(s) should we use?
what weight initialization scheme should we use?
which weight update rule should we use?
how do we regularize our model? what kind of regularization should we use?DimitrisBouras/ML_2020 unsupervised learning
with labels -> supervised learning
classification problem -> categorical variables -> discrete labels -> classification loss functions -> cross entropy loss function -> softmax function -> categorical cross entropy loss function
regression problem -> continuous variables -> continuous labels -> regression loss functions -> mean squared error loss function
linear model + sigmoid activation function + binary cross entropy loss function => logistic regression model => binary classification problem
linear model + softmax activation function + categorical cross entropy loss function => multinomial logistic regression model => multi-class classification problem
linear model + identity activation function + mean squared error loss function => linear regression model => regression problem
layers w/o nonlinearities => linear models
fully connected layer followed by nonlinearities => deep neural network
convolutional layer followed by nonlinearities => convolutional neural network
pooling layer followed by nonlinearities => pooling layer
flatten layer followed by fully connected layer followed by nonlinearities => fully connected layer
convolutional layer followed by pooling layer followed by flatten layer followed by fully connected layer followed by nonlinearities => convolutional neural network
fully connected layers only ?????
convolutional layers only ?????
Show / Hide Solution Code For Exercise #01:
python
import numpy as np
class LinearModel():
def __init__(self,input_dim,output_dim):
self.input_dim=input_dim
self.output_dim=output_dim
python
class LogisticRegression(LinearModel):
python
python
python
python
python
python
python
### Example Usage:
python
python
### Output:
details open=””>
Show / Hide Solution Code For Exercise #02:
python
python
### Example Usage:
python
python
### Output:
details open=””>
Show / Hide Solution Code For Exercise #03:
python
### Example Usage:
python
python
### Output:
details open=””>
Show / Hide Solution Code For Exercise #04:
python
### Example Usage:
python
python
### Output:
details open=””>
Show / Hide Solution Code For Exercise #05:
`r”””
This exercise is not part of our original homework assignments,
but was provided later after I realized that my students were struggling with implementing backpropagation algorithm from scratch,
so I decided it would be useful if they had access to sample code that implements backpropagation algorithm so that they could better understand how it works.
“””`
#### Forward Propagation Algorithm:
##### Linear Layer:
##### Activation Functions:
##### Loss Functions:
##### Backward Propagation Algorithm:
##### Linear Layer:
##### Activation Functions:
##### Loss Functions:
#### Backward Propagation Algorithm Implemented Using Python Classes And Methods:
#### Backward Propagation Algorithm Implemented Using Numpy Operations Only:
#### Example Usage Of Backpropagation Algorithm To Train A Multilayer Perceptron Classifier On The Iris Dataset: details open=””>
Show / Hide Solution Code For Exercise #06:
`r”””
This exercise is not part of our original homework assignments,
but was provided later after I realized that my students were struggling with implementing backpropagation algorithm from scratch,
so I decided it would be useful if they had access to sample code that implements backpropagation algorithm so that they could better understand how it works.
“””`
#### Forward Propagation Algorithm:
##### Linear Layer:
##### Activation Functions:
##### Loss Functions:
#### Backward Propagation Algorithm:
##### Linear Layer:
##### Activation Functions:
##### Loss Functions:
#### Backward Propagation Algorithm Implemented Using Python Classes And Methods: details open=””>
Show / Hide Solution Code For Exercise #07:
`r”””
This exercise is not part of our original homework assignments,
but was provided later after I realized that my students were struggling with implementing backpropagation algorithm from scratch,
so I decided it would be useful if they had access to sample code that implements backpropagation algorithm so that they could better understand how it works.
“””`
#### Forward Propagation Algorithm:
##### Linear Layer:
##### Activation Functions:
##### Loss Functions:
#### Backward Propagation Algorithm:
##### Linear Layer:
##### Activation Functions:
##### Loss Functions:
#### Backward Propagation Algorithm Implemented Using Numpy Operations Only: details open=””>
Show / Hide Solution Code For Exercise #08:
`r”””
This exercise is not part of our original homework assignments,
but was provided later after I realized that my students were struggling with implementing backpropagation algorithm from scratch,
so I decided it would be useful if they had access to sample code that implements backpropagation algorithm so that they could better understand how it works.
“””`
#### Forward Propagation Algorithm:
###### Dense Layers Without Nonlinear Activations Are Just Linear Models.
###### Fully Connected Layers Followed By Nonlinear Activations Are Deep Neural Networks.
###### Convolutional Layers Followed By Nonlinear Activations Are Convolutional Neural Networks.
###### Pooling Layers Followed By Nonlinear Activations Are Pooling Layers.
###### Flatten Layers Followed By Fully Connected Layers Followed By Nonlinear Activations Are Fully Connected Layers.
###### Convolutional Layers Followed By Pooling Layers Followed By Flatten Layers Followed By Fully Connected Layers Followed By Nonlinear Activations Are Convolutional Neural Networks.
###### Dense Layers Without Nonlinear Activations Are Just Linear Models.
###### Fully Connected Layers Followed By Nonlinear Activations Are Deep Neural Networks.
###### Convolutional Layers Followed By Nonlinear Activations Are Convolutional Neural Networks.
###### Pooling Layers Followed By Nonlinear Activations Are Pooling Layers.
###### Flatten Layers Followed By Fully Connected Layers Followed By Nonlinear Activations Are Fully Connected Layers.
###### Convolutional Layers Followed By Pooling Layer(s) Followed By Flatten Layer(s) Followed By Fully Connected Layer(s) FollowedByNonLinearActivationsAreConvolutionNeuralNetworks.
## Sigmoid Function And Binary Cross Entropy Loss Function Used Together Define Logistic Regression Model Which Is A Binary Classifier.
## Softmax Function And Categorical Cross Entropy Loss Function Used Together Define Multinomial Logistic Regression Model Which Is A Multi-Class Classifier.
## Identity Function And Mean Squared Error Loss Function Used Together Define Linear Regression Model Which Is A Regressor.
## How Do We Decide What Kind Of Model We Should Use?
## How Do We Decide How Many Hidden Units Our Network Should Have?
## How Do We Decide How Many Hidden Units Each Hidden Layer Should Have?
## How Do We Decide What Kind Of Activation Function Each Hidden Unit Should Use?
## How Do We Decide Which Weight Initialization Scheme We Should Use?
## How Do We Decide Which Weight Update Rule We Should Use?
## Why Do We Need To Regularize Our Model?
## What Kind Of Regularization Technique Should We Use?
## Why Do We Need Dropout?
# References:
[Goodfellow et al., Chapter Sixteen](http://www.deeplearningbook.org)
[Goodfellow et al., Chapter Fifteen](http://www.deeplearningbook.org)
[Goodfellow et al., Chapter Fourteen](http://www.deeplearningbook.org)
[Goodfellow et al., Chapter Thirteen](http://www.deeplearningbook.org)
[Goodfellow et al., Chapter Twelve](http://www.deeplearningbook.org)
[Goodfellow et al., Chapter Eleven](http://www.deeplearningbook.org)
[Goodfellow et al., Chapter Ten](http://www.deeplearningbook.org)
[A Gentle Introduction To Artificial Neural Networks Part One — From Perceptrons To Multilayer Perceptrons](https://medium.com/datadriveninvestor/a-gentle-introduction-to-artificial-neural-networks-part-one-from-perceptrons-to-multilayer-perceptrons-9a7c8b70c6b6)
[A Gentle Introduction To Artificial Neural Networks Part Two — From Multilayer Perceptrons To Deep Learning Frameworks](https://medium.com/datadriveninvestor/a-gentle-introduction-to-artificial-neural-networks-part-two-from-multilayer-perceptrons-to-deep-learning-frameworks-fd470646cbe8)
[Solved Assignments From Andrew Ng’s Coursera Course On Machine Learning Specialization At Stanford University — Part One — Week One Exercises Solutions In Python Implementations With Numpy Only And No Libraries Or Frameworks Involved!](https://medium.com/@dmitri.bourakivskyi/solved-assignments-from-andrew-ng-s-coursera-course-on-machine-learning-specialization-at-stanford-university-part-one-week-one-exercises-solutions-in-python-implementati-b7bf12de35b68)
[Solved Assignments From Andrew Ng’s Coursera Course On Machine Learning Specialization At Stanford University — Part Two — Week Two Exercises Solutions In Python Implementations With Numpy Only And No Libraries Or Frameworks Involved!](https://medium.com/@dmitri.bourakivskyi/solved-assignments-from-andrew-ng-s-coursera-course-on-machine-learning-specialization-at-stanford-university-part-two-week-two-exercises-solutions-in-python-implementati-d59b76bc7eb7)
[Solved Assignments From Andrew Ng’s Coursera Course On Machine Learning Specialization At Stanford University — Part Three — Week Three Exercises Solutions In Python Implementations With Numpy Only And No Libraries Or Frameworks Involved!](https://medium.com/@dmitri.bourakivskyi/solved-assignments-from-andrew-ng-s-coursera-course-on-machine-learning-specialization-at-stanford-university-part-three-week-three-exercises-solutions-in-pytho-44c22cd28c9c)
[Solved Assignments From Andrew Ng’s Coursera Course On Machine Learning Specialization At Stanford University — Part Four — Week Four Exercises Solutions In Python Implementations With Numpy Only And No Libraries Or Frameworks Involved!](https://medium.com/@dmitri.bourakivskyi/solved-assignments-from-andrew-ng-s-coursera-course-on-machine-learning-specialization-at-stanford-university-part-four-week-four-exercises-solutions-in-pytho-e16595db6b47)
[Solved Assignments From Andrew Ng’s Coursera Course On Machine Learning Specialization At Stanford University — Part Five — Week Five Exercises Solutions In Python Implementations With Numpy Only And No Libraries Or Frameworks Involved!](https://medium.com/@dmitri.bourakivskyi/solved-assignments-from-andrew-ng-s-courservia-course-on-machine-learning-specialization-at-stanford-university-part-five-week-five-exercises-solutions-in-python-implementati-a09cbba23fe9)
[Solving The XOR Problem Using A Single Neuron Without Any Hidden Units Is Impossible Because It Cannot Solve Problems That Aren’t Linearly Separable But Solving It Using A Single Neuron With One Hidden Unit Is Possible Because Now Our Model Can Solve Problems That Aren’t Linearly Separable As Well! This Video Will Show You Exactly How It Works Step-by-step In Detail So That You Can Understand It Thoroughly! https://youtu.be/Xz8QmUZ5KwM ](https:/youtu.be/Xz8QmUZ5KwM)
[Solving The XOR Problem Using A Single Neuron Without Any Hidden Units Is Impossible Because It Cannot Solve Problems That Aren’t Linearly Separable But Solving It Using A Single Neuron With One Hidden Unit Is Possible Because Now Our Model Can Solve Problems That Aren’t Linearly Separable As Well! This Video Will Show You Exactly How It Works Step-by-step In Detail So That You Can Understand It Thoroughly! https://youtu.be/Xz8QmUZ5KwM ](https:/youtu.be/Xz8QmUZ5KwM)
[Solving The XOR Problem Using A Single Neuron Without Any Hidden Units Is Impossible Because It Cannot Solve Problems That Aren’t Linearly Separable But Solving It Using A Single Neuron With One Hidden Unit Is Possible Because Now Our Model Can Solve Problems That Aren’t Linearly Separable As Well! This Video Will Show You Exactly How It Works Step-by-step In Detail So That You Can Understand It Thoroughly! https://youtu.be/Xz8QmUZ5KwM ](https:/youtu.be/Xz8QmUZ5KwM)
[Making Sense Of Recurrent Neural Networks – Understanding LSTM Inside Out – Step-by-step Explanation – https://youtu.be/gGkR6EJGJWs ](https:/youtu.be/gGkR6EJGJWs)
[Making Sense Of Recurrent Neural Networks – Understanding LSTM Inside Out – Step-by-step Explanation – https://youtu.be/gGkR6EJGJWs ](https:/youtu.be/gGkR6EJGJWs)
[Making Sense Of Recurrent Neural Networks – Understanding LSTM Inside Out – Step-by-step Explanation – https://youtu.be/gGkR6EJGJWs ](https:/youtu.be/gGkR6EJGJWs) /assignment_03/exercise.py | file_sep=’rn’ > import numpy as np
class AutoEncoder():
if __name__ == “__main__”:
pass
<|file_sep|introduction.mdwn — |
title:A Brief Introduction into Haskell Programming Language |
author:Dennis O'Sullivan |
date:today{} |
A Brief Introduction into Haskell Programming Language {#sec:introduction}
=====================================================
Introduction {#sec:intro}
————-
Haskell is one example out there amongst many functional programming languages which has evolved over time since its conception around twenty years ago cite{wiki:haskell}. Haskell borrows many concepts from other languages including Lisp cite{wiki:lisp}, ML cite{wiki:sml} ,and Miranda cite{wiki:miranda}. Although originally developed independently Haskell has now gained popularity through its inclusion into Glasgow Haskell Compiler cite{wiki:gch} which is used widely throughout industry cite{wiki:haskell-industry}.
The language itself uses lazy evaluation which means all expressions are evaluated when required rather than immediately when first encountered during compilation cite{wiki:haskell-lazy}. This feature allows developers greater flexibility when writing programs but comes at a price because although values may be calculated only once per program run these values must still be stored somewhere until no longer required at which point memory can be freed up again cite{wiki:haskell-lazy}. However thanks largely due again due largely due Glasgow Haskell Compiler’s efficient garbage collection mechanisms much more memory can be reclaimed than would otherwise be possible leading ultimately towards faster execution times compared those obtained via eager evaluation strategies employed elsewhere e.g OCaml etc.. Another benefit associated here too arises because lazy evaluation enables easier reasoning about programs given expressions remain unevaluated until explicitly requested allowing programmers focus solely upon desired outputs rather than worrying about side effects caused within intermediate calculations themselves thus making debugging simpler too!
Haskell also provides extensive support through type inference enabling programmers write concise code without having specify explicit types everywhere making programs easier read maintain modify further down line especially useful when dealing complex data structures e.g lists trees graphs etc.. Furthermore thanks again Glasgow Haskell Compiler’s sophisticated compiler infrastructure including optimisations performed automatically behind scenes e.g constant folding dead code elimination tail call optimisation etc.. leads towards highly efficient compiled binaries producing fast executing applications able run smoothly even under heavy loads!
Finally worth noting final advantage comes via powerful abstraction mechanisms available within language itself such modularity achieved via modules allowing programmers group related functionality together whilst keeping unrelated pieces separate thus avoiding clutter across large projects where organisation becomes increasingly important factor especially relevant today given growing complexity found modern software development practices such agile methodologies DevOps CI CD pipelines etc..
All things considered above therefore suggests clearly why Haskell remains popular choice amongst researchers practitioners alike particularly those interested exploring functional programming paradigms further developing new ideas building upon existing foundations laid down before them already providing fertile ground fertile ground continue pushing boundaries frontiers computing science today tomorrow onwards!
Basic Syntax {#sec:syntax}
————-
Haskell syntax closely resembles mathematical notation making programs easy read understand follow along even beginners familiarise themselves quickly basic concepts involved language usage itself moreover thanks comprehensive documentation tutorials available online helping newcomers learn effectively efficiently build practical experience working hands actual projects soon enough!
Functions {#sec:function}
———-
Functions represent core building blocks within Haskell programming language defined simply pair inputs outputs where each input mapped uniquely corresponding output value returned upon invocation according specified behaviour expressed through body containing expressions computations performed derive result finally returned caller invoking same initially defined manner previously discussed earlier section introduction overview general concepts underlying fundamentals principles driving design philosophy behind entire ecosystem surrounding toolchains technologies encompassing wide array diverse components ranging compilers interpreters libraries frameworks tools utilities various kinds supporting development activities carried out daily basis across multitude domains industries sectors worldwide today tomorrow onwards!
Data Types {#sec:data-type}
———-
Haskell provides rich collection built-in data types including primitive ones integers floating point numbers characters strings booleans lists tuples arrays vectors sets maps dictionaries hash tables bit vectors bitsets bit arrays bitmaps bit strings bit fields bits flags masks boolean matrices tensors vectors spaces coordinates points angles distances sizes shapes dimensions lengths widths heights depths scales factors ratios proportions percentages rates speeds velocities accelerations forces energies powers temperatures pressures densities masses volumes areas perimeters circumferences diameters radii circumferences arcs sectors segments chords secants tangents normals bisectors medians altitudes heights bases legs hypotenuses diagonals diagonals conjugates complements supplements orthocenters centroids incenters excenters circumcenters nine-point centers Fermat points Gergonne points isotomic conjugates isotomic points isotomic conjugates isotomic conjugates isotomic conjugates isotomic conjugates isotomic conjugates isotomic conjugates isotomic conjugates etcetera ad infinitum!
Modules {#sec,module}
——–
Modules provide mechanism grouping related functionality together keeping unrelated pieces separate avoiding clutter large projects organisation becoming increasingly important factor especially relevant today given growing complexity found modern software development practices such agile methodologies DevOps CI CD pipelines etcetera ad infinitum!
Type Classes {#sec:type-class}
————-
Type classes provide mechanism defining generic interfaces specifying behaviours expected types conforming same must adhere obey following similar patterns structure adherences enforced compile-time ensuring correctness reliability robustness dependability security trustworthiness safety stability consistency predictability verifiability provability soundness completeness decidability computability solvability tractability simplicity elegance beauty harmony unity perfection totality absoluteness omnipresence omnipotence omniscience omnipresence omniscience omnipotence omnipresence omniscience omnipotence!
Pattern Matching {#sec:patt-match}
——————
Pattern matching provides mechanism deconstructing data structures extracting individual components elements parts pieces fragments segments slices chunks chunkslets slivers splinters splinters splinters splinters splinters splinters splinters splinters splinters splinters splinters splinters chunkslets slivers fragments parts pieces elements components structures data extracting deconstructing mechanism pattern matching provides!
Recursion {#sec:rccur}
———-
Recursion provides mechanism defining functions calling themselves repeatedly performing computations iteratively until base case reached terminating recursion cycle returning final result computed derived accumulated aggregated collected gathered assembled constructed built created synthesized generated produced formulated devised invented contrived crafted designed engineered fabricated manufactured assembled constructed built created synthesized generated produced formulated devised invented contrived crafted designed engineered fabricated manufactured assembled constructed built created synthesized generated produced formulated devised invented contrived crafted designed engineered fabricated manufactured assembled constructed built created synthesized generated produced formulated devised invented contrived crafted designed engineered fabricated manufactured assembled constructed built created synthesized generated produced formulated devised invented contrived crafted designed engineered fabricated manufactured assembled constructed built created synthesized generated produced formulated devised invented contrived crafted designed engineered fabricated manufactured assembled constructed built created synthesized generated produced formulated devised invented contrived crafted designed engineered fabricated manufactured assembled constructed built!
Lazy Evaluation {#sec:lzy-eval}
—————
Lazy evaluation provides mechanism delaying computation until required evaluating expressions only once necessary reducing unnecessary work saving resources improving performance efficiency effectiveness productivity throughput scalability extensibility maintainability portability reusability interoperability compatibility interoperabili…
Higher-order functions {#sec:hofunc}
———————-
Higher-order functions provide mechanism passing functions arguments receiving functions return values allowing composition abstraction encapsulation decoupling separation concerns modularisation decomposition refinement specification refinement refinement refinement refinement refinement refinement refinement refinement refinement refinement…
Monads {#sec:mnd}
——-
Monads provide mechanism encapsulating side-effects managing stateful computations sequencing operations chaining actions composing workflows modelling interactions systems environments contexts scenarios situations circumstances conditions cases events occurrences happenings incidents happenstances instances exemplars archetypes paradigms prototypes models standards norms conventions traditions customs practices habits routines protocols procedures methods techniques processes approaches styles fashions modes trends vogue voguish voguishness voguishnessness voguishnessnessness voguishnessessences essences essencesences essencesessesences essencesencesessences…
Conclusion {#conclusion}
———–
Haskell remains popular choice amongst researchers practitioners alike particularly those interested exploring functional programming paradigms further developing new ideas building upon existing foundations laid down before them already providing fertile ground fertile ground continue pushing boundaries frontiers computing science today tomorrow onwards!
**get**
Return type:*str*
Sets *get*.
© Copyright IBM Corporation 2017
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
[`http://www.apache.org/licenses/LICENSE-2.0`](http://www.apache.org/licenses/LICENSE-2.0)
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
—
Transaction Class Reference
===========================
Class Transaction
An object representing an ISAM transaction object handles transactions between ISAM database files
Constructor Summary
——————-
`new(dbenv[, isolation_level])`
`newWithAutoCommit(dbenv[, isolation_level])`
`newWithReadonly(dbenv[, isolation_level])`
`newWithReadOnlyAndAutoCommit(dbenv[, isolation_level])`
Method Summary
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`begin()`Begin transaction
`commit()`Commit transaction
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