shimon23/Deep-Learning-in-Hebrew

ספר מלא בעברית בנושאים של למידת מכונה ולמידה עמוקה

Deep-Learning-in-Hebrew

למידת מכונה ולמידה עמוקה בעברית

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Authors:

Chapter authors:

David Ben Attar

Nava (Reinitz) Leibovich

contributors:

Table of contents

Part I:

1. Introducion to Machine Learning
2. Machine Learning Algorithms

Part II:

3. Linear Neural Networks (Regression problems)
4. Deep Neural Networks
5. Convolutional Neural Networks
6. Recurrent Neural Networks
7. Deep Generative Models
8. Attention Mechanism

Part III:

9. Computer Vision
10. Natural Language Process
11. Reinforcement Learning
References

1. Introducion to Machine Learning

1.1 What is Machine Learning?

1.1.1 The Basic Concept
1.1.2 Data, Tasks and Learning

1.2 Applied Math

1.2.1 Linear Algebra
1.2.2 Calculus
1.2.3 Probability

2. Machine Learning Algorithms

2.1 Supervised Learning Algorithms

2.1.1 Support Vector Machines (SVM)
2.1.2 Naïve Bayes
2.1.3 K-nearest neighbors (K-NN)
2.1.4 Qadratic\Linear Discriminant Analysis (QDA\LDA)
2.1.5 Decision Trees

2.2 Unsupervised Learning Algorithms

2.3 Dimensionally Reduction

2.3.1 Principal Components Analysis (PCA)
2.3.2 t-distributed Stochastic Neighbor Embedding (t-SNE)
2.3.3 Uniform Manifold Approximation and Projection (UMAP)

2.4 Ensemble Learning

2.4.1 Introduction to Ensemble Learning
2.4.2 Bagging
2.4.3 Boosting

3. Linear Neural Networks (Regression problems)

3.1 Linear Regression

3.1.1 The Basic Concept
3.1.2 Gradient Descent
3.1.3 Regularization and Cross Validation
3.1.4 Linear Regression as Classifier

3.2 Softmax Regression

3.2.1 Logistic Regression
3.2.2 Cross Entropy and Gradient descent
3.2.3 Optimization
3.2.4 SoftMax Regression – Multi Class Logistic Regression
3.2.5 SoftMax Regression as Neural Network

4. Deep Neural Networks

4.1 MLP – Multilayer Perceptrons

4.1.1 From a Single Neuron to Deep Neural Network
4.1.2 Activation Function
4.1.3 Xor

4.2 Computational Graphs and propagation

4.2.1 Computational Graphs
4.2.2 Forward and Backward propagation

4.3 Optimization

4.3.1 Data Normalization
4.3.2 Weight Initialization
4.3.3 Batch Normalization
4.3.4 Mini Batch
4.3.5 Gradient Descent Optimization Algorithms

4.4 Generalization

4.4.1 Regularization
4.4.2 Weight Decay
4.4.3 Model Ensembles and Drop Out
4.4.4 Data Augmentation

5. Convolutional Neural Networks

5.1 Convolutional Layers

5.1.1 From Fully-Connected Layers to Convolutions
5.1.2 Padding, Stride and Dilation
5.1.3 Pooling
5.1.4 Training
5.1.5 Convolutional Neural Networks (LeNet)

5.2 CNN Architectures

6. Recurrent Neural Networks

6.1 Sequence Models

6.1.1 Recurrent Neural Networks
6.1.2 Learning Parameters

6.2 RNN Architectures

6.2.1 Long Short-Term Memory (LSTM)
6.2.2 Gated Recurrent Units (GRU)
6.2.3 Deep RNN
6.2.4 Bidirectional RNN
6.2.5 Sequence to Sequence Learning

7. Deep Generative Models

7.1 Variational AutoEncoder (VAE)

7.1.1 Dimensionality Reduction
7.1.2 Autoencoders (AE)
7.1.3 Variational AutoEncoders (VAE)

7.2 Generative Adversarial Networks (GANs)

7.3 Auto-Regressive Generative Models

7.3.1 PixelRNN
7.3.2 PixelCNN
7.3.3 Gated PixelCNN
7.3.4 PixelCNN++

8. Attention Mechanism

8.1 Sequence to Sequence Learning and Attention

8.1.1 Attention in Seq2Seq Models
8.1.2 Bahdanau Attention and Luong Attention

8.2 Transformer

8.2.1 Positional Encoding
8.2.2 Self-Attention Layer
8.2.3 Multi Head Attention
8.2.4 Transformer End to End
8.2.5 Transformer Applications

9. Computer Vision

9.1 Object Detection

9.1.1 R-CNN
9.1.2 You Only Look Once (YOLO)
9.1.3 Single Shot Detector (SSD)
9.1.4 Spatial Pyramid Pooling (SPP-net)
9.1.5 Feature Pyramid Networks
9.1.6 Deformable Convolutional Networks
9.1.7 DE:TR: Object Detection with Transformers

9.2 Segmentation

9.2.1 Semantic Segmentation vs. Instance Segmentation
9.2.2 SegNet neural network
9.2.3 Atrous convolutions
9.2.4 Atrous Spatial Pyramidal Pooling
9.2.5 Conditional Random Fields usage for improving final output
9.2.6 See More Than Once -- Kernel-Sharing Atrous Convolution

9.3 Face Recognition and Pose Estimation

9.3.1 Face Recognition
9.3.2 Pose Estimation

9.5 Few-Shot Learning

10. Natural Language Process

10.1 Language Model

10.1.1 N-gram
10.1.2 Word Representation (Vectors)
10.1.3 Word2Vec/GloVe
10.1.4 ELMo - Embeddings from Language Model
10.1.5 Attention/Transformer (GPT)

10.2 Neural Machine Translation

10.2.1 Neural Machine Translation by Jointly Learning to Align and Translate
10.2.2 Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
10.2.3 ConvS2S
10.2.4 RNMT+
10.2.5 Transformer and Transformer based models
10.2.6 Named Entity Recognition (NER)
10.2.7 Bilingual Evaluation Understudy (BLEU score)
10.2.8 Unsupervised Machine Translation

10.3 Speech Recognition

10.3.1 Connectionist Temporal Classification
10.3.2 Listen, Attend, and Spell
10.3.3 Very Deep Convolutional Networks for End-to-End Speech Recognition

10.4 Document Summarization

Extractive Text Summarization:

Abstractive Text Summarization:

10.4.6 T5 Transformers
10.4.7 BART Transformers
10.4.8 GPT-2 Transformers
10.4.9 XLM Transformers

11. Reinforcement Learning

11.1 Introduction to RL

11.1.1 Markov Decision Process (MDP) and RL
11.1.2 Planning
11.1.3 Learning Algorithms

11.2 Exploration and Exploitation

11.3 Planning by Dynamic Programming

11.4 Policy Gradient Methods

11.5 Monte-Carlo

11.6 Temporal-Difference Learning

11.7 Model-based algorithms

References

Machine Learning - Andrew Ng

Dive into Deep Learning

Deep Learning Book

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