Deep-Learning-in-Hebrew

למידת מכונה ולמידה עמוקה בעברית

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If you find this book useful in your research work, please consider citing:

@InProceedings{MDLH,
author = {Raviv, Avraham and Erlihson, Mike},
booktitle = {Machine and Deep learning in Hebrew},
year = {2021}
}

Table of Content

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. Quadratic\Linear Discriminant Analysis (QDA\LDA)
2.1.5. Decision Trees

2.2. [Unsupervised Learning Algorithms]

2.2.1. K-means
2.2.2. Mixture Models
2.2.3. Expectation–maximization (EM)
2.2.4. Hierarchical Clustering
2.2.5. Local Outlier Factor

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 – Multiclass 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
4.2.3. Back Propagation and Stochastic Gradient Descent

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. Introduction to Object Detection
9.1.2. R-CNN
9.1.3. You Only Look Once (YOLO)
9.1.4. Single Shot Detector (SSD)
9.1.5 Spatial Pyramid Pooling (SPP-net)
9.1.6. Feature Pyramid Networks
9.1.7. Deformable Convolutional Networks
9.1.8. 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

9.5.1. The Problem
9.5.2 Metric Learning
9.5.3. Meta-Learning (Learning-to-Learn)
9.5.4. Data Augmentation
9.5.5. Zero-Shot Learning

10. Natural Language Process

10.1. Language Models and Word Representation

10.1.1. Basic Language Models
10.1.2. Word Representation (Vectors) and Word Embeddings
10.1.3. COntextual Embeddings

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. Model Free Prediction

11.2.1. Monte-Carlo (MC) Policy Evaluation
11.2.2. Temporal Difference (TD) – Bootstrapping
11.2.3. TD(λ)

11.3. Model Free Control

11.4. Model Based Control

11.4.1. Known Model – Dyna algorithm
11.4.2. Known Model – Tree Search
11.4.3. Planning for Continuous Action Space

11.5. Exploration and Exploitation

11.5.1. N-armed bandits
11.5.2. Full MDP

11.6. Learning From an Expert

11.6.1. Imitation Learning
11.6.2. Inverse RL

11.7. Partially Observed Markov Decision Process (POMDP)

12. Graph Neural Networks

12.1. Introduction to Graphs

12.1.1. Represent Data as a Graph
12.1.2. Tasks on Graphs
12.1.3. The challenge of learning graphs

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