/Deep-Learning-in-Hebrew

ספר מלא בעברית בנושאים של למידת מכונה ולמידה עמוקה

Deep-Learning-in-Hebrew

למידת מכונה ולמידה עמוקה בעברית

Add a star if the repository helped you :)

MDLH

For any issue please contact us at Avrahamsapir1@gmail.com.

People

Authors:

Avraham Raviv

Mike Erlihson

Chapter authors:

David Ben Attar

Jeremy Rutman

Maya Rapaport

Nava (Reinitz) Leibovich

Or Avrahami

Or Shemesh

Ron Levy

Uri Almog

contributors:

Avi Caciularu

Avshalom Dayan

Table of contents

Part I:

Part II:

Part III:



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

  • 2.2.1 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 – 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

  • 5.2.1 AlexNet

  • 5.2.2 VGG

  • 5.2.3 GoogleNet

  • 5.2.4 Residual Networks (ResNet)

  • 5.2.5 Densely Connected Networks (DenseNet)

  • 5.2.6 U-Net

  • 5.2.7 Transfer Learning

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)

  • 7.2.1 Generator and Discriminator

  • 7.2.2 DCGAN

  • 7.2.3 Conditional GAN (cGAN)

  • 7.2.4 Pix2Pix

  • 7.2.5 CycleGAN

  • 7.2.6 Progressively Growing (ProGAN)

  • 7.2.7 StyleGAN

  • 7.2.8 Wasserstein GAN

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

  • 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 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:

  • 10.4.1 TextRank

  • 10.4.2 LexRank

  • 10.4.3 Luhn

  • 10.4.4 Latent Semantic Analysis, LSA

  • 10.4.5 KL-Sum

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

Stanford cs231

Machine Learning - Andrew Ng

Dive into Deep Learning

Deep Learning Book

כל הזכויות שמורות @