Ahmad Badary

Resources:

Interesting Links:

Quick Links:

Probability Theory
Statistics
Information Theory
Essence of Linear Algebra
Linear Algebra: Basics and Definitions
Linear Algebra Resources (dir)
Calculus
Matrix Calculus (DL-book)
Topology and Smooth Manifolds
Machine Learning Research
Cheatsheets
Core
- Concepts
- Machine Learning Research
Architectures
- Feed-Forward Neural Network (FNN)
- Multi-Layer Perceptron (MLP)
- Deep FeedForward Neural Network
- Convolutional Neural Network (CNN)
- Recurrent Neural Network (RNN)
- Long-Short-Term Memory Network (LSTM)
- Gated-Recurrent Units Network (GRU)
- Probabilistic Graphical Models (PGMs)
- Deep Generative Models (DGMs)
- Hopfield Networks
- Boltzmann Machines (BMs)
- Restricted Boltzmann Machines (RBMs) [Coming Soon]
- Deep Belief Networks (DBNs) [Coming Soon]
- (Vanilla) Auto-Encoders
- (Variational) Auto-Encoders (VAEs)
- Generative Adversarial Networks (GANs)
- Neural Networks (Architectures+)
- Latent Variable Models
Concepts
- Activation Functions
- Data Processing
- Gradient-Based Optimization
- Loss Functions
- Normalization Methods in Deep Learning
- Inference and Approximate Inference
- The Partition Function
- Sampling and Monte Carlo Methods
Theory
- Elements of Machine Learning - DLBook Ch.5
- Basics of Deep Learning - DLBook Ch.6
- Regularization - DLBook Ch.7
- ML (Statistical) Models
- Statistical Learning Theory
- The Learning Problem (Caltech)
- Information Theory
- Probability Theory
- Statistics
- Manifold Learning
- Computational Learning Theory (TBD)
- Stochastic Processes
- Learning for Machines (Concepts)
- The Theory of Learning
- Interpretable Machine Learning (Models)
- Representation Learning
- Learning Summary
- Model Compression
- Uncertainty in Neural Networks
- Estimation
Practical
- Practical Concepts in Machine Learning
- CheatSheets
- Practical NLP
Machine Learning (ML)
- Clustering
- Decision Trees
- Ensemble Learning - Aggregating
- (Statistical) (parameter) Estimation
- K-Means
- K-Nearest Neighbor (KNN)
- The Naive Bayes Classifier
- Principal Component Analysis (PCA)
- Regression
- The Centroid Method
- The Perceptron
- Maximum Margin Classifiers
- (Hard-Margin) Support Vector Machines (SVM)
- Support Vector Machines (SVM)
Computer Vision (CV)
- Convolutional Neural Networks (CNNs)
- Image Classification and Deep Learning
- Recurrent Neural Networks | Applications in Computer Vision
- Image Segmentation with Deep Learning
- Image Classification and Localization with Deep Learning
- Object Detection with Deep Learning
- Deep Auto-Encoders
- Generative Models | Unsupervised Learning
- Articulated Body Pose Estimation
- Generative Compression
- CNN Architectures
- RNNs | Applications in Computer Vision
- Generative Adversarial Networks (GANs)
- Deep-Dream | Visualizing Features in ConvNets
Natural Language Processing (NLP)
- Introduction to NLP with DL
- Word Embeddings | Word2Vec
- Language Modeling | Recurrent Neural Networks (RNNs)
- Neural Machine Translation | Advanced Recurrent Networks
- Gated Units | RNN Architectures
- Attention Mechanism for DNNs
- Speech Processing | Automatic Speech Recognition
- Sentence Embeddings and Contextualized Embeddings
- Contextual Word Representations and Pretraining (Transformers)
- Text Classification
- CNNs in NLP
- TensorFlow in NLP
- TensorFlow in NLP #2
- Research Papers
- ASR Research Papers
- List of Papers
Classical NLP
- Introduction to NLP
- Text Processing
- Discriminative Models in NLP | MaxEnt Models and Discriminative Estimation
- Text Classification
- Sentiment Analysis
- Information Extraction
Math
- Statistics
Misc
- Reinforcement Learning (RL)
Extra

Will be updated soon!

-->

OLD INDEX - BEFORE FIXING WEBSITE

Resources:

Interesting Links:

Quick Links:

Will be updated soon!