Quick Link to Resumé

• 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