| title | category |
|---|---|
Machine Learning |
Libraries |
- A Course in Machine Learning - Hal Daumé III (PDF)
- A First Encounter with Machine Learning - Max Welling (PDF)
- AI Algorithms, Data Structures, and Idioms in Prolog, Lisp, and Java - George F. Luger, William A Stubblefield (PDF)
- An Introduction to Statistical Learning - Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
- Bayesian Reasoning and Machine Learning - David Barber
- Computer Vision: Algorithms and Applications - Richard Szeliski
- Gaussian Processes for Machine Learning - Carl Edward Rasmussen, Christopher K. I. Williams
- Information Theory, Inference, and Learning Algorithms - David MacKay
- Introduction to Machine Learning - Alex Smola and S.V.N. Vishwanathan (PDF)
- Learning Deep Architectures for AI - Yoshua Bengio (PDF)
- Machine Learning - Abdelhamid Mellouk, Abdennacer Chebira
- Machine Learning, Neural and Statistical Classification - D. Michie, D.J. Spiegelhalter, C.C. Taylor
- Natural Language Processing with Python - Edward Loper, Ewan Klein, and Steven Bird (PDF)
- Neural Networks and Deep Learning
- Probabilistic Models in the Study of Language (Draft, with R code)
- Programming Computer Vision with Python - Jan Erik Solem
- The LION Way: Machine Learning plus Intelligent Optimization - Roberto Battiti, Mauro Brunato
- The Python Game Book
- Kaggle: The Home of Data Science
- UCI Machine Learning Repository
- Welcome to Deep Learning
- Excellent Answer by Franck-Dernoncourt
- Machine Learning Demos
- AI WEBSITES THAT DESIGN THEMSELVES
- A Visual Introduction to Machine Learning
- Machine Learning in Games
- Data Mining, Analytics, Big Data, and Data Science
- Reinforcement Learning
- Deep Beat(Lyrics Generating AI)
- UCI KDD Archive
- DELVE datasets
- AWS Public datasets
- Pew Research Center
- Image Databases
- Deep Learning Datasets
- Qwiklab. NVIDIA courses
- Scikit-learn:
- PyBrain
- Natural Language Toolkit(nltk)
- Theano
- Caffe(Deep learning framework by the BVLC)
- Pylearn2
- MDP (Modular toolkit for Data Processing):
- TensorFlow™
- Spark
- Milk
- OpenCV(object detection stuff)
- Machine Learning Python
- LIBSVM -- A Library for Support Vector Machines
- Keras
- Lassage (Built over Theano)
- PyTorch
- Turicreate (by Apple)
- Kernel Density Estimation and Non-parametric Bayes Classifier
- K-Means
- Affinity Propagation
- Hierarchical clustering/Agglomerative clustering
- Kernel Principal Components Analysis
- Linear Regression
- Logistic Regression
- Neighbors (Nearest, Farthest, Range, k, Classification)
- Non-Negative Matrix Factorization
- Support Vector Machines
- Dimensionality Reduction
- Fast Singular Value Decomposition
- Decision Tree
- Random Forest
- Bootstrapped SVM
- DBSCAN
- Principal Component Analysis(PCA)
- Linear Discriminant Analysis (LDA)
- Autoencoders
- Bagging and boosting
- Titanic Dataset
- Iris Dataset
- Wine Quality Dataset
- House Prices Dataset
- Car Evaluation Data Set
- MNIST Handwritten Digits Dataset
- Game Of Thrones Dataset
- IMDB 5000 Image Movie Dataset
- Enron Dataset for ham/spam classification
- 20 Newsgroups Dataset
- Kodak Image Dataset
- Image Net Dataset
Will be updated soon!