Machine Learning
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- curators:
Apps
Articles
Audio, Podcasts
- The Talking Machines
- Data Skeptic
- Linear Digressions
- Machine learning guide
- Not So Standard Deviations
- Practical AI
- Talking machines - Human conversation about machine learning.
- This Week in Machine Learning
Blogs
- KDnuggets
- MLWave
- Aditya Prakash
- Andrej Karpathy
- Andrew Gambarella
- Apple ML Journal
- Arg min
- Bradford Cross
- Brief History of Machine Learning
- Christopher Olah
- Clarifai
- Concept Search on Wikipedia
- Deep Dojo
- DeepMath
- DeepMind
- Distill
- Facebook Research
- FastML
- Google Research
- I Am Trask
- Igor Babuschkin
- InFERENCe
- Inverse Probability
- I’m a Bandit
- Kaggle Blog
- Lilian Weng
- Machine Learning Theory
- Machine Think
- Max Welling
- Off the Convex Path
- OpenAI
- Peter Goldsborough
- Ryan Dahl
- Sander Dieleman
- Silicon Valley AI Lab
- Somatic
- Sourabh Bajaj
- Statistical Modelling, Causal Inference and Social Science
- Stephan Hoyer
- TensorFlow
- The Neural Perspective
- The Spectator
- Tim Head
- Triangle Inequality
- Unintentionally Inconsiderate
- WildML
Books
- Pattern Recognition and Machine Learning by Christopher Bishop
- The Elements of Statistical Learning
📕 Paradigms of artificial intelligence programming (1991)📕 Artificial intelligence a modern approach (1994)📕 Machine learning (1997)📖 The quest for artificial intelligence - a history of ideas and achievements (2009)📕 Introduction to artificial intelligence (2011)📕 Machine learning: a probabilistic perspective (2012)📖 The Nature of Code (2012)📕 Superintelligence: paths, dangers, strategies (2014)📖 Understanding machine learning: from theory to algorithms (2014)📖 Neural Networks and Deep Learning (2015)📕 Deep Larning with Python (2017)📕 Tensorflow machine learning cookbook (2017)📕 Hands-On Machine Learning with Scikit-Learn and TensorFlow (2017)📕 Machine Learning with Go (2017) - Build simple, maintainable, and easy to deploy machine learning applications.📖 Interpretable Machine Learning (2018)📖 Deep learning📖 Interpretable machine learning (2018) - Explaining the decisions and behavior of machine learning models.
Certifications & Assessment
Cheatsheets
Code
Conferences
Courses
- AI for Everyone by Andrew Ng (non-technical)
- MIT Deep Learning (2019)
- Amazon’s Machine Learning University course (2018)
🆓 - Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization - Get hands-on experience optimizing, deploying, and scaling production ML models.
💰 - Artificial intelligence for robotics
🆓 - Coursera machine learning
💰 - Introduction to Deep Learning (2018) - Introductory course on deep learning algorithms and their applications.
🆓 - Introduction to Machine Learning for Coders - The course covers the most important practical foundations for modern machine learning.
🆓 - Introduction to matrix methods (2015)
🆓 - Learning from data (2012)
🆓 - Machine Learning Crash Course (2018) - Google's fast-paced, practical introduction to machine learning.
🆓 - Machine learning for data science (2015)
🆓 - Machine learning in Python with scikit-learn
🆓 - Machine Learning with TensorFlow on Google Cloud Platform Specialization - Learn ML with Google Cloud. Real-world experimentation with end-to-end ML.
💰 - Mathematics of Deep Learning, NYU, Spring (2018)
🆓 - mlcourse.ai - Open Machine Learning course by OpenDataScience.
🆓 - Neural networks for machine learning
💰 - Notes
🆓 - Practical Deep Learning For Coders (2018) - Learn how to build state of the art models without needing graduate-level math.
🆓 - Statistical learning (2015)
🆓 - Tensorflow for deep learning research (2017)
🆓
FlashCards
Forums, Group chats
- Artificial Intelligence
- Big data
- Data science
- Learn Machine Learning
- Machine learning
- ML Questions
- DataHouse Discord
Games
Images
Interactives
Learning Plans
Livestreams
Meetups
Newsletters
People
Q&A
Research Papers
- AutoAugment: Learning Augmentation Policies from Data (2018)
- Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm (2017)
- Neural Machine Translation and Sequence-to-sequence Models: A Tutorial (2017)
- Deep voice: real-time neural text-to-speech (2017)
- Dropout: a simple way to prevent neural networks from overfitting (2014)
- A Unifying Review of Linear Gaussian Models (1998)
- Gaussian processes for big data
- Adversarial Patch
- A few useful things to know about machine learning
- The high-interest credit card of technical debt
- InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets