Prashant Parashar

Prashant Parashar

Techie at heart. Loves writing on Java, Scalability, Internet Tech and modern Trends. Villager.

Awesome List - AI and Machine Learning

  1. Fast.ai Course - Computational Linear Algebra
  2. Practical Deep Learning for Coders 2019
  3. Fast.ai course - Deep Learning Part 1
  4. Fast.ai Course - Cutting Edge Deep Learning Part 2
  5. Fast.ai Course - Machine Learning for Coders
  6. Dive into deep learning course - Berkley
  7. Learn TensorFlow and deep learning, without a Ph.D.
  8. Cornell Machine Learning Class - From Scratch
  9. Free Resources for beginners on Deep Learning
  10. A Tour of The Top 10 Algorithms for Machine Learning Newbies
  11. MNIST for ML beginners
  12. Online Course on Neural Networks by Hugo Larochelle
  13. Python Data Science Handbook
  14. Applied Deep Learning Tutorial
  15. Lecture Collection - Convolutional Neural Networks for Visual Recognition Spring 2017
  16. Kaggle Competition Solutions
  17. Developing Bug Free Machine Learning using Mathematics
  18. An end to end implementation of a Machine Learning pipeline
  19. Applications of Deep Learning
  20. Solving logistic regression
  21. An Overview of Multi-Task Learning in Deep Neural Networks
  22. Probability Theory Book - by Jaynes
  23. Building music recommender using Deep Learning
  24. Book - Artificial Intelligence a modern approach by Stuart Russel
  25. Awesome Machine Learning Resources - Programming
  26. Tensorflow Examples
  27. Think Stats
  28. Concrete Introduction to Probability
  29. Awesome Data Science Resources
  30. AI Cheat Sheets
  31. Oxford Deep NLP lectures
  32. Pytorch Tutorial
  33. Mobile Deep Learning
  34. Deep speech
  35. macOS for deep learning with Python, TensorFlow, and Keras
  36. Automatic License Plate Recognition library
  37. Google Colab Free GPU Tutorial
  38. Markov Chains in Python: Beginner Tutorial
  39. Mathematics for Machine Learning Book
  40. Technical Book on Deep Learning
  41. Understanding Basic Machine Learning Algorithms
  42. Book - Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares
  43. Video Introduction to Bayesian Data Analysis, Part 1: What is Bayes?
  44. Hacker’s guide to Neural Networks
  45. MIT Deep Learning course material
  46. University of Toronto Self Driving Cars course
  47. Grokking deep learning resources
  48. Book - Understanding Machine Learning: From Theory to Algorithms
  49. Book - Foundations of Data Science by Avrim Blum, John Hopcroft, and Ravindran Kannan
  50. Book - a brief introduction to Neural Networks
  51. Book - Elemenst of Statistical Learning
  52. Comparative Study on Classic Machine learning Algorithms

Tutorials

  1. a16z AI Playbook
  2. THE Machine Learning Course
  3. RNN Tutorial
  4. Machine Learning in Python
  5. Stanford Deep Learning Tutorial , [http://deeplearning.stanford.edu/tutorial/]
  6. Python Sciekit
  7. Getting started with TensorFlow
  8. Deep Learning Book - MIT Press
  9. Neural Networks and Deep Learning Book
  10. Practical Deep Learning for Coders Course
  11. Deep Learning Comprehensive Course by Google
  12. Learn TensorFlow and Deep Learning without PhD
  13. tflearn - to learn Tensorflow
  14. Awesome Deep Learning Resources
  15. Awesome Deep Learning Papers
  16. Intelligence and Learning- introduction videos
  17. Thinking in tensors writing in Pytorch
  18. A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow
  19. Deep learning with Python and Pytorch Course
  20. Deep Neural Networks with Pytorch
  21. ML from Scratch

Books and Foundation

  1. Understanding Machine Learning from Theory to Algorithms
  2. Foundations of Data Science Book
  3. Algebra, Topology, Differential Calculus, andOptimization TheoryFor Computer Science and Machine Learning
  4. The Matrix Calculus You Need For Deep Learning

NLP

  1. Deep Learning for NLP resources
  2. Stanford Core NLP
  3. CMU Sphinx for Speech to Text
  4. [Stanford CS224U: Natural Language Understanding Spring 2019](https://www.youtube.com/watch?reload=9&v=tZ_Jrc_nRJY)

Useful resources

  1. Open AI
  2. Exploring LSTMs

Recommendation

  1. Netflix 3rd
  2. Make your own Recommendation System
  3. Recommendation Systems at Scale — Making Grab’s everyday app super
  4. DLRM: An advanced, open source deep learning recommendation model
  5. Beginner’s Recommendation Systems with Python

Papers

  1. Foundations of Deep Learning
  2. Learning Collaborative Information Filters
  3. Collaborative Filtering for Implicit Feedback Datasets
  4. Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis
  5. BPR: Bayesian Personalized Ranking from Implicit Feedback
  6. Factorization Machines
  7. Restricted Boltzmann Machines for Collaborative Filtering

Videos

  1. Videos to Learn Machine Learning and Deep Learning
  2. https://vimeo.com/170189199
  3. Deep Learning Online courses list
  4. [Stanford CS230: Deep Learning Autumn 2018 Lecture 2 ](https://onlinehub.stanford.edu/youtube-cs230-deep-learning-winter-2019/181003-cs230-720)
  5. cs234 reinforcement learning video
  6. Best Deep Learning Courses
  7. [Harvard CS 109[(http://cs109.github.io/2015/pages/videos.html)
  8. Stanford 2017 - Lecture Collection - Convolutional Neural Networks for Visual Recognition
  9. Neural Networks class
  10. Pytorch at Tesla by Karpathy

Tools

  1. SyntaxNet

Learn Data Science

  1. Datalab.cc
  2. Introduction to Data Science
  3. Code free Data Science
  4. Data Science Essentials Course by MIT and Microsoft
  5. Coursera Data Science Specialization

Other

  1. Speech to text Sphinx
  2. Top 5 Machine Learning Projects for Beginners
  3. 6 interesting ML Project ideas for beginners
  4. Machine Learning Projects for beginners
  5. How to get started on ML
  6. Machine Learning Infrastructure at Netflix
  7. 70+ Machine Learning Datasets – Gain real-world experience with Data Science projects
  8. Predicting the Stock price Using TensorFlow
  9. CCSM: Scalable statistical anomaly detection to resolve app crashes faster - Facebook
  10. Prediction and Analysis of Time Series Data using Tensorflow