Links to useful deep learning resources
- Rules of Machine Learning, Zinkevich M.
- Learn Pandas
- Weights initializing
- The Python graph Gallery, Data Visualization
- Seaborn Example Gallery, Data Visualization
- Tensorboard.dev
- Deep Learning Papers Reading Roadmap, by floodsung, github
- Neocognitron: A Self-organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position, Fukushima, 1980, first CNN paper
- Deep Residual Learning for Image Recognition, He K., 2015, resnet block
- Explained.ai, a bunch of papers very enlightening
- GAN Lab, Understanding Deep Generative Models interactively
- Parameter optimization in neural network, DeepLearning.ai
- TF Projector
- MIT OpenCourseware Plenty of free academic resources from MIT.
- from Data to Viz
- Intializing Neural Networks, deeplearning.ai
- Setosa.io, Explained Visually, Image Kernels
- Optimizing gradient descent, ruder.io, Gradient descent overview
- Transformers in Vision
- Deep Learning Specialization, deeplearning.ai
- Practical Deep Learning for Coders, fast.ai
- Deep Learning, DS-GA 1008 · SPRING 2020 · NYU CENTER FOR DATA SCIENCE
- Deep Learning, Udacity
- MIT 6.S191 Introduction to Deep Learning
- Full Stack Deep Learning, UC Berkeley Instructors
- Deep Learning, Yann LeCun & Alfredo Canziani, NYU Center for Data SCience
- Deep Learning Drizzle, a curated gigantic list of deep learning related courses.
- Python for DataScience, DataCamp
- ML cheatsheet
- DS in Python learning path, Analyticsvidhya
- Numpy, DataCamp
- ScikitLearn, Datacamp
- ScikitLearn, analyticsvidhya
- EDA with Pandas, analyticsvidhya
- EDA in Python, Analyticsvidhya
- More cheatsheets (28), Analyticsvidhya
- Deep Learning Cheatsheets, Stanford University
- DEV, pytest cheatsheet
- What should I do when my neural network doesn't learn?, StatsExchange, very useful summary of typical critical points in DL experiments setup
- Debugging Neural Networks, Stackoverflow
- How to initialize weights in Pytorch, Stackoverflow, Weights initializations comparison
- AWS Open Data Registry
- Datasetlist
- Elite Datascience, DSets for DS and ML
- Data sets, Preflib
- Generatedata.com
- Google Public Data sets
- Data Packaged Core Datasets
- The Big Bad NLP Database
- CVOnline, Edinburgh School of Informatics, very good index
- Deep Learning; I.Goodfellow, Bengio, Courville
- Pattern Recognition and Machine Learning; C.Bishop
- Mathematics for Machine Learning; Deisenroth, Faisal
- Jean Feydy's teaching page, it has a lot of very insightful lectures.
- Machine Learning: Basic Principles, Jung A., 2018
- Python Data Science Handbook
- Problem Solving with Algorithms and Data Structures, Miller,Ranum
- Practical Algorithms and Data Structures
- Python Tips
- Python books, web site with links to free Python books.
Here are some places where you can find interesting collections about a lot of useful stuff