Deep Learning Resources

This is a collection of various article links, websites, infographics and papers which will help anyone learn the concepts of Machine Learning and Deep Learning.

Books

1. Deep Learning, Ian Goodfellow, Yoshua Bengio and Aaron Courville
2. The hundred-page ML book, Andriy Burkov
3. Neural Networks and Deep Learning, Michael Nielsen
4. Python Machine Learning, Sebastian Raschka
5. CS321 Course Notes by Roger Grosse.

Websites and Blogs

1. r2d3 nice visualizations for understanding ML techniques
2. Qure.ai for AI in radiology
3. skymind basic concepts of AI
4. OpenAI
5. DeepMind
6. Distill for clear explanations of machine learning
7. Jeremy Jordan blog
8. Daniil’s blog
9. Lil’Log
10. Google AI Blog
11. Uber AI Labs
12. Jay Alammar’s blog
13. Berkeley AI Research
14. Sebastian Ruder
15. Facebook AI Blog

Courses

1. cs231n Convolutional Neural Networks
2. Machine Learning CMU
3. Introduction to Deep Learning CMU
4. Reinforcement Learning UCL
5. Self Driving Cars
6. Fast.ai
7. Machine Learning by Andrew Ng, Coursera
8. Deeplearning.ai, DL Specialization by Andrew Ng, Coursera
9. Deep RL Bootcamp
10. Linear Algebra
11. Roger Grosse CSC 321
12. Computer Vision

Articles and Tutorials

1. Understanding of t-SNE
2. Setting up Learning Rate
3. Implementation of TFRecords
4. Introduction to Reinforcement Learning
5. Data Science and Robots
6. Understanding LSTM
7. Speech Processing
8. Autoencoders Keras
9. Bucket Iterator R2RT
10. Deep Learning tutorials
11. Image Segmentation techniques
12. Transpose Convolution
13. cs231n Andrej Karpathy
14. Key Papers in Deep RL
15. A Recipe for Training Neural Networks, by Andrej Karpathy.

Videos

1. Entropy, Cross-Entropy and KL Divergence.
2. cs231n CNN lecture series.
3. Deep Reinforcement Learning Bootcamp.
4. Deep Unsupervised Learning by UC Berkeley.
5. ML with python from Sentdex.

Cheatsheets

1. Pandas
2. Numpy
3. Matplotlib
4. Seaborn
5. Scikit-learn
6. Keras
7. Python Basics
8. Importing Data
9. CNN
10. RNN
11. AI/ML/DL
12. Deep Learning

GitHub Repositories

1. Goku Mohandas practicalAI

Datasets

1. Grand Challenge
2. Kaggle
3. Visual Data
4. UCI ML Repository
5. SkyMind
6. deeplearning.net
7. AWS
8. Microsoft
9. GitHub
10. Chahub

Hackathons

1. Machine Hack
2. OpenEd.ai
3. Kaggle
4. TechGig
5. OpenML
6. HackerEarth

Pretrained Models

1. ModelZoo
2. IBM
3. Kaggle

Other Guides

1. ML Resources by Sam Finlayson
2. Incomplete DL Guide by Sanny Kim