- CS 230 ― Deep Learning Cheatsheet
- Complete Deep Learning by Krish Naik | Notes
- DL Stanford CS230: 2018 Lectures from Stanford graduate course CS230 taught by Andrew Ng
- MIT 6.S191: Introduction to DL
- MIT 6.S192: Deep Learning for Art, Aesthetics, and Creativity
- Convolutional Neural Networks for Visual Recognition
- Deep Learning Architectures Comparative Analysis
- Deep Learning Specialization on Coursera (offered by deeplearning.ai)
- http://introtodeeplearning.com/2021/index.html
- https://d2l.ai/chapter_preface/index.html#about-this-book
- https://www.deeplearningbook.org/front_matter.pdf
- Project: generate your data in parallel with PyTorch
- Zero to Mastery Learn PyTorch for Deep Learning
- git: the-incredible-pytorch
- PyTorch Tutorial: How to Develop Deep Learning Models with Python
- GAN - Generative Adversarial Networks
- Swiggy- Data Science blogs
- Amazon Science blogs
- ML/ Recommendation Netflix Research
- Getting started with Machine Learning in MS Excel using XLMiner
- When You Should Not Use Accuracy to Evaluate Your Machine Learning Model
- What are the disadvantages of accuracy?
- 3 Best metrics to evaluate Regression Model?
- Hyperparameter Tuning the Random Forest in Python
- A Beginner’s Guide to Random Forest Hyperparameter Tuning
- 15 Generative Adversarial Networks (GAN) Based Project Ideas
- Breaking it Down: Logistic Regression
- ML Collaboration: Best Practices From 4 ML Teams
- An ML approach for routing payment transactions
- Structure of Data Science Projects
- How a machine learning project pipeline is build
- A visual introduction to machine learning