ML Fundamentals

Introduction to Machine Learning for Developers at F8 2019

PyTorch 1.2 and PyTorch Hub: A Quick Introduction by Soumith Chintala and Ailing Zhang

Designing a Machine Learning Project with Neal Khosla (2019)

How a Biologist became a Data Scientist

Tutorial 21- What is Convolution operation in CNN?

Tutorial 20- Convolution Neural Network vs Human Brain

These books will help you learn machine learning

Supervised Learning: Crash Course AI #2

Model Understanding and Business Reality (TensorFlow Extended)

Tesla is Going to Win Level 5 - George Hotz | AI Podcast Clips

The Problem with Black Boxes with Cynthia Rudin - TWIML Talk #290

NASA Chief Scientist | Dr. Jim Green | Talks at Google

Manifold Mixup: Better Representations by Interpolating Hidden States

Troubleshooting and Iterating ML Models with Lee Redden (2019)

University of Illinois Master of Computer Science (& MCS-DS) - a top-ranked degree

Distributed Processing and Components (TensorFlow Extended)

TWiML x Fast.ai Deep Learning Part 2 Study Group - Lesson 5

Crash Course Artificial Intelligence Preview

Rage Inside The Machine | Robert Elliott Smith | Talks at Google

The Projects You Should Do To Get A Data Science Job

CNN Training Loop Refactoring - Simultaneous Hyperparameter Testing

Training Loop Run Builder - Neural Network Experimentation Code

Tips and Tricks on Hacking with PyTorch: A Quick Tutorial by Brad Heintz

What is PyTorch?

Tutorial 37: Entropy In Decision Tree Intuition

What is Cross Validation and its types?

Tutorial 19- Training Artificial Neural Network using Google Colab GPU

Applied Deep Learning - Troubleshooting and Debugging with Josh Tobin (2019)

Tutorial 18- Hyper parameter Tuning To Decide Number of Hidden Layers in Neural Network

Tutorial 17- Create Artificial Neural Network using Weight Initialization Tricks

Tutorial 4- Book Recommendation using Collaborative Filtering

Tutorial 16- AdaDelta and RMSprop optimizer

Tutorial 3- Book Recommendation System using Pearson Correlation

Tutorial 2- Creating Recommendation Systems using Nearest Neighbors

Developing a brain atlas using deep learning with Theofanis Karayannis - TWIML Talk #287

Tutorial 15- Adagrad Optimizers in Neural Network

Tutorial 14- Stochastic Gradient Descent with Momentum

Why do I need metadata? (TensorFlow Extended)

AI Governance & Risk Management | Kartik Hosanagar | Talks at Google

Tutorial 13- Global Minima and Local Minima in Depth Understanding

Tutorial 12- Stochastic Gradient Descent vs Gradient Descent

TWiML x Fast.ai Deep Learning Part 2 Study Group - Lesson 4

Tutorial 11- Various Weight Initialization Techniques in Neural Network

TWIMLcon: AI Platforms Keynotes Announced!

Tutorial 10- Activation Functions Rectified Linear Unit(relu) and Leaky Relu Part 2

Snorkel: Programming Training Data with Paroma Varma of Stanford University (2019)

Applied Deep Learning - Data Management with Josh Tobin (2019)

Tutorial 9- Drop Out Layers in Multi Neural Network