ML Fundamentals

How would a Data Scientist analyze Customer Churn?

GOOGLE FIRES AI ETHICS TEAM 📰 DEEP NEWS 📰

Simple Explanation of GRU (Gated Recurrent Units) | Deep Learning Tutorial 37 (Tensorflow & Python)

Deep Learning News #4, Feb 20 2021

Is Machine Learning by Andrew Ng on Coursera worth it in 2021? [Course Review]

'Git for Data' - Who, What, How and Why? // Luke Feeney & Gavin Mendel-Gleason // MLOps Meetup #52

Stochastic Gradient Descent and Deploying Your Python Scripts on the Web | Real Python Podcast #48

Daphne Koller — Digital Biology and the Next Epoch of Science

Jim Keller: The Future of Computing, AI, Life, and Consciousness | Lex Fridman Podcast #162

UMAP explained | The best dimensionality reduction?

L7.0 GPU resources & Google Colab

2020 PyTorch Summer Hackathon Winners Recap

Javier Ideami on Loss Landscapes and the Flatland Perspective

IBM Applied AI Professional Certificate: Gain AI Skills on Coursera

Explainable AI explained! | #2 By-design interpretable models with Microsofts InterpretML

How to get started with Graph ML? (Blog walkthrough)

An AI software able to detect and count plastic waste in the ocean

The Art of Learning Data Science (How to learn data science)

Intel: Machine Learning and the Future of the Data Center w/Intel

What is OneAPI? The Software Tool Gap: A Roundtable Discussion

Join us at TensorFlow Everywhere

EfficientNet from scratch in Pytorch

EfficientNet Paper Walkthrough

L6.5 A Closer Look at the PyTorch API

L6.4 Training ADALINE with PyTorch -- Code Example

L6.3 Automatic Differentiation in PyTorch -- Code Example

L6.2 Understanding Automatic Differentiation via Computation Graphs

L6.1 Learning More About PyTorch

L6.0 Automatic Differentiation in PyTorch -- Lecture Overview

How to solve Santander Kaggle Transaction Competition [Top 1% Solution, No Ensemble]

L5.8 Adaline Code Example

L5.7 Training an Adaptive Linear Neuron (Adaline)

L5.6 Understanding Gradient Descent

L5.5 (Optional) Calculus Refresher II: Gradients

L5.4 (Optional) Calculus Refresher I: Derivatives

L5.3 An Iterative Training Algorithm for Linear Regression

L5.2 Relation Between Perceptron and Linear Regression

L5.1 Online, Batch, and Minibatch Mode

L5.0 Gradient Descent -- Lecture Overview

Piero Molino — The Secret Behind Building Successful Open Source Projects

How to do the Titanic Kaggle Competition

Intel: How Google Health Uses Machine Learning With Intel

L4.5 A Fully Connected (Linear) Layer in PyTorch

L4.4 Notational Conventions for Neural Networks

L4.3 Vectors, Matrices, and Broadcasting

L4.2 Tensors in PyTorch

L4.1 Tensors in Deep Learning

L4.0 Linear Algebra for Deep Learning -- Lecture Overview