ML Fundamentals

PerceptiLabs-The Best Machine Learning Visual Modeling Tool-Train Deep Learning Neural Network

L7.0 GPU resources & Google Colab

2020 PyTorch Summer Hackathon Winners Recap

Code With Me : Gibbs Sampling

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)

L6.5 A Closer Look at the PyTorch API

Session On Different Types Of Loss Function In Deep Learning

Coding MCMC : Data Science Code

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

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

Piero Molino — The Secret Behind Building Successful Open Source Projects

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

How to do the Titanic Kaggle Competition

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

Speech Command Recognition With Tensorflow.JS and React.JS | Javascript AI

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

Join us at TensorFlow Everywhere

How does a Data Scientist Fight FRAUD?

Push Notifications from Jupyter Notebook after Code Execution [Python for Data Science]

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

Sunday Late Night Live Q&A - Ask Anything Related To Data Science

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

Democratize AI with OneAPI

Intel: How Google Health Uses Machine Learning With Intel

Bayesian Treasure Hunt : Data Science Code

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

Advice on Publishing Machine Learning Papers with MLC's founder Rosanne Liu

What the Heck is Bayesian Stats ?? : Data Science Basics