ML Fundamentals

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

L6.5 A Closer Look at the PyTorch API

L6.4 Training ADALINE with PyTorch -- Code Example

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

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

Join us at TensorFlow Everywhere

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

How does a Data Scientist Fight FRAUD?

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

The SoftMax Derivative, Step-by-Step!!!

Neural Networks Part 5: ArgMax and SoftMax

Simple Explanation of LSTM | Deep Learning Tutorial 36 (Tensorflow, Keras & Python)

Build a 1D convolutional neural network, part 7: Evaluate the model

Build a 1D convolutional neural network, part 6: Text summary and loss history

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

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

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

Build a 1D convolutional neural network, part 5: One Hot, Flatten, and Logging blocks

Build a 1D convolutional neural network , part 3: Connect the blocks into a network structure

Build a 1D convolutional neural network , part 2: Collect the Cottonwood blocks

Build a 1D convolutional neural network, part 1: Create a test data set

Implement 1D convolution, part 7: Weight gradient and input gradient

Implement 1D convolution, part 6: Multi-channel, multi-kernel convolutions

Implement 1D convolution, part 5: Forward and backward pass

Implement 1D convolution, part 4: Initialize the convolution block

Implement 1D convolution, part 3: Create the convolution block