ML Fundamentals

Build a 2D convolutional neural network, part 10: Connecting layers

Build a 2D convolutional neural network, part 9: Adding layers

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

Run Jupyter Lab for Python, R, Swift from Google Colab with ColabCode

L3.1 About Brains and Neurons

Rosanne Liu — Conducting Fundamental ML Research as a Nonprofit

L3.5 The Geometric Intuition Behind the Perceptron

Deep Networks Are Kernel Machines (Paper Explained)

Let's talk about AGI: Elon Musk vs Andrew Ng on Superintelligence

Capturing Object Detection History with Tensorflow Object Detection and Python

Code With Me : Decision Trees

This Neural Network Makes Virtual Humans Dance! 🕺

Predicting Stock Prices in Python

A Future of Work for the Invisible Workers in A.I. with Saiph Savage - #447

Making a Board Game using MCMC!

Object Localization Vs Object Detection Deep Learning

The Importance and Concern of NLP in National Intelligence with Sean Gourley, Primer CEO

Build a 2D convolutional neural network, part 8: Training code setup

Build a 2D convolutional neural network, part 7: Why Cottonwood?

Build a 2D convolutional neural network, part 6: Examples of successes and failures

Build a 2D convolutional neural network, part 5: Pre-trained model results

Build a 2D convolutional neural network, part 4: Model overview

Build a 2D convolutional neural network, part 3: MNIST digits

Build a 2D convolutional neural network, part 2: Overview

Build a 2D convolutional neural network, part 1: Getting started

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

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

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

Build a 1D convolutional neural network, part 4: Training, evaluation, reporting

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

Implement 1D convolution, part 2: Comparison with NumPy convolution()

Implement 1D convolution, part 1: Convolution in Python from scratch

L3.3 Vectorization in Python

L3.4 Perceptron in Python using NumPy and PyTorch

L3.2 The Perceptron Learning Rule

L3.0 Perceptron Lecture Overview

L2.4 The Deep Learning Hardware & Software Landscape

L2.3 The Origins of Deep Learning

L2.1 Artificial Neurons

L2.2 Multilayer Networks

L2.0 A Brief History of Deep Learning -- Lecture Overview