ML Fundamentals

L4.4 Notational Conventions for Neural Networks

L4.3 Vectors, Matrices, and Broadcasting

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

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

Rosanne Liu — Conducting Fundamental ML Research as a Nonprofit

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

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

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

15 Programming Project Ideas - From Beginner to Advanced

PyTorch Quick Tip: Mixed Precision Training (FP16)

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

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

L3.1 About Brains and Neurons

L3.5 The Geometric Intuition Behind the Perceptron

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

L1.6 About the Practical Aspects and Tools Used in This Course

L1.5 Necessary Machine Learning Notation and Jargon

L1.4 The Supervised Learning Workflow