ML Fundamentals

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

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

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)

Practical MLOps // Noah Gift // MLOps Coffee Sessions #27

Automated Videoing Assistant - Made with TensorFlow.js

2021 Microsoft Research Ada Lovelace Fellow: Demba Komma

Metropolis - Hastings : Data Science Concepts

How to serve any machine learning or deep learning model using FastAPI

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()

L3.3 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

L1.3.4 Broad Categories of ML Part 4: Special Cases of Supervised Learning

L1.3.3 Broad Categories of ML Part 3: Reinforcement Learning

L1.3.2 Broad Categories of ML Part 2: Unsupervised Learning

L1.3.1 Broad Categories of ML Part 1: Supervised Learning

L1.2 What is Machine Learning?

L1.1.2 Course Overview Part 2: Organization (Optional)

L1.1.1 Course Overview Part 1: Motivation and Topics

L1.0 Intro to Deep Learning, Course Introduction

Albumentations Tutorial for Data Augmentation (Pytorch focused)

Intro to Python chapter 2.5, Building a timer