ML Fundamentals

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

Neural Networks Part 5: ArgMax and SoftMax

Building a recommendation system using deep learning

Deep Learning News #2, Feb 6 2021

Build a 2D convolutional neural network, part 17: Cottonwood cheatsheet

Build a 2D convolutional neural network, part 16: Cottonwood code tour

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

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

Build a 2D convolutional neural network, part 15: Rendering examples

Build a 2D convolutional neural network, part 14: Collecting examples

Build a 2D convolutional neural network, part 13: Loss history and text summary

Build a 2D convolutional neural network, part 12: Testing loop

Build a 2D convolutional neural network, part 11: The training loop

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

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

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

L3.5 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