Resources Shared in Slack
Here is a collection of relevant resources that were shared in Slack over the duration of the workshop.
Articles
AI vs. machine learning vs. deep learning vs. neural networks: What’s the difference?
>Author: IBM Data and AI Team
>Topic: What differences make AI vs. machine learning vs. deep learning vs. neural networks each a unique technology?
AlphaFold: When AI Meets Biology
>Author: Nigel Ma, for Medium
>Topic: What is AlphaFold protein structure prediction?
Understanding Decision Trees
>Author: Jainvidip, for Medium
>Topic: Comprehensive explanation of decision trees, including Python examples
A Practical Example of How to Use Pandas to Feature Engineer Messy Data
> Author: Lexi Base, for Medium
> Topic: Walkthrough of using Python and Pandas for feature engineering
Datasets: Class-imbalanced datasets
>Author: Google
>Topic: Strategies for using imbalance datasets in machine learning
But what is a Neural Network?
>Author: Grant Sanderson and Josh Pullen
>Topic: An overview of what a neural network is, introduced in the context of recognizing hand-written digits.
Batching and Mini-Batch: Making Your Deep Learning Model Work Efficiently
>Author: NasuhcaN for Medium
>Topic: How to divide your dataset into smaller parts for training a machine learning model, with PyTorch examples
In-Depth: Decision Trees and Random Forests
>Author: Jake VanderPlas
>Topic: Differences between random forests and decision trees
Weight initialization and regularization
>Author: Stanford CS230
>Topic: Weight initialization and regularization during Neural Network training
Books & Textbooks
A Brief History of Intelligence: Evolution, AI, and the Five Breakthroughs That Made Our Brains
>Author: Max S. Bennett
>Topic: Book about the connections between neurosciences and AI
Basics of Machine Learning
>Author: Martha White
>Topic: Textbook on core concepts in machine learning: probabilistic underpinnings, estimators, evaluating confidence in an estimator, bias-variance, generalization and overfitting, regularization and basic optimization algorithms
Intermediate Machine Learning
>Author: Martha White
>Topic: Machine learning textbook expanding on “Basics of Machine Learning”
Visualizations
Backprop Explainer
>Author: Donny Bertucci
>Topic: Explanation and visual representation of backpropagation
Transformer Explainer: LLM Transformer Model Visually Explained
>Author: Polo Club of Data Science, Georgia Tech
>Topic: Visualization tool for exploring transformer models
Gradient Descent Visualiser
>Author: ACM at UCLA
>Topic: Visual explanation of gradient descent
Underfitting vs Overfitting with Visualization
>Author: Shrinath Venkatesh, for Medium
>Topic: Visual explanation of overfitting and underfitting