Turtles! Hill Climbing! Hammers! Paper Bags!
Explore optimization techniques through turtle graphics, from hill climbing to simulated annealing, laying the groundwork for understanding neural networks and machine learning concepts.
A Gentle Introduction to Data Science
Explore data science fundamentals, from AI's influence to practical machine learning in Python. Covers key concepts, problem-solving applications, and essential Python tools for aspiring data scientists.
An Introduction to PyTorch & Autograd
Explore PyTorch's dynamic computation graphs and Autograd package for efficient deep learning, with practical examples and insights into GPU processing and algorithmic differentiation.
Deep Learning with TensorFlow
Interactive TensorFlow session: build deep learning models from basics to advanced concepts. Hands-on approach to creating and using models in applications, suitable for beginners and experienced developers alike.
Writing a Neural Net from Scratch
Learn to build a neural network for image recognition from scratch in C#, covering key concepts like gradient descent and backpropagation without complex math or external libraries.
Writing a Neural Net from Scratch
Learn to code a neural network for image recognition in C# from scratch, covering gradient descent, activation functions, and backpropagation without complex math or external libraries.
Pytorch Neural Network Example
Step-by-step guide to building a simple neural network using PyTorch, covering network creation, data handling, loss calculation, and accuracy evaluation.
Neural ODEs: Continuous-Time Models for Machine Learning - TMLS 2019
Explore neural networks as implicit models and their applications in scientific computing with David Duvenaud at TMLS2019.
Backpropagation: Training Deep Neural Networks
Explore backpropagation, a crucial technique for training deep neural networks through gradient descent, with insights from ASU's Paulo Shakarian.
Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures
Explores Direct Feedback Alignment as a biologically plausible alternative to backpropagation, demonstrating its effectiveness in training modern deep learning architectures for various challenging tasks.
Recurrent Neural Networks and Transformers
Comprehensive exploration of recurrent neural networks, LSTMs, and attention mechanisms in deep learning, covering theory, implementation, and applications for sequence modeling tasks.
MIT Introduction to Deep Learning
Comprehensive introduction to deep learning fundamentals, covering perceptrons, neural networks, loss functions, gradient descent, backpropagation, and regularization techniques for building effective models.
Introduction to Deep Learning
Comprehensive introduction to deep learning fundamentals, covering perceptrons, neural networks, loss functions, gradient descent, backpropagation, and regularization techniques for building effective models.
Deep Dive: Gradients in Ivy - Tutorial 20
Explore gradients in Ivy, mastering advanced techniques for deep learning and neural network optimization.
Deep Learning - What is it and What It Can Do For You
Explore deep learning fundamentals, architectures, and applications in AI, from basics to advanced concepts like CNNs and RNNs, with practical insights for implementation and future possibilities.
Understanding 1-Bit Large Language Models - From Theory to Implementation
Dive into the revolutionary concept of 1-bit Large Language Models, exploring how weights can be represented with simple integers (0, 1, -1) instead of complex floating-point numbers.
Learning Paradigms for Neural Networks: The Locally Backpropagated Forward-Forward Algorithm
Explore innovative neural network training methods combining backpropagation and forward-forward algorithms to address limitations in complex, physics-related AI applications.
EfficientML.ai - Basics of Neural Networks - Lecture 2
Explore neural network fundamentals, including architecture, training, and optimization techniques. Gain insights into efficient machine learning concepts and applications.
Basics of Neural Networks - EfficientML.ai Lecture 2
Explore neural network fundamentals, including architecture, training, and optimization techniques, in this comprehensive MIT lecture on efficient machine learning.
Introduction to Deep Neural Networks - Lecture 21
Explore deep neural networks, their architecture, and applications in AI. Learn key concepts and techniques for implementing advanced machine learning models.
GShard- Scaling Giant Models with Conditional Computation and Automatic Sharding
Detailed explanation of Google's 600 billion parameter transformer for multilingual translation, focusing on scaling techniques, mixture-of-experts, and automatic sharding implementation on TPUs.