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Introduction to AI: Neural Nets, LLM & Generative AI
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Introduction
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Great textbooks in the field
Web resources
Zoom for Remote Participation
Slack Invite Link
Quizzes
Getting started
Activation Functions
Autoencoders
Convolutional Neural Networks
Day 2: Neural Nets are Universal Approximators
Starter Code Archive
Day 3: Gradient Descent
Gradient Descent on Old Faithful Geyser
3Blue1Brown: gradient descent
Loss Landscape Visualization
Day 4: Activation Functions
Notes on activation functions
Activation functions in PyTorch
Three Decades of Activations: A Comprehensive Survey of 400 Activation Functions for Neural Networks
Jupyter notebook on activation functions
Day 4: PyTorch
Homework: PyTorch Models from Scratch
PyTorch Website
PyTorch YouTube Channel
PyTorch Lightning
Day 4: Back Propagation
Excellent 3Blue1Brown video on back-propagation
A Step by Step Backpropagation Example
How the backpropagation algorithm works
A Comprehensive Guide to the Backpropagation Algorithm in Neural Networks
[Youtube] The Absolutely Simplest Neural Network Backpropagation Example
Day 5: Pytorch homework solution
Partial Solutions
Day 5: Optimizers
Why Momentum Really Works
An overview of gradient descent optimization algorithms
A survey of deep learning optimizers -- first and second order methods
Convex Optimization, by Boyd & Vandenberghe
Stanford: Convex Optimization I
Stanford: Convex Optimization II
Day 5: Loss Functions
A Comprehensive Survey of Loss Functions in Machine Learning
A survey and taxonomy of loss functions in machine learning
StatQuest: Cross-Entropy Loss
Day 5 Quiz
Day 6: Convolutional Networks
Reading Assignment
CNN Explainer
CNN chapter from Dive in Deep Learning
Inceptionism: Going Deeper into Neural Networks
Chris Olah's must read on Feature Visualization
Collection of CNN structures
PyTorch Vision
OpenCV
Pillow
scikit-image
[Inception] Going Deeper with Convolutions
[AlexNet] ImageNet Classification with Deep Convolutional Neural Networks
[Resnet] Deep Residual Learning for Image Recognition
Day 8: Autoencoders
Wikipedia
The Information Bottleneck View of Deep Learning: Why do we need it?
Contractive Auto-Encoders: Explicit Invariance During Feature Extraction
Sparse Autoencoders
Extracting and Composing Robust Features with Denoising Autoencoders
Generative Adversarial Networks
Playing with GAN Lab
"Awesome" Github compilation on GAN
[Original Paper] Generative Adversarial Networks
Generative Adversarial Networks: An Overview
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Neural Style Transfer
"Awesome" curated compilation on Neural Art
"Awesome" Style Transfer
A Neural Algorithm of Artistic Style
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
A Style-Aware Content Loss for Real-time HD Style Transfer
Neuralstyle.art
Transformers
Peter Bloem: Transformers from scratch
Jay Alammar: The Illustrated Transformer
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Course info
Introduction to AI: Neural Nets, LLM & Generative AI
Teacher:
Asif Qamar
Skill Level
:
Beginner