AI Universe
Connecting the dots in AI Universe
🌌 AI Universe Courses
Welcome to *AI Universe Courses* — a curated gateway into the vast world of Artificial Intelligence. This project brings together free and open-access courses across the entire AI spectrum:
- Machine Learning (ML) — foundations, algorithms, applied ML.
- Deep Learning (DL) — neural networks, transformers, generative models.
- Large Language Models (LLMs) — advanced NLP, prompt engineering, applications.
- Natural Language Processing (NLP) — text mining, embeddings, classic methods.
- Computer Vision (CV) — image recognition, visual perception, applied vision AI.
- Reinforcement Learning (RL) — agents, robotics, decision-making under uncertainty.
- Ethics & Governance — responsible AI, fairness, interpretability, compliance.
Our goal is simple: make world-class AI education accessible to everyone. Whether you’re a beginner exploring your first ML model or an advanced learner diving into LLM architectures, this repo is your map through the AI cosmos.
Deep Learning
What is Deep Learning? (DL 01)
Deep Learning Prerequisites (DL 02)
What can a single neuron compute? (DL 03)
How to train your neuron (DL 04)
The Data Analysis Pipeline (DL 05)
Out-of-Sample Validation (DL 06)
Feed-Forward Neural Networks (DL 07)
Neural Network Backpropagation (DL 08)
Softmax & Categorical Crossentropy (DL 09)
Vectorization for Speed (DL 10)
Vanishing / Exploding Gradients (DL 11)
Avoiding Overfitting (DL 12)
Convolutional Layers (DL 13)
Transfer Learning & Data Augmentation (DL 14)
Residual Networks & Skip Connections (DL 15)
Word Embeddings (DL 16)
Recurrent Neural Networks (DL 17)
LSTMs (DL 18)
Transformers & Self-Attention (DL 19)
Other Metrics & ROC Curve (DL 20)
Adam Optimizer (DL 21)
Auto-Encoders (DL 22)
Generative Adversarial Networks (DL 23)
AlphaGo & AlphaGo Zero (DL 24)
Computation Graphs (DL 25)
Automatic Differentiation (DL 26)
Reindex page
make reindex