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AI: Beginning to Advanced
Deep dive from first neurons to production systems. Neural networks, GANs, LLMs, multi-agent systems, and edge AI.
39 chaptersFirst chapter free to preview
After this course, you'll be able to:
✓Build neural networks from scratch — no libraries, pure math
✓Implement CNNs, RNNs, GANs, diffusion models, and LLMs
✓Deploy production AI systems with GPU acceleration
✓Understand every architecture from 2017 Transformers to 2026 frontier models
Full syllabus
2
Learning And Backpropagation
3
Training And Networks
4
Perceptron History And Practical Neurons
5
Layers And Matrix Operations
6
Complete Network And Training
7
Scaling And Performance
8
Computation Graphs And Tensors
9
Mini Pytorch Training
10
Pytorch Comparison And Advanced
11
GPU Architecture And Cuda
12
GPU Tensors And Training
13
Benchmarks And Advanced GPU
14
Convolutional Networks
15
Recurrent Networks
16
Generative Models Overview
17
Reinforcement Learning
18
Autoencoders And Vaes
19
Generative Adversarial Networks
20
Diffusion Models
21
Large Language Models
22
Multi Agent Systems
23
Tool Use And Planning
24
NLP Systems
25
Computer Vision
26
Speech Audio
27
Scientific AI
28
Training At Scale
29
Model Compression
30
Edge AI
31
Production Systems
32
Hardware Acceleration
33
Data Engineering
34
Mlops
35
Multimodal
36
Nas
37
Neurosymbolic AI
38
Emerging Architectures
39
Future Of AI
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