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Generative Deep Learningfirst principles.

DEEP LEARNING • FROM NEURONS TO TRANSFORMERS.

Chapter-by-chapter theory notes, step-by-step textbook exercises, video lectures, and SLO-aligned board examination prep for Generative Deep Learning (Deep Learning).

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Neural Networks (18)Training Deep Networks (23)Computer Vision (15)Sequences & Transformers (19)Generative Deep Learning (14)Reinforcement & Frontier (12)
Syllabus

Curriculum Syllabus

Course chapter sequence roadmap and learning outcomes.

Generative Deep Learning (Deep Learning)

1. Autoencoders

1.1 Efficient Data Representations
1.2 Stacked & Convolutional Autoencoders
1.3 Denoising & Sparse Autoencoders
1.4 Variational Autoencoders

2. Generative Adversarial Networks

2.1 How GANs Work
2.2 Training Difficulties
2.3 DCGAN & StyleGAN
2.4 Conditional GANs & Image-to-Image

3. Diffusion Models

3.1 The Forward & Reverse Process
3.2 Training & Sampling
3.3 Latent Diffusion & Guidance

4. Autoregressive and Multimodal Generation

4.1 Autoregressive Generation & Sampling
4.2 Multimodal Generative Models
4.3 Evaluating Generative Models
INTERACTIVE LAB • 18 STEM WORKSTATIONS

Bridge High-School Science to Computational AI

Connect textbook physics, mathematics, and computing to real-world artificial intelligence models and symbolic solvers.

scienceOpen Interactive Laboratories→Board MCQs & Past Papers