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Master

Training Deep Networksfirst 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 Training Deep Networks (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.

Training Deep Networks (Deep Learning)

1. Unstable Gradients

1.1 Vanishing & Exploding Gradients
1.2 Weight Initialization
1.3 Better Activation Functions
1.4 Batch & Layer Normalization
1.5 Gradient Clipping & Skip Connections

2. Optimizers and Learning Rate Schedules

2.1 Momentum & Nesterov
2.2 Adaptive Optimizers
2.3 Learning-Rate Scheduling

3. Regularization for Deep Nets

3.1 Weight Decay (L1/L2)
3.2 Dropout & MC Dropout
3.3 Data Augmentation
3.4 Early Stopping & Max-Norm
3.5 Label Smoothing & Stochastic Depth

4. Debugging and Diagnosing Training

4.1 Sanity Checks Before Training
4.2 Reading Loss Curves
4.3 NaNs, Divergence & Loss Spikes

5. Transfer Learning and Fine Tuning

5.1 Transfer Learning & Fine-Tuning
5.2 Self-Supervised Pretraining
5.3 Parameter-Efficient Fine-Tuning

6. Training at Scale

6.1 Efficient Data Pipelines
6.2 Mixed Precision Training
6.3 Distributed Training
6.4 Experiment Tracking & Checkpointing at Scale
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