Resource Focus
Deep training & efficiency
Revisit accelerators, mixed precision, distributed training, optimisation and scaling considerations.
Architecture families
Navigate CNNs, computer vision, RNNs, LSTMs/GRUs, attention and Transformers.
Representation & generation
Return to transfer learning, embeddings, generative models and multimodal/foundation-model concepts.
Research bridge
Use the module’s cited papers as a bridge from textbook explanations into primary research literature.
Chapter Resource Map
Every chapter in this Module already contains its own Key Terms and References & Further Reading collections. These links open the relevant chapter and automatically expand the requested resource section.
Deep Training & Computer Vision
Chapter 1 — Deep Learning Foundations
Use the chapter resource layer to revise terminology or open the source list used for deeper verification and study.
Chapter 2 — Training Deep Neural Networks
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Chapter 3 — Convolutional Neural Networks
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Chapter 4 — Advanced Computer Vision
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Sequence Models, Attention & Transformers
Chapter 5 — Recurrent Neural Networks
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Chapter 6 — LSTM and GRU Architectures
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Chapter 7 — Attention Mechanisms
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Chapter 8 — Transformers
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Representations, Generative & Foundation Models
Chapter 9 — Transfer Learning, Embeddings and Representation Learning
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Chapter 10 — Deep Generative Models
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Chapter 11 — Foundation Models, Large Language Models and Multimodal AI
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Deployment, Efficiency & Responsible Deep Learning
Chapter 12 — Deployment, Efficiency, Responsible Deep Learning and Capstone
Use the chapter resource layer to revise terminology or open the source list used for deeper verification and study.
Curated External Reference Shelf
This shelf surfaces a small set of high-value references already used by the Module. The complete chapter-specific source lists remain inside the individual chapters.
| Resource | Why it is useful | Used in |
|---|---|---|
| Deep Learning — Goodfellow, Bengio & Courville | Broad deep-learning theory reference | Chapter 1 |
| PyTorch Documentation | Framework reference for training and deployment examples | Chapters 1–12 |
| Deep Residual Learning for Image Recognition | Residual-network architecture and deep vision | Chapters 1–3 |
| Attention Is All You Need | Transformer architecture and self-attention | Chapters 7–8 |
| BERT | Bidirectional Transformer pre-training | Chapter 8 |
| Retrieval-Augmented Generation | Retrieval-connected language-model systems | Chapter 11 |
| LoRA | Parameter-efficient model adaptation | Chapters 9, 11 |
| Denoising Diffusion Probabilistic Models | Diffusion-based generative modelling | Chapter 10 |
| NIST AI Risk Management Framework | Responsible deployment and risk-management reference | Chapter 12 |
Continue the Learning Journey
Return to Deep Learning
Continue the full 12-Chapter learning sequence rather than using the resource directory as a substitute for the lessons.
Open the Module →Next: AI Models & Model Ecosystems
Continue into the next logical learning area when you are ready.
Continue Learning →NIYAMA-VIDYĀ Resources
Return to the permanent site-wide directory for Learn, Explore, Applied AI and transparency resources.
Back to Resources →Reference Transparency
External documentation and publications remain the property of their respective owners and are provided for attribution, verification and further learning. See Third-Party Rights & Attribution and the Website Disclaimer for the site-wide transparency framework.