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Ramalakshmi, R.

Deep learning : algorithms and applications R. Ramalakshmi, T. Marimuthu, Vaibhav Gandhi - Delhi Cengage c2026 - various pages P.B.

Part I Foundations

1. Introduction to Deep Learning

2. Machine Learning Fundamentals

3. Mathematical Building Blocks

Part II Deep Learning Models

4. Artificial Neural Network (ANN)

5. Convolutional Neural Network (CNN)

6. Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM)

7. Gated Recurrent Unit (GRU) and Generative Adversarial Networks (GANs)

8. Optimization Algorithms and Regularization Techniques

Part III Advanced Techniques in Deep Learning

9. Auto Encoders, Attention Mechanisms, and Transformers

10. Reinforcement Learning (RL) and Deep Q-Network (DQN)

11. Neural Architecture Search (NAS) and Automated Machine Learning (AutoML)

Part IV Deep Learning Frameworks and Tools

12. Popular Deep Learning Frameworks and Libraries

Part V Ethical and Social Implications of Deep Learning

13. Bias, Fairness, Data Protection, and Ethical Challenges in DL Models

Part VI Deep Learning Applications and Future Trends

14. DL Applications in Computer Vision, NLP, Recommender Systems, and Time-series

15. Challenges, Opportunities and Future Research Trends

ISBN: 9789366606606 725.00

Subjects--Topical Terms:
Machine learning

Dewey Class. No.: R 006.31 RAM(DEP)