Deep learning : algorithms and applications R. Ramalakshmi, T. Marimuthu, Vaibhav Gandhi

By:

Ramalakshmi, R

Contributor(s):

R. Ramalakshmi, T. Marimuthu, Vaibhav Gandhi [auth.]

Material type: Text

TextLanguage: English Publication details: Delhi Cengage c2026Description: various pages P.BISBN:

9789366606606

Subject(s):

Machine learning

DDC classification:

R 006.31 RAM(DEP)

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Item type	Current library	Collection	Call number	Vol info	Copy number	Status	Barcode
MBA Referance	St. Xavier's University, Kolkata Reference Section	Reference	R 006.31 RAM(DEP) (Browse shelf(Opens below))	S.X.U.K	5717	Not For Loan	UM5717
MBA - Masters on Business Administration	St. Xavier's University, Kolkata Lending Section		006.31 RAM(DEP) (Browse shelf(Opens below))	S.X.U.K	5718	Available	M5718
MBA - Masters on Business Administration	St. Xavier's University, Kolkata Lending Section		006.31 RAM(DEP)C1 (Browse shelf(Opens below))	S.X.U.K	5719	Available	M5719
MBA - Masters on Business Administration	St. Xavier's University, Kolkata Lending Section		006.31 RAM(DEP)C2 (Browse shelf(Opens below))	S.X.U.K	5720	Available	M5720
MBA - Masters on Business Administration	St. Xavier's University, Kolkata Lending Section		006.31 RAM(DEP)C3 (Browse shelf(Opens below))	S.X.U.K	5721	Available	M5721

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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

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