000			01758nam a22002057a 4500
005			20260324130118.0
008			260324b |||||||| |||| 00| 0 eng d
020			_a9789366606606 _c725.00
040			_aS.X.U.K
041			_aEnglish
082			_aR 006.31 RAM(DEP)
100			_aRamalakshmi, R.
245			_aDeep learning _b: algorithms and applications _cR. Ramalakshmi, T. Marimuthu, Vaibhav Gandhi
260			_aDelhi _bCengage _cc2026
300			_avarious pages _bP.B.
500			_aPart 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
650			_aMachine learning
700			_4auth. _aR. Ramalakshmi, T. Marimuthu, Vaibhav Gandhi
942			_cMBA REF
999			_c14625 _d14625