000			01970nam a22003017i 4500
005			20250722115533.0
008			250722b |||||||| |||| 00| 0 eng d
020			_a9789391030353
040			_aEG-GaU‬‬ _cEG-GaU‬‬ _dEG-GaU‬‬ _erda
082			_223 _a006.3 _bs.s.e.
100	1		_aSoppin, Shashidhar, _eauthor. _960430
245	1	0	_aEssentials of deep learning and AI : _bexperience unsupervised learning, autoencoders, feature engineering, and time series analysis with TensorFlow, Keras, and scikit‑learn / _cShashidhar Soppin, Manjunath Ramachandra, B. N. Chandrashekar.
250			_a 1st edition.
264	1		_aNew Delhi : _bBPB Publications, _c2021.
300			_a394 pages : _billustrations ; _c24 cm.
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
504			_aIncludes bibliographical references.
505			_a1. Introduction ‡ 2. Supervised Machine Learning ‡ 3. System Analysis & Unsupervised Learning ‡ 4. Feature Engineering ‡ 5. Classification, Clustering, Association Rules & Regression ‡ 6. Time Series Analysis ‡ 7. Data Cleanup & Feature Selection ‡ 8. Ensemble Model Development ‡ 9. Design with Deep Learning ‡ 10. MLP Networks ‡ 11. LSTM Networks ‡ 12. Autoencoders ‡ 13. Applications ‡ 14. Emerging & Future Technologies.
520			_aA practical introduction to deep learning and AI emphasizing unsupervised learning techniques—including autoencoders, feature engineering, and time series analysis—using TensorFlow, Keras, and scikit‑learn. Ideal for data scientists and ML engineers. :contentReference[oaicite:1]{index=1}
650		0	_aMachine learning$xStudy and teaching. _960431
650		0	_aAutoencoders (Computer science). _960432
700	1		_aRamachandra, Manjunath, _eauthor. _960433
700	1		_aChandrashekar, B. N., _eauthor. _960434
942			_2ddc _cBK
999			_c11066 _d11066