Essentials of deep learning and AI : experience unsupervised learning, autoencoders, feature engineering, and time series analysis with TensorFlow, Keras, and scikit‑learn /
Soppin, Shashidhar,
Essentials of deep learning and AI : experience unsupervised learning, autoencoders, feature engineering, and time series analysis with TensorFlow, Keras, and scikit‑learn / Shashidhar Soppin, Manjunath Ramachandra, B. N. Chandrashekar. - 1st edition. - 394 pages : illustrations ; 24 cm.
Includes bibliographical references.
1. 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.
A 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]
9789391030353
Machine learning$xStudy and teaching.
Autoencoders (Computer science).
006.3 / s.s.e.
Essentials of deep learning and AI : experience unsupervised learning, autoencoders, feature engineering, and time series analysis with TensorFlow, Keras, and scikit‑learn / Shashidhar Soppin, Manjunath Ramachandra, B. N. Chandrashekar. - 1st edition. - 394 pages : illustrations ; 24 cm.
Includes bibliographical references.
1. Introduction ‡ 2. Supervised Machine Learning ‡ 3. System Analysis & Unsupervised Learning ‡ 4. Feature Engineering ‡ 5. Classification, Clustering, Association Rules & Regression ‡ 6. Time Series Analysis ‡ 7. Data Cleanup & Feature Selection ‡ 8. Ensemble Model Development ‡ 9. Design with Deep Learning ‡ 10. MLP Networks ‡ 11. LSTM Networks ‡ 12. Autoencoders ‡ 13. Applications ‡ 14. Emerging & Future Technologies.
A practical introduction to deep learning and AI emphasizing unsupervised learning techniques—including autoencoders, feature engineering, and time series analysis—using TensorFlow, Keras, and scikit‑learn. Ideal for data scientists and ML engineers. :contentReference[oaicite:1]
9789391030353
Machine learning$xStudy and teaching.
Autoencoders (Computer science).
006.3 / s.s.e.