Applied machine learning and AI for engineers : solve business problems that can't be solved algorithmically
Material type:![Text](/opac-tmpl/lib/famfamfam/BK.png)
- 9789355422354
- 006.31 PRO-A
Item type | Current library | Collection | Call number | Status | Date due | Barcode | Item holds |
---|---|---|---|---|---|---|---|
![]() |
IIITD General Stacks | Computer Science and Engineering | 006.31 PRO-A (Browse shelf(Opens below)) | Available | 012040 |
Browsing IIITD shelves, Shelving location: General Stacks, Collection: Computer Science and Engineering Close shelf browser (Hides shelf browser)
![]() |
![]() |
![]() |
![]() |
![]() |
![]() |
![]() |
||
006.310 NIE-E Essential math for data science : take control of your data with fundamental linear algebra, probability, and statistics | 006.31 PAT-D Deep learning : a practitioner's approach | 006.31 PAT-H Hands-on unsupervised learning using python : | 006.31 PRO-A Applied machine learning and AI for engineers : | 006.31 PRU-P Practicing trustworthy machine learning : | 006.31 RAH-M Machine learning using R | 006.31 RAM-T TensorFlow for deep learning : |
This book includes an index.
I. Machine Learning with Scikit-Learn 1. Machine Learning 2. Regression Models 3. Classification Models 4. Text Classification 5. Support Vector Machines 6. Principal Component Analysis 7. Operationalizing Machine Learning Models II. Deep Learning with Keras and TensorFlow 8. Deep Learning 9. Neural Networks 10. Image Classification with Convolutional Neural Networks 11. Face Detection and Recognition 12. Object Detection 13. Natural Language Processing 14. Azure Cognitive Services
There are no comments on this title.