TensorFlow2 pocket reference : (Record no. 79861)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01513nam a22001577a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 220805b2021 |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9789391043827 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 TUN-K |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Tung, K.C. |
| 245 ## - TITLE STATEMENT | |
| Title | TensorFlow2 pocket reference : |
| Remainder of title | building and deploying machine learning models / |
| Statement of responsibility, etc. | K C Tung |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc. | India |
| Name of publisher, distributor, etc. | Shroff Publishers |
| Date of publication, distribution, etc. | 2021 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 237p. |
| 365 ## - TRADE PRICE | |
| Price type code | INR |
| Price amount | 550.00 |
| 500 ## - GENERAL NOTE | |
| General note | <br/>This easy-to-use reference for Tensorflow 2 pattern designs in Python will help you make informed decisions for various use cases. Author KC Tung addresses common topics and tasks in enterprise data science and machine learning practices rather than focusing on TensorFlow. When and why would you feed training data as NumPy or a streaming dataset? How would you set up cross-validations in the training process? How do you leverage a pre-trained model using transfer learning? How do you perform hyperparameter tuning? Pick up this pocket reference and reduce your time searching through options for your TensorFlow use cases. Understand best practices in Tensorflow model patterns and ML workflows. Use code snippets as templates in building TensorFlow models and workflows. Save development time by integrating pre-built models in TensorFlow Hub. Make informed design choices about data ingestion, training paradigms, model saving, and inferencing. Address common scenarios such as model design style, data ingestion workflow, model training, and tuning. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Machine learning |
| 952 ## - LOCATION AND ITEM INFORMATION (KOHA) | |
| Withdrawn status | |
| Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Home library | Current library | Shelving location | Date acquired | Cost, normal purchase price | Total Checkouts | Full call number | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | 003-007 | BITS Pilani Hyderabad | BITS Pilani Hyderabad | General Stack (For lending) | 05/08/2022 | 550.00 | 006.31 TUN-K | 46260 | 13/11/2024 | 05/08/2022 | Books |