Building machine learning projects with TensorFlow : (Record no. 76311)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03330nam a2200277 4500 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9781786466587 |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 |
| Item number | BON/B |
| 100 1# - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Bonnin, Rodolfo |
| 245 10 - TITLE STATEMENT | |
| Title | Building machine learning projects with TensorFlow : |
| Remainder of title | engaging projects that will teach you how complex data can be exploited to gain the most insight / |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication | Birmingham |
| Name of publisher | Packt |
| Year of publication | 2016 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | 271 p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Engaging projects that will teach you how complex data can be exploited to gain the most insight About This Book Bored of too much theory on TensorFlow? This book is what you need! Thirteen solid projects and four examples teach you how to implement TensorFlow in production. This example-rich guide teaches you how to perform highly accurate and efficient numerical computing with TensorFlow It is a practical and methodically explained guide that allows you to apply Tensorflow's features from the very beginning. Who This Book Is For This book is for data analysts, data scientists, and researchers who want to increase the speed and efficiency of their machine learning activities and results. Anyone looking for a fresh guide to complex numerical computations with TensorFlow will find this an extremely helpful resource. This book is also for developers who want to implement TensorFlow in production in various scenarios. Some experience with C++ and Python is expected. What You Will Learn Load, interact, dissect, process, and save complex datasets Solve classification and regression problems using state of the art techniques Predict the outcome of a simple time series using Linear Regression modeling Use a Logistic Regression scheme to predict the future result of a time series Classify images using deep neural network schemes Tag a set of images and detect features using a deep neural network, including a Convolutional Neural Network (CNN) layer Resolve character recognition problems using the Recurrent Neural Network (RNN) model In Detail This book of projects highlights how TensorFlow can be used in different scenarios - this includes projects for training models, machine learning, deep learning, and working with various neural networks. Each project provides exciting and insightful exercises that will teach you how to use TensorFlow and show you how layers of data can be explored by working with Tensors. Simply pick a project that is in line with your environment and get stacks of information on how to implement TensorFlow in production. Style and approach This book is a practical guide to implementing TensorFlow in production. It explores various scenarios in which you could use TensorFlow and shows you how to use it in the context of real world projects. This will not only give you an upper hand in the field, but shows the potential for innovative uses of TensorFlow in your environment. This guide opens the door to second generation machine le... |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Machine learning. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Artificial intelligence. |
| 650 #2 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Artificial Intelligence |
| 650 #6 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Apprentissage automatique. |
| 650 #6 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Intelligence artificielle. |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | artificial intelligence. |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | COMPUTERS |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Artificial intelligence |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Machine learning. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | BK |
| 001 - CONTROL NUMBER | |
| control field | ocn966316351 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS--GENERAL INFORMATION | |
| fixed length control field | m o d |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr unu|||||||| |
| 952 ## - LOCATION AND ITEM INFORMATION (KOHA) | |
| Withdrawn status | |
| Lost status | |
| Damaged status | Collection code | Home library | Current library | Shelving location | Date acquired | Cost, normal purchase price | Full call number | Accession Number | Koha item type |
|---|---|---|---|---|---|---|---|---|---|
| Stack | Kannur University Central Library | Kannur University Central Library | Stack | 12/07/2024 | 799.00 | 006.31 BON/B | 68260 | BK |