TensorFlow Machine Learning Cookbook : Over 60 Recipes to Build Intelligent Machine Learning Systems with the Power of Python, 2nd Edition.
This book will help you overcome any problem you might come across while training and deploying machine learning models using the recently released Tensorflow. This book includes recipes on important machine learning concepts such as supervised and unsupervised learning, as well as neural networks a...
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Full text (MCPHS users only) |
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| Main Author: | |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Birmingham :
Packt Publishing Ltd,
2018
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| Edition: | 2nd ed. |
| Subjects: | |
| Local Note: | ProQuest Ebook Central |
Table of Contents:
- Cover; Title Page; Copyright and Credits; Dedication; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Getting Started with TensorFlow; Introduction; How TensorFlow works; Getting ready; How to do it ... ; How it works ... ; See also; Declaring variables and tensors; Getting ready; How to do it ... ; How it works ... ; There's more ... ; Using placeholders and variables; Getting ready; How to do it ... ; How it works ... ; There's more ... ; Working with matrices; Getting ready; How to do it ... ; How it works ... ; Declaring operations; Getting ready; How to do it ... ; How it works ...
- There's more ... Implementing activation functions; Getting ready; How to do it ... ; How it works ... ; There's more ... ; Working with data sources; Getting ready; How to do it ... ; How it works ... ; See also; Additional resources; Getting ready; How to do it ... ; Chapter 2: The TensorFlow Way; Introduction; Operations in a computational graph; Getting ready; How to do it ... ; How it works ... ; Layering nested operations; Getting ready; How to do it ... ; How it works ... ; There's more ... ; Working with multiple layers; Getting ready; How to do it ... ; How it works ... ; Implementing loss functions.
- Getting readyHow to do it ... ; How it works ... ; There's more ... ; Implementing backpropagation; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Working with batch and stochastic training; Getting ready; How to do it ... ; How it works ... ; There's more ... ; Combining everything together; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Evaluating models; Getting ready; How to do it ... ; How it works ... ; Chapter 3: Linear Regression; Introduction; Using the matrix inverse method; Getting ready; How to do it ... ; How it works ...
- Implementing a decomposition methodGetting ready; How to do it ... ; How it works ... ; Learning the TensorFlow way of linear regression; Getting ready; How to do it ... ; How it works ... ; Understanding loss functions in linear regression; Getting ready; How to do it ... ; How it works ... ; There's more ... ; Implementing deming regression; Getting ready; How to do it ... ; How it works ... ; Implementing lasso and ridge regression; Getting ready; How to do it ... ; How it works ... ; There's more ... ; Implementing elastic net regression; Getting ready; How to do it ... ; How it works ...
- Implementing logistic regressionGetting ready; How to do it ... ; How it works ... ; Chapter 4: Support Vector Machines; Introduction; Working with a linear SVM; Getting ready; How to do it ... ; How it works ... ; Reduction to linear regression; Getting ready; How to do it ... ; How it works ... ; Working with kernels in TensorFlow; Getting ready; How to do it ... ; How it works ... ; There's more ... ; Implementing a non-linear SVM; Getting ready; How to do it ... ; How it works ... ; Implementing a multi-class SVM; Getting ready; How to do it ... ; How it works ... ; Chapter 5: Nearest-Neighbor Methods.
