Book Image

TensorFlow for Machine Learning Solutions [Video]

By : Nick McClure
Book Image

TensorFlow for Machine Learning Solutions [Video]

By: Nick McClure

Overview of this book

<p>TensorFlow is an open source software library for Machine Intelligence. The independent solutions in this video course will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You’ll work through solutions on training models, model evaluation and sentiment analysis – each using Google’s machine learning library TensorFlow.This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow</p> <h1>Style and Approach</h1> <p>This video course takes a solution-based approach where every topic is explicated with the help of a real-world example.</p>

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TensorFlow for Machine Learning Solutions [Video]
Nick McClure
Feb, 2018 | 1 hour 37 minutes
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Table of Contents (5 chapters)
Free Chapter
1
Getting Started with TensorFlow
The Course Overview
How TensorFlow Works?
Declaring Tensors
Using Placeholders
Working with Matrices
Declaring Operations
Implementing Activation Functions
Working with Data Sources
2
The TensorFlow Way
Operations in a Computational Graph
Layering Nested Operations
Working with Multiple Layers
Implementing Loss Functions
Implementing Back Propagation
Working with Batch and Stochastic Training
Combining Everything Together
Evaluating Models
3
Linear Regression
Using the Matrix Inverse Method
Implementing a Decomposition Method
Learning the TensorFlow Way of Linear Regression
Understanding Loss Functions in Linear Regression
Implementing Deming regression
Implementing Lasso and Ridge Regression
Implementing Elastic Net Regression
4
Support Vector Machines
Working with a Linear SVM
Reduction to Linear Regression
Working with Kernels in TensorFlow
Implementing a Non-Linear SVM
Implementing a Multi-Class SVM
5
Nearest Neighbor Methods
Working with Nearest Neighbors
Working with Text-Based Distances
Computing with Mixed Distance Functions
Using an Address Matching Example
Using Nearest Neighbors for Image Recognition
Chapter 4
Support Vector Machines
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Section 1
Working with a Linear SVM
For this example, we will create a linear separator from the iris data set. We know from prior chapters that the sepal length and petal width create a linear separable binary data set for predicting if a flower is I. setosa or not. - Create a linear separator from the iris data set - Implement it in tensor flow using the specific loss function
Previous Section
End of Section 1
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