**TensorFlow for Machine Learning Solutions**

English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 1h 37m | 453 MB

Explore machine learning concepts using the latest numerical computing library - TensorFlow

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

This video course takes a solution-based approach where every topic is explicated with the help of a real-world example.

What You Will Learn

- Become familiar with the basics of the TensorFlow machine learning library
- Get to know Linear Regression techniques with TensorFlow
- Learn SVMs with hands-on solutions
- Implementing Nearest Neighbor Methods

**Table of Contents**

01 The Course Overview

02 How TensorFlow Works

03 Declaring Tensors

04 Using Placeholders

05 Working with Matrices

06 Declaring Operations

07 Implementing Activation Functions

08 Working with Data Sources

09 Operations in a Computational Graph

10 Layering Nested Operations

11 Working with Multiple Layers

12 Implementing Loss Functions

13 Implementing Back Propagation

14 Working with Batch and Stochastic Training

15 Combining Everything Together

16 Evaluating Models

17 Using the Matrix Inverse Method

18 Implementing a Decomposition Method

19 Learning the TensorFlow Way of Linear Regression

20 Understanding Loss Functions in Linear Regression

21 Implementing Deming regression

22 Implementing Lasso and Ridge Regression

23 Implementing Elastic Net Regression

24 Working with a Linear SVM

25 Reduction to Linear Regression

26 Working with Kernels in TensorFlow

27 Implementing a Non-Linear SVM

28 Implementing a Multi-Class SVM

29 Working with Nearest Neighbors

30 Working with Text-Based Distances

31 Computing with Mixed Distance Functions

32 Using an Address Matching Example

33 Using Nearest Neighbors for Image Recognition

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