Machine Learning with Tensorflow: A Deeper Look at Machine Learning with TensorFlow

By Frank Millstein

WHAT IS IN THE BOOK?

INTRODUCTION

WHY MACHINE LEARNING?

MACHINE LEARNING APPROACH

USES OF MACHINE LEARNING

MACHINE LEARNING, DEEP LEARNING AND DATA MINING

MACHINE LEARNING METHODS

THE BENEFITS OF MACHINE LEARNING

CHAPTER 1: GETTING STARTED WITH TENSORFLOW

INSTALLING TENSORFLOW

DATA FLOW GRAPHS

SIMPLE TENSORFLOW EXPRESSIONS

DEFINING COMPUTATIONAL GRAPHS

VISUALIZING THE COMPUTATIONAL GRAPHS USING TENSORBOARD

CHAPTER 2: BUILDING TENSORS IN TENSORFLOW

TENSOR OPERATIONS

MATRIX OPERATIONS

DATA TRANSFORMATION

DATA SEGMENTATION

SEQUENCE UTILITY METHODS

CHAPTER 3: ALGORITHMS FOR CLASSIFYING DATA

K-NEAREST NEIGHBORS

LINEAR REGRESSION

CHAPTER 4: BUILDING HIGH PERFORMANCE DATA PIPELINES

TENSORFLOW DATASET FRAMEWORK

CREATING ITERATORS

SETTING TENSORFLOW OPERATIONS

DATASET MANIPULATION

ZIPPING DATASETS

CHAPTER 5: CREATING MNIST CLASSIFIERS

LOADING DATA

ONE-HOT TRANSFORMATION

EXTRACTING DATA

LOSS FUNCTION AND OPTIMIZATION

PREDICTION ACCURACY

MODEL TRAINING

LAST WORDS

Copyright © 2018 by Frank Millstein- All rights reserved.

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INTRODUCTION

Machine learning arguably is the basis for some of the most exciting data analytics careers out there today. It is no wonder why you are interested in learning more about this dynamic topic. The main benefit of machine learning techniques is that the models that are created able to make countless predictions of many real-world problems. They can utilize huge datasets on their own without the need to be explicitly programmed by humans.

Data scientists need to have several skills such as knowing the basic tools such as those of Python that we are going to cover here in the book. You must learn how to work with difficult or messy data. Fortunately, Python will help you deal with data munging. You must also learn about data visualization and, of course, learn how to perform hands-on machine learning techniques and algorithms that will help you sort out and organization massive amounts of datasets into workable formats.

Machine learning has great functionality which helps software, or a machine perform different tasks without requiring human intercession for programming. Machine learning is considered a field of artificial intelligence that involves statistical techniques such as deep learning or neural networks. Machine learning is mainly powered by different algorithmic models, which are trained to recognize different patterns obtained in the data such as images, text, speech or logs.

Access to the great amounts of data present and computing powers are the most important preconditions for success. Machine learning has risen in the past several years becoming one of the main data analytics tools.

Examples of machine learning techniques and algorithms abound in many everyday experiences. One of the basic examples of machine learning is the auto-completition of addresses, keywords or names in search fields. This same concept can be easily applied to more complex cases as well. For instance, across different industries, machine learning is commonly used to look for different keywords in bulk numbers of emails or text documents.

Moreover, machine learning techniques are used