David Paper - State-of-the-Art Deep Learning Models in TensorFlow: Modern Machine Learning in the Google Colab Ecosystem
Here you can read online David Paper - State-of-the-Art Deep Learning Models in TensorFlow: Modern Machine Learning in the Google Colab Ecosystem full text of the book (entire story) in english for free. Download pdf and epub, get meaning, cover and reviews about this ebook. year: 2021, publisher: Apress, genre: Computer / Science. Description of the work, (preface) as well as reviews are available. Best literature library LitArk.com created for fans of good reading and offers a wide selection of genres:
Romance novel
Science fiction
Adventure
Detective
Science
History
Home and family
Prose
Art
Politics
Computer
Non-fiction
Religion
Business
Children
Humor
Choose a favorite category and find really read worthwhile books. Enjoy immersion in the world of imagination, feel the emotions of the characters or learn something new for yourself, make an fascinating discovery.
- Book:State-of-the-Art Deep Learning Models in TensorFlow: Modern Machine Learning in the Google Colab Ecosystem
- Author:
- Publisher:Apress
- Genre:
- Year:2021
- Rating:4 / 5
- Favourites:Add to favourites
- Your mark:
State-of-the-Art Deep Learning Models in TensorFlow: Modern Machine Learning in the Google Colab Ecosystem: summary, description and annotation
We offer to read an annotation, description, summary or preface (depends on what the author of the book "State-of-the-Art Deep Learning Models in TensorFlow: Modern Machine Learning in the Google Colab Ecosystem" wrote himself). If you haven't found the necessary information about the book — write in the comments, we will try to find it.
Use TensorFlow 2.x in the Google Colab ecosystem to create state-of-the-art deep learning models guided by hands-on examples. The Colab ecosystem provides a free cloud service with easy access to on-demand GPU (and TPU) hardware acceleration for fast execution of the models you learn to build. This book teaches you state-of-the-art deep learning models in an applied manner with the only requirement being an Internet connection. The Colab ecosystem provides everything else that you need, including Python, TensorFlow 2.x, GPU and TPU support, and Jupyter Notebooks.
The book begins with an example-driven approach to building input pipelines that feed all machine learning models. You will learn how to provision a workspace on the Colab ecosystem to enable construction of effective input pipelines in a step-by-step manner. From there, you will progress into data augmentation techniques and TensorFlow datasets to gain a deeper understanding of how to work with complex datasets. You will find coverage of Tensor Processing Units (TPUs) and transfer learning followed by state-of-the-art deep learning models, including autoencoders, generative adversarial networks, fast style transfer, object detection, and reinforcement learning.
Author Dr. Paper provides all the applied math, programming, and concepts you need to master the content. Examples range from relatively simple to very complex when necessary. Examples are carefully explained, concise, accurate, and complete. Care is taken to walk you through each topic through clear examples written in Python that you can try out and experiment with in the Google Colab ecosystem in the comfort of your own home or office.
- Take advantage of the built-in support of the Google Colab ecosystem
- Work with TensorFlow data sets
- Create input pipelines to feed state-of-the-art deep learning models
- Create pipelined state-of-the-art deep learning models with clean and reliable Python code
- Leverage pre-trained deep learning models to solve complex machine learning tasks
- Create a simple environment to teach an intelligent agent to make automated decisions
Who This Book Is For
Readers who want to learn the highly popular TensorFlow deep learning platform, those who wish to master the basics of state-of-the-art deep learning models, and those looking to build competency with a modern cloud service tool such as Google Colab
David Paper: author's other books
Who wrote State-of-the-Art Deep Learning Models in TensorFlow: Modern Machine Learning in the Google Colab Ecosystem? Find out the surname, the name of the author of the book and a list of all author's works by series.
State-of-the-Art Deep Learning Models in TensorFlow: Modern Machine Learning in the Google Colab Ecosystem — read online for free the complete book (whole text) full work
Below is the text of the book, divided by pages. System saving the place of the last page read, allows you to conveniently read the book "State-of-the-Art Deep Learning Models in TensorFlow: Modern Machine Learning in the Google Colab Ecosystem" online for free, without having to search again every time where you left off. Put a bookmark, and you can go to the page where you finished reading at any time.
Font size:
Interval:
Bookmark:
This Apress imprint is published by the registered company APress Media, LLC part of Springer Nature.
The registered company address is: 1 New York Plaza, New York, NY 10004, U.S.A.
I dedicate my book to my Mom and younger brother Bruce. Both have been instrumental in my development as an author and a person.
We apply the TensorFlow end-to-end open source platform within the Google Colaboratory (Colab) ecosystem to demonstrate state-of-the-art deep neural network models with hands-on Python code exercises for intermediate to advanced Python users. The Colab ecosystem is a product from Google Research that allows anybody to write and execute arbitrary Python code through a browser. The ecosystem is especially suited to deep learning, data analytics, research, and machine learning education applications. The Colab ecosystem is a hosted Jupyter notebook service that requires no setup to use while providing free access to powerful computing resources such as Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs).
The book is organized into 14 chapters. Chapter ends the book with a very simple reinforcement learning experiment.
Dr. David Paper has competency in several programming languages, but his focus is currently on deep learning with Python in the TensorFlow-Colab ecosystem. He has published extensively on machine learning (ML) including such books as Data Science Fundamentals for Python and MongoDB (2018, Apress), Hands-on Scikit-Learn for Machine Learning Applications: Data Science Fundamentals with Python (2019, Apress), and TensorFlow 2.x in the Colaboratory Cloud: An Introduction to Deep Learning on Googles Cloud Service (2021, Apress). He has also published more than 100 academic articles.
Besides growing up in family businesses, Dr. Paper has worked for Texas Instruments, DLS Inc., and the Phoenix Small Business Administration. He has performed information systems (IS) consulting work for IBM, AT&T, Octel, the Utah Department of Transportation, and the Space Dynamics Laboratory. He has worked on research projects with several corporations, including Caterpillar, Fannie Mae, Comdisco, IBM, Raychem, Ralston Purina, and Monsanto. He maintains contacts in corporations such as Google, Micron, Oracle, and Goldman Sachs.
We introduce you to TensorFlow input pipelines with the tf.data API, which enables you to build complex input pipelines from simple, reusable pieces. Input pipelines are the lifeblood of any deep learning experiment because learning models expect data in a TensorFlow consumable form. It is very easy to create high-performance pipelines with the tf.data.Dataset abstraction (a component of the tf.data API) because it represents a sequence of elements from a dataset in a simple format.
Although data cleaning is a critical component of input pipelining, we focus on building pipelines with cleansed data. We want to focus you on building TensorFlow consumable pipelines rather than data cleansing. A data scientist can spend upwards of 80% of the total machine learning (ML) projects time on just cleaning the data.
We build input pipelines from three data sources. The first data source is from data loaded into memory. The second one is from external files. The final one is from cloud storage.
Notebooks for chapters are located at the following URL:
https://github.com/paperd/deep-learning-models
A machine learning (ML)input pipeline is an approach to codify and automate the workflows required to produce a machine learning model. ML workflows are the phases that are implemented during a ML project. Typical phases include data collection, data preprocessing, building datasets, model training and refinement, evaluation, and deployment to production. So the goal of an input pipeline is to automate the workflows (or phases) associated with ML problem solving. Once an input pipeline is automated, it can be reused as new data is added to a ML project. It can even be tweaked for use with similar ML projects.
The first step in any input pipeline is data preprocessing. In this step, raw data is gathered, cleansed, and merged into a single organized framework. Data cleaning is the process of identifying and fixing any issues with a dataset. The objective of data cleaning is to fix any data that is incorrect, inaccurate, incomplete, incorrectly formatted, duplicated, or irrelevant to the purpose of the ML project so that the cleansed dataset is correct, consistent, reliable, and usable.
Font size:
Interval:
Bookmark:
Similar books «State-of-the-Art Deep Learning Models in TensorFlow: Modern Machine Learning in the Google Colab Ecosystem»
Look at similar books to State-of-the-Art Deep Learning Models in TensorFlow: Modern Machine Learning in the Google Colab Ecosystem. We have selected literature similar in name and meaning in the hope of providing readers with more options to find new, interesting, not yet read works.
Discussion, reviews of the book State-of-the-Art Deep Learning Models in TensorFlow: Modern Machine Learning in the Google Colab Ecosystem and just readers' own opinions. Leave your comments, write what you think about the work, its meaning or the main characters. Specify what exactly you liked and what you didn't like, and why you think so.