Do you want to learn how to write your own codes and programming and get your computer set up to learn just like humans do? Do you want to learn how to write out codes in deep learning-without having to spend years going to school to learn to code and how all this works? Do you know a bit of Python coding and want to learn more about how this deep learning works?
This guidebook is the tool that you need to not only learn how to do machine learning but also learn how to take this even further and write some of your own codes in deep learning. The field of deep learning is pretty new, and many programmers have not been able to delve into the depths of what we can see with this type of programming-but with the growing market for products and technology that can act and learn just like the human brain, this field is definitely taking off
This book will take some time to explore the different Python libraries that will help you to do some deep learning algorithms in no time. Investing your time in the Python language and learning the different libraries that are needed to turn this basic programming language into a deep learning machine can be one of the best decisions for you.
By learning some of the tips in this book, you will be able to save time and resources when it comes to your deep learning needs. Rather than spending time with other, more difficult programming languages, or having to go take complicated classes to learn how to do these algorithms, we will explore exactly how to do all of the tasks that you need with this type of machine learning.
You will learn:
1. What deep learning is, how it is different from machine learning, and why Python is such a beneficial language to use with the deep learning algorithms;
2. The basics of the three main Python languages that will help you get the work done-including TensorFlow, Keras, and PyTorch;
3. How to install the three Python libraries to help you get started;
4. A closer look at neural networks, what they are, why they are important, and some of the mathematics of making them work;
5. The basics you need to know about TensorFlow and some of the deep learning you can do with this library;
6. The basics of the Keras library and some of the deep learning you can do with this library;
7. A look at the PyTorch library, how it is different from the other two, and the basics of deep learning with this library;
8. And so much more
Even if you are just a beginner, with very little programming knowledge but lots of big dreams and even bigger ideas, this book is going to give you the tools that you need to start with deep learning
Автор: Koul Anirudh, Ganju Siddha, Kasam Meher Название: Practical Deep Learning for Cloud and Mobile: Hands-On Computer Vision Projects Using Python, Keras & Tensorflow ISBN: 149203486X ISBN-13(EAN): 9781492034865 Издательство: Wiley Рейтинг: Цена: 76020.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This step-by-step guide teaches you how to build practical deep learning applications for the cloud and mobile using a hands-on approach.
Автор: Samuel Burns Название: Python Deep Learning: Develop Your First Neural Network in Python Using Tensorflow, Keras, and Pytorch ISBN: 1092562222 ISBN-13(EAN): 9781092562225 Издательство: Неизвестно Цена: 17230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Build your Own Neural Network today. Through easy-to-follow instruction and examples, you'll learn the fundamentals of Deep learning and build your very own Neural Network in Python using TensorFlow, Keras, PyTorch, and Theano. While you have the option of spending thousands of dollars on big and boring textbooks, we recommend getting the same pieces of information for a fraction of the cost. So Get Your Copy Now Why this book?Book ObjectivesThe following are the objectives of this book:
To help you understand deep learning in detail
To help you know how to get started with deep learning in Python by setting up the coding environment.
To help you transition from a deep learning Beginner to a Professional.
To help you learn how to develop a complete and functional artificial neural network model in Python on your own.
Who this Book is for? The author targets the following groups of people:
Anybody who is a complete beginner to deep learning with Python.
Anybody in need of advancing their Python for deep learning skills.
Professors, lecturers or tutors who are looking to find better ways to explain Deep Learning to their students in the simplest and easiest way.
Students and academicians, especially those focusing on python programming, neural networks, machine learning, and deep learning.
What do you need for this Book? You are required to have installed the following on your computer:
Python 3.X.
TensorFlow .
Keras .
PyTorch
The Author guides you on how to install the rest of the Python libraries that are required for deep learning.The author will guide you on how to install and configure the rest. What is inside the book?
What is Deep Learning?
An Overview of Artificial Neural Networks.
Exploring the Libraries.
Installation and Setup.
TensorFlow Basics.
Deep Learning with TensorFlow.
Keras Basics.
PyTorch Basics.
Creating Convolutional Neural Networks with PyTorch.
Creating Recurrent Neural Networks with PyTorch.
From the back cover.Deep learning is part of machine learning methods based on learning data representations. This book written by Samuel Burns provides an excellent introduction to deep learning methods for computer vision applications. The author does not focus on too much math since this guide is designed for developers who are beginners in the field of deep learning. The book has been grouped into chapters, with each chapter exploring a different feature of the deep learning libraries that can be used in Python programming language. Each chapter features a unique Neural Network architecture including Convolutional Neural Networks. After reading this book, you will be able to build your own Neural Networks using Tenserflow, Keras, and PyTorch. Moreover, the author has provided Python codes, each code performing a different task. Corresponding explanations have also been provided alongside each piece of code to help the reader understand the meaning of the various lines of the code. In addition to this, screenshots showing the output that each code should return have been given. The author has used a simple language to make it easy even for beginners to understand.
Автор: Moocarme Matthew, Abdolahnejad Mahla, Bhagwat Ritesh Название: The Deep Learning with Keras Workshop: Learn how to define and train neural network models with just a few lines of code ISBN: 1800562969 ISBN-13(EAN): 9781800562967 Издательство: Неизвестно Рейтинг: Цена: 39410.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The Deep Learning with Keras Workshop outlines a simple and straightforward way for you to understand deep learning with Keras. Starting with basic concepts such as data preprocessing, this book equips you with all the tools and techniques required for training your neural networks to solve various modeling problems.
Автор: Moolayil Jojo Название: Learn Keras for Deep Neural Networks: A Fast-Track Approach to Modern Deep Learning with Python ISBN: 1484242394 ISBN-13(EAN): 9781484242391 Издательство: Springer Рейтинг: Цена: 41920.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras.The overall book comprises three sections with two chapters in each section. The first section prepares you with all the necessary basics to get started in deep learning. Chapter 1 introduces you to the world of deep learning and its difference from machine learning, the choices of frameworks for deep learning, and the Keras ecosystem. You will cover a real-life business problem that can be solved by supervised learning algorithms with deep neural networks. You’ll tackle one use case for regression and another for classification leveraging popular Kaggle datasets. Later, you will see an interesting and challenging part of deep learning: hyperparameter tuning; helping you further improve your models when building robust deep learning applications. Finally, you’ll further hone your skills in deep learning and cover areas of active development and research in deep learning. At the end of Learn Keras for Deep Neural Networks, you will have a thorough understanding of deep learning principles and have practical hands-on experience in developing enterprise-grade deep learning solutions in Keras.What You’ll Learn Master fast-paced practical deep learning concepts with math- and programming-friendly abstractions. Design, develop, train, validate, and deploy deep neural networks using the Keras framework Use best practices for debugging and validating deep learning models Deploy and integrate deep learning as a service into a larger software service or product Extend deep learning principles into other popular frameworks Who This Book Is For Software engineers and data engineers with basic programming skills in any language and who are keen on exploring deep learning for a career move or an enterprise project.
Do you want to learn how to write your own codes and programming and get your computer set up to learn just like humans do? Do you want to learn how to write out codes in deep learning-without having to spend years going to school to learn to code and how all this works? Do you know a bit of Python coding and want to learn more about how this deep learning works?
This guidebook is the tool that you need to not only learn how to do machine learning but also learn how to take this even further and write some of your own codes in deep learning. The field of deep learning is pretty new, and many programmers have not been able to delve into the depths of what we can see with this type of programming-but with the growing market for products and technology that can act and learn just like the human brain, this field is definitely taking off
This book will take some time to explore the different Python libraries that will help you to do some deep learning algorithms in no time. Investing your time in the Python language and learning the different libraries that are needed to turn this basic programming language into a deep learning machine can be one of the best decisions for you.
By learning some of the tips in this book, you will be able to save time and resources when it comes to your deep learning needs. Rather than spending time with other, more difficult programming languages, or having to go take complicated classes to learn how to do these algorithms, we will explore exactly how to do all of the tasks that you need with this type of machine learning.
You will learn:
1. What deep learning is, how it is different from machine learning, and why Python is such a beneficial language to use with the deep learning algorithms;
2. The basics of the three main Python languages that will help you get the work done-including TensorFlow, Keras, and PyTorch;
3. How to install the three Python libraries to help you get started;
4. A closer look at neural networks, what they are, why they are important, and some of the mathematics of making them work;
5. The basics you need to know about TensorFlow and some of the deep learning you can do with this library;
6. The basics of the Keras library and some of the deep learning you can do with this library;
7. A look at the PyTorch library, how it is different from the other two, and the basics of deep learning with this library;
8. And so much more
Even if you are just a beginner, with very little programming knowledge but lots of big dreams and even bigger ideas, this book is going to give you the tools that you need to start with deep learning
Do you want to learn how to write your own codes and programming and get your computer set up to learn just like humans do? Do you want to learn how to write out codes in deep learning-without having to spend years going to school to learn to code and how all this works? Do you know a bit of Python coding and want to learn more about how this deep learning works?
This guidebook is the tool that you need to not only learn how to do machine learning but also learn how to take this even further and write some of your own codes in deep learning. The field of deep learning is pretty new, and many programmers have not been able to delve into the depths of what we can see with this type of programming-but with the growing market for products and technology that can act and learn just like the human brain, this field is definitely taking off
This book will take some time to explore the different Python libraries that will help you to do some deep learning algorithms in no time. Investing your time in the Python language and learning the different libraries that are needed to turn this basic programming language into a deep learning machine can be one of the best decisions for you.
By learning some of the tips in this book, you will be able to save time and resources when it comes to your deep learning needs. Rather than spending time with other, more difficult programming languages, or having to go take complicated classes to learn how to do these algorithms, we will explore exactly how to do all of the tasks that you need with this type of machine learning.
You will learn:
1. What deep learning is, how it is different from machine learning, and why Python is such a beneficial language to use with the deep learning algorithms;
2. The basics of the three main Python languages that will help you get the work done-including TensorFlow, Keras, and PyTorch;
3. How to install the three Python libraries to help you get started;
4. A closer look at neural networks, what they are, why they are important, and some of the mathematics of making them work;
5. The basics you need to know about TensorFlow and some of the deep learning you can do with this library;
6. The basics of the Keras library and some of the deep learning you can do with this library;
7. A look at the PyTorch library, how it is different from the other two, and the basics of deep learning with this library;
8. And so much more
Even if you are just a beginner, with very little programming knowledge but lots of big dreams and even bigger ideas, this book is going to give you the tools that you need to start with deep learning
Do you want to learn how to write your own codes and programming and get your computer set up to learn just like humans do? Do you want to learn how to write out codes in deep learning-without having to spend years going to school to learn to code and how all this works? Do you know a bit of Python coding and want to learn more about how this deep learning works?
This guidebook is the tool that you need to not only learn how to do machine learning but also learn how to take this even further and write some of your own codes in deep learning. The field of deep learning is pretty new, and many programmers have not been able to delve into the depths of what we can see with this type of programming-but with the growing market for products and technology that can act and learn just like the human brain, this field is definitely taking off
This book will take some time to explore the different Python libraries that will help you to do some deep learning algorithms in no time. Investing your time in the Python language and learning the different libraries that are needed to turn this basic programming language into a deep learning machine can be one of the best decisions for you.
By learning some of the tips in this book, you will be able to save time and resources when it comes to your deep learning needs. Rather than spending time with other, more difficult programming languages, or having to go take complicated classes to learn how to do these algorithms, we will explore exactly how to do all of the tasks that you need with this type of machine learning.
You will learn:
1. What deep learning is, how it is different from machine learning, and why Python is such a beneficial language to use with the deep learning algorithms;
2. The basics of the three main Python languages that will help you get the work done-including TensorFlow, Keras, and PyTorch;
3. How to install the three Python libraries to help you get started;
4. A closer look at neural networks, what they are, why they are important, and some of the mathematics of making them work;
5. The basics you need to know about TensorFlow and some of the deep learning you can do with this library;
6. The basics of the Keras library and some of the deep learning you can do with this library;
7. A look at the PyTorch library, how it is different from the other two, and the basics of deep learning with this library;
8. And so much more
Even if you are just a beginner, with very little programming knowledge but lots of big dreams and even bigger ideas, this book is going to give you the tools that you need to start with deep learning
Build a Keras model to scale and deploy on a Kubernetes cluster
We have seen an exponential growth in the use of Artificial Intelligence (AI) over last few years. AI is becoming the new electricity and is touching every industry from retail to manufacturing to healthcare to entertainment. Within AI, we re seeing a particular growth in Machine Learning (ML) and Deep Learning (DL) applications. ML is all about learning relationships from labeled (Supervised) or unlabeled data (Unsupervised). DL has many layers of learning and can extract patterns from unstructured data like images, video, audio, etc.
Keras to Kubernetes: The Journey of a Machine Learning Model to Production takes you through real-world examples of building DL models in Keras for recognizing product logos in images and extracting sentiment from text. You will then take that trained model and package it as a web application container before learning how to deploy this model at scale on a Kubernetes cluster. You will understand the different practical steps involved in real-world ML implementations which go beyond the algorithms.
- Find hands-on learning examples
- Learn to uses Keras and Kubernetes to deploy Machine Learning models
- Discover new ways to collect and manage your image and text data with Machine Learning
- Reuse examples as-is to deploy your models
- Understand the ML model development lifecycle and deployment to production
If you re ready to learn about one of the most popular DL frameworks and build production applications with it, you ve come to the right place
Автор: Publishing Ai Название: Data Science from Scratch with Python: Concepts and Practices with NumPy, Pandas, Matplotlib, Scikit-Learn and Keras (2nd Section) ISBN: 1733042695 ISBN-13(EAN): 9781733042697 Издательство: Неизвестно Цена: 16080.00 T Наличие на складе: Невозможна поставка. Описание: Data Science from scratch for Beginners with Hands-On Projects Are you looking for a hands-on approach to learn Data Science fast? Do you need to start learning Machine Learning Fundamentals? This book is for you. This book is written for beginners and novices who want to develop fundamental data science skills and learn how to build models that learn useful information from data. This book will prepare the learner for a career or further learning that involves more advanced topics. It contains introduction and very basic concepts used in data science. The learner is not required to have any prior knowledge but some basic knowledge of mathematics is required. The working of each algorithm is traced back to its origin in probability, statistics or linear algebra which helps learner to understand the topics better. The concepts of probability and statistics are defined and explained at rudimentary level to make things simple and easy to comprehend. For intuitive understanding, algorithms have been explained through proper visualizations and various examples. While we will focus more on the techniques normally used in Data Science, we will also explain, in-details, all the Python libraries used in any data science project. What this book offers... You will learn all about data Science starting from Python Coding, Data Manipulation and Preprocessing, Data Visualization then data modeling using Python. All the modules will contain hands-on projects using real-world datasets. Clear and Easy to Understand Solutions All solutions in this book are extensively tested by a group of beta readers. The solutions provided are simplified as much as possible so that they can serve as examples for you to refer to when you are learning a new skill. What this book aims to do... This book is written with one goal in mind - to help beginners overcome their initial obstacles to learn Data Science from Scratch. A lot of times, newbies tend to feel intimidated by Data Science Models and Coding. The goal of this book is to isolate the different concepts so that beginners can gradually gain competency in the fundamentals and keys concepts before working on a project at the end of each chapter. Beginners in Data Science does not have to be scary or frustrating when you take one step at a time. Ready to start practicing and start learning Data Science from Scratch? Click the BUY button now to download this book Topics Covered:
Preliminary to Understand Data Science
Overview of Python and Data Processing
Statistics and Probability
Supervised Learning Techniques
Unsupervised Learning Techniques
Neural Networks and Deep Learning
Reinforcement Learning Techniques
..and more...
Click the BUY button and download the book now to start learning Data Science. ** MONEY BACK GUARANTEE BY AMAZON ** If you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform or contact us by sending an email at contact@aispublishing.net. **GET YOUR COPY NOW, the price will be 22.99$ soon**
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