Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Profound Python Libraries
Download Profound Python Libraries full books in PDF, epub, and Kindle. Read online Profound Python Libraries ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis Profound Python Libraries by : Önder Teker
Download or read book Profound Python Libraries written by Önder Teker and published by Godoro. This book was released on 2022-07-08 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book contains Python libraries used in many applications. Internet, Downloads, JSON, REST are covered. Utilities such as time, random, regular expressions are included. The operating systems & process are explained in detail. File system operations and Pathlib are covered. Some introductions to Big Data & Artificial Intelligence are added. CSV, Samples are explained as a preperation for data science. Visual libraries such as PIL & Matplotlib are included. Speech Recognition is covered. Finally Tk is is explained & a full sample application is supplied.
Download or read book Profound Python written by Önder Teker and published by Godoro. This book was released on 2021-08-07 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book starts the Python language from the basics and then intermediate and advanced topics are covered. After functional programming is explained in detail, object-oriented programming features such as classes, inheritance, abstract classes, polymorphism are described. Data structures and collections are given for both fundamental and advanced usage. The book contains new and advanced features such as magic functions, type checking.
Book Synopsis Profound Python Data Science by : Önder Teker
Download or read book Profound Python Data Science written by Önder Teker and published by Godoro. This book was released on 2023-11-26 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book covers data science topics in Python language. Libraries such as Numpy, Matplotlib, Pandas, Scipy are explained in detail. In addition to data science, the book contains the usage of many libraries for developers of Python. The basic knowledge needed to use Artificial Intelligence, Machine Learning, Natural Language Processing, Computer Vision features are covered. The book contains tools for data analysis and business intelligence.
Book Synopsis Deep Learning with Python by : Francois Chollet
Download or read book Deep Learning with Python written by Francois Chollet and published by Simon and Schuster. This book was released on 2017-11-30 with total page 597 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. What's Inside Deep learning from first principles Setting up your own deep-learning environment Image-classification models Deep learning for text and sequences Neural style transfer, text generation, and image generation About the Reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others. Table of Contents PART 1 - FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2 - DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deep-learning best practices Generative deep learning Conclusions appendix A - Installing Keras and its dependencies on Ubuntu appendix B - Running Jupyter notebooks on an EC2 GPU instance
Download or read book Effective Python written by Brett Slatkin and published by Pearson Education. This book was released on 2015 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: Effective Python will help students harness the full power of Python to write exceptionally robust, efficient, maintainable, and well-performing code. Utilizing the concise, scenario-driven style pioneered in Scott Meyers's best-selling Effective C++, Brett Slatkin brings together 53 Python best practices, tips, shortcuts, and realistic code examples from expert programmers. Each section contains specific, actionable guidelines organized into items, each with carefully worded advice supported by detailed technical arguments and illuminating examples.
Download or read book Python written by Daniel Géron and published by Daniel Geron. This book was released on 2021-02-24 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 bundle 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 bundle 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 bundle, 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; How to develop modules and functions in Python How to install and use magic command in Ipython Functionalities of NumPy library for numerical programming Functionalities of Pandas library for data analysis Creating and customizing figures to visualize data with Matplotlib library 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!
Book Synopsis Hands-On Python Deep Learning for the Web by : Anubhav Singh
Download or read book Hands-On Python Deep Learning for the Web written by Anubhav Singh and published by Packt Publishing Ltd. This book was released on 2020-05-15 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: Use the power of deep learning with Python to build and deploy intelligent web applications Key FeaturesCreate next-generation intelligent web applications using Python libraries such as Flask and DjangoImplement deep learning algorithms and techniques for performing smart web automationIntegrate neural network architectures to create powerful full-stack web applicationsBook Description When used effectively, deep learning techniques can help you develop intelligent web apps. In this book, you'll cover the latest tools and technological practices that are being used to implement deep learning in web development using Python. Starting with the fundamentals of machine learning, you'll focus on DL and the basics of neural networks, including common variants such as convolutional neural networks (CNNs). You'll learn how to integrate them into websites with the frontends of different standard web tech stacks. The book then helps you gain practical experience of developing a deep learning-enabled web app using Python libraries such as Django and Flask by creating RESTful APIs for custom models. Later, you'll explore how to set up a cloud environment for deep learning-based web deployments on Google Cloud and Amazon Web Services (AWS). Next, you'll learn how to use Microsoft's intelligent Emotion API, which can detect a person's emotions through a picture of their face. You'll also get to grips with deploying real-world websites, in addition to learning how to secure websites using reCAPTCHA and Cloudflare. Finally, you'll use NLP to integrate a voice UX through Dialogflow on your web pages. By the end of this book, you'll have learned how to deploy intelligent web apps and websites with the help of effective tools and practices. What you will learnExplore deep learning models and implement them in your browserDesign a smart web-based client using Django and FlaskWork with different Python-based APIs for performing deep learning tasksImplement popular neural network models with TensorFlow.jsDesign and build deep web services on the cloud using deep learningGet familiar with the standard workflow of taking deep learning models into productionWho this book is for This deep learning book is for data scientists, machine learning practitioners, and deep learning engineers who are looking to perform deep learning techniques and methodologies on the web. You will also find this book useful if you’re a web developer who wants to implement smart techniques in the browser to make it more interactive. Working knowledge of the Python programming language and basic machine learning techniques will be beneficial.
Book Synopsis Deep Learning with Python by : Daniel Géron
Download or read book Deep Learning with Python written by Daniel Géron and published by Tiger Gain Limited. This book was released on 2021-01-12 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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!
Book Synopsis Deep Learning with Python by : Mark Graph
Download or read book Deep Learning with Python written by Mark Graph and published by . This book was released on 2019-10-15 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book doesn't have any superpowers or magic formula to help you master the art of neural networks and deep learning. We believe that such learning is all in your heart. You need to learn a concept by heart and then brainstorm its different possibilities. I don't claim that after reading this book you will become an expert in Python and Deep Learning Neural Networks. Instead, you will, for sure, have a basic understanding of deep learning and its implications and real-life applications. Most of the time, what confuses us is the application of a certain thing in our lives. Once we know that, we can relate the subject to that particular thing and learn. An interesting thing is that neural networks also learn the same way. This makes it easier to learn about them when we know the basics. Let's take a look at what this book has to offer: ● The basics of Python including data types, operators and numbers. ● Advanced programming in Python with Python expressions, types and much more. ● A comprehensive overview of deep learning and its link to the smart systems that we are now building. ● An overview of how artificial neural networks work in real life. ● An overview of PyTorch. ● An overview of TensorFlow. ● An overview of Keras. ● How to create a convolutional neural network. ● A comprehensive understanding of deep learning applications and its ethical implications, including in the present and future. This book offers you the basic knowledge about Python and Deep Learning Neural Networks that you will need to lay the foundation for future studies. This book will start you on the road to mastering the art of deep learning neural networks. When I say that I don't have the magic formula to make you learn, I mean it. My point is that you should learn Python coding and Python libraries to build neural networks by practicing hard. The more you practice, the better it is for your skills. It is only after thorough and in depth practice that you will be able to create your own programs. Unlike other books, I don't claim that this book will make you a master of deep learning after a single read. That's not realistic, in fact, it's even a bit absurd. What I claim is that you will definitely learn about the basics. The rest is practice. The more you practice the better you code.
Book Synopsis Deep Learning with Python by : Daniel Géron
Download or read book Deep Learning with Python written by Daniel Géron and published by Daniel Geron. This book was released on 2021-02-19 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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!
Book Synopsis Python for Data Analysis by : Jason Scratch
Download or read book Python for Data Analysis written by Jason Scratch and published by . This book was released on 2020-11-09 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: Are you interested in seeing what deep learning, machine learning, and data analysis are all about and how they are going to be able to help you to get more out of your business and make good decisions about the future of your company? Would you like to see how all of this is going to come together and make you more profitable than ever? This guidebook is going to be the perfect companion and tool for your needs. You will find that we will talk about all of the topics that you need to know when it comes to working with data analysis and data science in no time. And it will not take long before we actually use some of these projects and processes on our own as well. Many companies want to find ways to get ahead of their competition and provide the best options to their customers all at the same time. And they want to make sure that they are making some of the best decisions that you need in order to get ahead in your competition. Some of the highlights of the book include: What is deep learning How to conduct a data analysis The different Python libraries that you are able to use for deep learning. Understanding some of the math behind neural networks. The basics of working with the TensorFlow library that can help you with your deep learning project. How to handle the Keras library for your needs. The PyTorch library and how this library is going to be able to help us out with machine learning and deep learning. Looking more at machine learning and how we are able to fit this into some of the data analysis that we are talking about. How deep learning is going to be helpful when it is time to handle your own predictive analysis. Deep learning, machine learning, and data analysis are important parts of many business today. These topics and processes are going to help us to really explore the industry, the customers, the competition and more that are going to come out when we want to help our business succeed and when we want to figure out what steps we need to take in order to get ahead of the competition. Are you ready to want to master this? Scroll up and click on the BUY NOW button to get your copy now!
Book Synopsis Hands-On Deep Learning Architectures with Python by : Yuxi (Hayden) Liu
Download or read book Hands-On Deep Learning Architectures with Python written by Yuxi (Hayden) Liu and published by Packt Publishing Ltd. This book was released on 2019-04-30 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: Concepts, tools, and techniques to explore deep learning architectures and methodologies Key FeaturesExplore advanced deep learning architectures using various datasets and frameworksImplement deep architectures for neural network models such as CNN, RNN, GAN, and many moreDiscover design patterns and different challenges for various deep learning architecturesBook Description Deep learning architectures are composed of multilevel nonlinear operations that represent high-level abstractions; this allows you to learn useful feature representations from the data. This book will help you learn and implement deep learning architectures to resolve various deep learning research problems. Hands-On Deep Learning Architectures with Python explains the essential learning algorithms used for deep and shallow architectures. Packed with practical implementations and ideas to help you build efficient artificial intelligence systems (AI), this book will help you learn how neural networks play a major role in building deep architectures. You will understand various deep learning architectures (such as AlexNet, VGG Net, GoogleNet) with easy-to-follow code and diagrams. In addition to this, the book will also guide you in building and training various deep architectures such as the Boltzmann mechanism, autoencoders, convolutional neural networks (CNNs), recurrent neural networks (RNNs), natural language processing (NLP), GAN, and more—all with practical implementations. By the end of this book, you will be able to construct deep models using popular frameworks and datasets with the required design patterns for each architecture. You will be ready to explore the potential of deep architectures in today's world. What you will learnImplement CNNs, RNNs, and other commonly used architectures with PythonExplore architectures such as VGGNet, AlexNet, and GoogLeNetBuild deep learning architectures for AI applications such as face and image recognition, fraud detection, and many moreUnderstand the architectures and applications of Boltzmann machines and autoencoders with concrete examples Master artificial intelligence and neural network concepts and apply them to your architectureUnderstand deep learning architectures for mobile and embedded systemsWho this book is for If you’re a data scientist, machine learning developer/engineer, or deep learning practitioner, or are curious about AI and want to upgrade your knowledge of various deep learning architectures, this book will appeal to you. You are expected to have some knowledge of statistics and machine learning algorithms to get the best out of this book
Book Synopsis Learn Python 3 the Hard Way by : Zed A. Shaw
Download or read book Learn Python 3 the Hard Way written by Zed A. Shaw and published by Addison-Wesley Professional. This book was released on 2017-06-26 with total page 752 pages. Available in PDF, EPUB and Kindle. Book excerpt: You Will Learn Python 3! Zed Shaw has perfected the world’s best system for learning Python 3. Follow it and you will succeed—just like the millions of beginners Zed has taught to date! You bring the discipline, commitment, and persistence; the author supplies everything else. In Learn Python 3 the Hard Way, you’ll learn Python by working through 52 brilliantly crafted exercises. Read them. Type their code precisely. (No copying and pasting!) Fix your mistakes. Watch the programs run. As you do, you’ll learn how a computer works; what good programs look like; and how to read, write, and think about code. Zed then teaches you even more in 5+ hours of video where he shows you how to break, fix, and debug your code—live, as he’s doing the exercises. Install a complete Python environment Organize and write code Fix and break code Basic mathematics Variables Strings and text Interact with users Work with files Looping and logic Data structures using lists and dictionaries Program design Object-oriented programming Inheritance and composition Modules, classes, and objects Python packaging Automated testing Basic game development Basic web development It’ll be hard at first. But soon, you’ll just get it—and that will feel great! This course will reward you for every minute you put into it. Soon, you’ll know one of the world’s most powerful, popular programming languages. You’ll be a Python programmer. This Book Is Perfect For Total beginners with zero programming experience Junior developers who know one or two languages Returning professionals who haven’t written code in years Seasoned professionals looking for a fast, simple, crash course in Python 3
Download or read book Python written by Daniel Géron and published by . This book was released on 2019-09-18 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: Buy the Paperback Version of this Book and get the Kindle Book Version for FREE!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 bundle 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 bundle 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 bundle, 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: 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; The basics of the three main Python languages that will help you get the work done-including TensorFlow, Keras, and PyTorch; How to install the three Python libraries to help you get started; A closer look at neural networks, what they are, why they are important, and some of the mathematics of making them work; The basics you need to know about TensorFlow and some of the deep learning you can do with this library; The basics of the Keras library and some of the deep learning you can do with this library; A look at the PyTorch library, how it is different from the other two, and the basics of deep learning with this library; How to develop modules and functions in Python How to install and use magic command in Ipython Functionalities of NumPy library for numerical programming Functionalities of Pandas library for data analysis Creating and customizing figures to visualize data with Matplotlib library 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! Would you like to know more? Scroll to the top of the page and select the BUY NOW button!
Book Synopsis Python Machine Learning By Example by : Yuxi (Hayden) Liu
Download or read book Python Machine Learning By Example written by Yuxi (Hayden) Liu and published by Packt Publishing Ltd. This book was released on 2019-02-28 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: Grasp machine learning concepts, techniques, and algorithms with the help of real-world examples using Python libraries such as TensorFlow and scikit-learn Key FeaturesExploit the power of Python to explore the world of data mining and data analyticsDiscover machine learning algorithms to solve complex challenges faced by data scientists todayUse Python libraries such as TensorFlow and Keras to create smart cognitive actions for your projectsBook Description The surge in interest in machine learning (ML) is due to the fact that it revolutionizes automation by learning patterns in data and using them to make predictions and decisions. If you’re interested in ML, this book will serve as your entry point to ML. Python Machine Learning By Example begins with an introduction to important ML concepts and implementations using Python libraries. Each chapter of the book walks you through an industry adopted application. You’ll implement ML techniques in areas such as exploratory data analysis, feature engineering, and natural language processing (NLP) in a clear and easy-to-follow way. With the help of this extended and updated edition, you’ll understand how to tackle data-driven problems and implement your solutions with the powerful yet simple Python language and popular Python packages and tools such as TensorFlow, scikit-learn, gensim, and Keras. To aid your understanding of popular ML algorithms, the book covers interesting and easy-to-follow examples such as news topic modeling and classification, spam email detection, stock price forecasting, and more. By the end of the book, you’ll have put together a broad picture of the ML ecosystem and will be well-versed with the best practices of applying ML techniques to make the most out of new opportunities. What you will learnUnderstand the important concepts in machine learning and data scienceUse Python to explore the world of data mining and analyticsScale up model training using varied data complexities with Apache SparkDelve deep into text and NLP using Python libraries such NLTK and gensimSelect and build an ML model and evaluate and optimize its performanceImplement ML algorithms from scratch in Python, TensorFlow, and scikit-learnWho this book is for If you’re a machine learning aspirant, data analyst, or data engineer highly passionate about machine learning and want to begin working on ML assignments, this book is for you. Prior knowledge of Python coding is assumed and basic familiarity with statistical concepts will be beneficial although not necessary.
Book Synopsis Python for Data Analysis by : Paul Jamsey
Download or read book Python for Data Analysis written by Paul Jamsey and published by . This book was released on 2020-02-26 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: Are you interested in learning more about your competition, and how they can benefit from some of your products and services? Are you interested in seeing what deep learning, machine learning, and data analysis are all about and how they are going to be able to help you to get more out of your business and make good decisions about the future of your company? Would you like to see how all of this is going to come together and make you more profitable than ever? This guidebook is going to be the perfect companion and tool for your needs. You will find that we will talk about all of the topics that you need to know when it comes to working with data analysis and data science in no time and it will not take long before we actually use some of these projects and processes on our own as well. There are so many benefits that come with working in data science, data analysis, and deep learning, and finding time to it it all in and making it work can seem complicated. This guidebook is going to be the tool that you need to get this all under control. Some of the topics that we are going to discuss in this topic and will ensure that we can get this process down includes: What is deep learning How to conduct a data analysis The different Python libraries that you are able to use for deep learning. Understanding some of the math behind neural networks. The basics of working with the TensorFlow library that can help you with your deep learning project. How to handle the Keras library for your needs. The PyTorch library and how this library is going to be able to help us out with machine learning and deep learning. Looking more at machine learning and how we are able to fit this into some of the data analysis that we are talking about. How deep learning is going to be helpful when it is time to handle your own predictive analysis. Deep learning, machine learning, and data analysis are important parts of many businesses today. These topics and processes are going to help us to really explore the industry, the customers, the competition and more that are going to come out when we want to help our business succeed and when we want to figure out what steps we need to take in order to get ahead of the competition. When you are ready to learn more about data analysis and deep learning, make sure to check out this guidebook to help you get started.
Book Synopsis Deep Learning for Coders with fastai and PyTorch by : Jeremy Howard
Download or read book Deep Learning for Coders with fastai and PyTorch written by Jeremy Howard and published by O'Reilly Media. This book was released on 2020-06-29 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
