Deep Learning Crash Course For Beginners With Python

Artificial intelligence is the rage today! While you may find it difficult to understand the most recent advancements in AI, it simply boils down to two most celebrated developments: Machine Learning and Deep Learning. In 2020, Deep Learning is leagues ahead because of its supremacy when it comes to accuracy, especially when trained with enormous amounts of data. Deep Learning, essentially, is a subset of Machine Learning, but it's capable of achieving tremendous power and flexibility. And the era of big data technology presents vast opportunities for incredible innovations in deep learning. How Is This Book Different? This book gives equal importance to the theoretical as well as practical aspects of deep learning. You will understand how high-performing deep learning algorithms work. In every chapter, the theoretical explanation of the different types of deep learning techniques is followed by practical examples. You will learn how to implement different deep learning techniques using the TensorFlow Keras library for Python. Each chapter contains exercises that you can use to assess your understanding of the concepts explained in that chapter. Also, in the Resources, the Python notebook for each chapter is provided. The key advantage of buying this book is you get instant access to all the extra content presented with this book--Python codes, references, exercises, and PDFs--on the publisher's website. You don't need to spend an extra cent. The datasets used in this book are either downloaded at runtime or are available in the Resources/Datasets folder. Another advantage is a detailed explanation of the installation steps for the software that you will need to implement the various deep learning algorithms in this book is provided. That is, you get to experiment with the practical aspects of Deep Learning right from page 1. Even if you are new to Python, you will find the crash course on Python programming language in the first chapter immensely useful. Since all the codes and datasets are included with this book, you only need access to a computer with the internet to get started. The topics covered include: Python Crash Course Deep Learning Prerequisites: Linear and Logistic Regression Neural Networks from Scratch in Python Introduction to TensorFlow and Keras Convolutional Neural Networks Sequence Classification with Recurrent Neural Networks Deep Learning for Natural Language Processing Unsupervised Learning with Autoencoders Answers to All Exercises Click the BUY button and download the book now to start your Deep Learning journey.

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!

Are you fascinated by Data Science but it seems too complicated? Do you want to learn everything about Artificial Intelligence but it looks like it is an exclusive club? If this is you, please keep reading: you are in the right place, looking at the right book. SInce you are reading these lines you have probably already noticed this: Artificial Intelligence is all around you. Your smartphone that suggests you the next word you want to type, your Netflix account that recommends you the series you may like or Spotify's personalised playlists. This is how machines are learning from you in everyday life. And these examples are only the surface of this technological revolution. Everyone knows (well, almost everyone) how important Data Science is for the growth and success of the biggest tech companies, and many people know about the Machine Learning impact in science, medicine and statistics. Also, it is quite commonly known that Artificial Intelligence, Machine Learning Deep Learning, and the mastering of their most important language, Python, can offer a lot of possibilities in work and business. And you yourself are probably thinking "I surely can see that opportunity, but how can I seize it?" Well, if you kept reading so far you are on the right track to answer your question. Either if you want to start your own AI entreprise, to empower your business or to work in the greatest and most innovative companies, Artificial Intelligence is the future, and Python and Neural Networks programming is The Skill you want to have. The good news is that there is no exclusive club, you can easily (if you commit, of course) learn how to find your way around Artificial Intelligence, Data Science, Deep Learning and Machine Learning, and to do that Data Science for Beginners is the best way. In Data Science for Beginners you will discover: The most effective starting points when training deep neural nets The smartest way to approach Machine Learning What libraries are and which one is the best for you Tips and tricks for a smooth and painless journey into artificial intelligence Why decision tree is the way The TensorFlow parts that are going to make your coding life easy Why python is the best language for Machine Learning How to bring your ideas into a computer How to talk with deep neural networks How to deal with variables and data The most common myths about Machine Learning debunked Even If you don't know anything about programming, understanding Data Science is the ideal place to start. Still, if you already know something about programming but not about how to apply it to Artificial Intelligence, Data Science is what you want to understand. Buy now Data Science for Beginners to start your path of Artificial Intelligence.

If you want a book that guides you through the programming world especially Python, then I would recommend this bookas your number one choice! There are number of interesting topics for experienced and novice developers that can be explord. These include algorithms and data structures, strings of text and numbers, types of operators, classes and objects, web programming and the main errors in the development of a program. It also talks about data science and its impact on the future so far. The purpose of this book on Python is to provide the fundamentals of this language to scientists accustomed to other languages such as Fortran, C, Matlab and so on, and also to computer scientists working in the scentific community. Basic programming skills are needed for the reader to make the most of this book. The author details the basics of language and the presents, with examples, tools and methods of implementation. Thus, anyone working in the field will enjoy this book without first having any knowledge of Python. It is nevertheless important to have good programming skills, otherwise you risk quickly being overtaken by the pace of the book. The presentation of the Python language and its standard library is quite dense but the explanations are clear and the remarks interesting, all with mant simple examples, ideal for someone who needs to dive quickly into Python. All you need to know from start to finish is contained in this book and will leave you satisfied with a better understanding of things GET IT NOW and you'll be glad you did!

Start Programming Python What if you could make your own program, one that is able to learn by trial and error, or based on the information that you show it? What if you could get a program that could adapt and change based on the input of the user? And what if you were able to make all of this happen with the Python coding language, helping even beginner's work with more complicated codes? This is all possible with Python machine learning. This guidebook is going to take some time to look at Python machine learning and all of the neat things that you are able to do with it. Machine learning is a growing field, one that a lot of programmers want to spend their time on. But even though this sounds like a complicated part of technology to work with, you will find that with the help of the Python coding language, anyone can start writing their own codes in machine learning. This guidebook is going to take a look at all of the different topics that you need to know in order to get started with Python machine learning. Some of the topics that we will explore inside include: The basics of machine learning The difference between supervised and unsupervised machine learning. Setting up your new environment in the Python language. Data preprocessing with the help of machine learning. How to use Python coding to help with linear regression. Decision trees and random forests. How to work with support vector regression problems. Can machine learning really help with Naïve Bayes problems? Accelerated data analysis using the Python code. And so much more! If you have been interested in learning more about machine learning, and you want to be able to learn a few of the codes that can make it happen for you, make sure to check out this guidebook to help you get started! If all of this sounds like your ideal book, then hop on over and hit now that buy button! Well, stress no more! Buy this book and also learn all... and DOWNLOAD IT NOW! ★★Buy the Paperback Version of this Book and get the Kindle Book version for FREE ★★

Python programming language has rendered itself as the language of choice for coding beginners and advanced software programmers alike. This book is written to help you master the basic concepts of Python coding and how you can utilize your coding skills to analyze a large volume of data and uncover valuable information that can otherwise be easily lost in the volume. It was designed primarily to emphasize the readability of the programming code, and its syntax enables programmers to convey ideas using fewer lines of code. Python programming language increases the speed of operation while allowing for higher efficiency in creating system integrations. Some of the highlights of the book include: - Key features and advantages of learning to code Python as well as the history of how Python programming was created - Step-by-step instructions on how to install Python on your operating systems (Windows, Mac, and Linux) - The concept of Python data types is presented in exquisite detail with various examples of each data type - How to create Python variables - Comprehensive lists of a variety of built-in functions and methods supported by Python - Basic concepts of writing efficient and effective Python codes, focusing on various programming elements - How to write if and else statements to retrieve desired information from your data - For and While loops are explained with explicit details in an easy-to-understand language - Basic concepts of big data analysis and machine learning algorithms - A brief overview of various renowned machine learning libraries All the concepts are explained with standard Python coding syntax supported with relevant examples and followed by exercises to help you test and verify your understanding of those concepts. Finally, as an added bonus you will learn some Python tips and tricks to take your machine learning programming game to the next level. Remember, knowledge is power, and with the great power you will gather from this book, you will be armed to make sound personal and professional technological choices. Your Python programming skillset will improve drastically, and you will be poised to develop your very own machine learning model! Don't you think it can be that easy? If you really want to have proof of all this, don't waste any more time! Grab your copy now!

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