DEEP LEARNING WITH PYTHON

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Advanced Deep Learning With Python

Author : Ivan Vasilev
ISBN : 9781789952711
Genre : Computers
File Size : 53.18 MB
Format : PDF
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Gain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural networks using the Python ecosystem Key Features Get to grips with building faster and more robust deep learning architectures Investigate and train convolutional neural network (CNN) models with GPU-accelerated libraries such as TensorFlow and PyTorch Apply deep neural networks (DNNs) to computer vision problems, NLP, and GANs Book Description In order to build robust deep learning systems, you’ll need to understand everything from how neural networks work to training CNN models. In this book, you’ll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application. You’ll start by understanding the building blocks and the math behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular CNN architectures in object detection and image segmentation. Further on, you’ll focus on variational autoencoders and GANs. You’ll then use neural networks to extract sophisticated vector representations of words, before going on to cover various types of recurrent networks, such as LSTM and GRU. You’ll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later, you’ll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural networks with fewer training samples. Finally, you’ll understand how to apply deep learning to autonomous vehicles. By the end of this book, you’ll have mastered key deep learning concepts and the different applications of deep learning models in the real world. What you will learn Cover advanced and state-of-the-art neural network architectures Understand the theory and math behind neural networks Train DNNs and apply them to modern deep learning problems Use CNNs for object detection and image segmentation Implement generative adversarial networks (GANs) and variational autoencoders to generate new images Solve natural language processing (NLP) tasks, such as machine translation, using sequence-to-sequence models Understand DL techniques, such as meta-learning and graph neural networks Who this book is for This book is for data scientists, deep learning engineers and researchers, and AI developers who want to further their knowledge of deep learning and build innovative and unique deep learning projects. Anyone looking to get to grips with advanced use cases and methodologies adopted in the deep learning domain using real-world examples will also find this book useful. Basic understanding of deep learning concepts and working knowledge of the Python programming language is assumed.
Category: Computers

Python Machine Learning

Author : Sebastian Raschka
ISBN : 9781789958294
Genre : Computers
File Size : 34.98 MB
Format : PDF
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Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning. Key Features Third edition of the bestselling, widely acclaimed Python machine learning book Clear and intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover TensorFlow 2, Generative Adversarial Network models, reinforcement learning, and best practices Book Description Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself. Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents. This book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments. What you will learn Master the frameworks, models, and techniques that enable machines to 'learn' from data Use scikit-learn for machine learning and TensorFlow for deep learning Apply machine learning to image classification, sentiment analysis, intelligent web applications, and more Build and train neural networks, GANs, and other models Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who This Book Is For If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for anyone who wants to teach computers how to learn from data.
Category: Computers

Deep Learning With Python

Author : Jason Brownlee
ISBN :
Genre : Computers
File Size : 71.85 MB
Format : PDF, Docs
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Deep learning is the most interesting and powerful machine learning technique right now. Top deep learning libraries are available on the Python ecosystem like Theano and TensorFlow. Tap into their power in a few lines of code using Keras, the best-of-breed applied deep learning library. In this Ebook, learn exactly how to get started and apply deep learning to your own machine learning projects.
Category: Computers

Deep Learning With Python

Author : Mark Graph
ISBN : 169994735X
Genre :
File Size : 86.95 MB
Format : PDF, ePub, Mobi
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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.
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Deep Learning With Python

Author : Nihkil Ketkar
ISBN : 1484253639
Genre : Computers
File Size : 47.78 MB
Format : PDF
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This new edition focuses on the practical aspects of implementing deep learning solutions with PyTorch, a platform developed by Facebook’s Artificial Intelligence Research Group with a hands-on approach to understanding both theory and practice. This book will prepare you for applying deep learning to real world problems with a sound theoretical foundation and practical know-how with respect to PyTorch. You'll start with a perspective on how and why deep learning with PyTorch has emerged as an path-breaking framework with a set of tools and techniques to solve real-world problems. Next, the book will ground you with the mathematical fundamentals of linear algebra, vector calculus, probability and optimization. Having established this foundation, you'll move on to key components and functionality of PyTorch including layers, loss functions and optimization algorithms. You'll also gain an understanding of Graphical Processing Unit (GPU) based computation, which is essential for training deep learning models. All the key architectures in deep learning are covered, including feedforward networks, convolution neural networks, recurrent neural networks, long short-term memory networks, autoencoders and generative adversarial networks. Backed by a number of tricks of the trade for training and optimizing deep learning models, the new edition of Deep Learning with Python explains the best practices in taking these models to production with PyTorch
Category: Computers

Hands On Deep Learning Algorithms With Python

Author : Sudharsan Ravichandiran
ISBN : 9781789344516
Genre : Computers
File Size : 74.28 MB
Format : PDF
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Understand basic to advanced deep learning algorithms, the mathematical principles behind them, and their practical applications. Key Features Get up-to-speed with building your own neural networks from scratch Gain insights into the mathematical principles behind deep learning algorithms Implement popular deep learning algorithms such as CNNs, RNNs, and more using TensorFlow Book Description Deep learning is one of the most popular domains in the AI space, allowing you to develop multi-layered models of varying complexities. This book introduces you to popular deep learning algorithms—from basic to advanced—and shows you how to implement them from scratch using TensorFlow. Throughout the book, you will gain insights into each algorithm, the mathematical principles behind it, and how to implement it in the best possible manner. The book starts by explaining how you can build your own neural networks, followed by introducing you to TensorFlow, the powerful Python-based library for machine learning and deep learning. Moving on, you will get up to speed with gradient descent variants, such as NAG, AMSGrad, AdaDelta, Adam, and Nadam. The book will then provide you with insights into RNNs and LSTM and how to generate song lyrics with RNN. Next, you will master the math for convolutional and capsule networks, widely used for image recognition tasks. Then you learn how machines understand the semantics of words and documents using CBOW, skip-gram, and PV-DM. Afterward, you will explore various GANs, including InfoGAN and LSGAN, and autoencoders, such as contractive autoencoders and VAE. By the end of this book, you will be equipped with all the skills you need to implement deep learning in your own projects. What you will learn Implement basic-to-advanced deep learning algorithms Master the mathematics behind deep learning algorithms Become familiar with gradient descent and its variants, such as AMSGrad, AdaDelta, Adam, and Nadam Implement recurrent networks, such as RNN, LSTM, GRU, and seq2seq models Understand how machines interpret images using CNN and capsule networks Implement different types of generative adversarial network, such as CGAN, CycleGAN, and StackGAN Explore various types of autoencoder, such as Sparse autoencoders, DAE, CAE, and VAE Who this book is for If you are a machine learning engineer, data scientist, AI developer, or simply want to focus on neural networks and deep learning, this book is for you. Those who are completely new to deep learning, but have some experience in machine learning and Python programming, will also find the book very helpful.
Category: Computers

Beginning Anomaly Detection Using Python Based Deep Learning

Author : Sridhar Alla
ISBN : 9781484251775
Genre : Computers
File Size : 49.81 MB
Format : PDF, Docs
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Utilize this easy-to-follow beginner's guide to understand how deep learning can be applied to the task of anomaly detection. Using Keras and PyTorch in Python, the book focuses on how various deep learning models can be applied to semi-supervised and unsupervised anomaly detection tasks. This book begins with an explanation of what anomaly detection is, what it is used for, and its importance. After covering statistical and traditional machine learning methods for anomaly detection using Scikit-Learn in Python, the book then provides an introduction to deep learning with details on how to build and train a deep learning model in both Keras and PyTorch before shifting the focus to applications of the following deep learning models to anomaly detection: various types of Autoencoders, Restricted Boltzmann Machines, RNNs & LSTMs, and Temporal Convolutional Networks. The book explores unsupervised and semi-supervised anomaly detection along with the basics of time series-based anomaly detection. By the end of the book you will have a thorough understanding of the basic task of anomaly detection as well as an assortment of methods to approach anomaly detection, ranging from traditional methods to deep learning. Additionally, you are introduced to Scikit-Learn and are able to create deep learning models in Keras and PyTorch. What You Will Learn Understand what anomaly detection is and why it is important in today's world Become familiar with statistical and traditional machine learning approaches to anomaly detection using Scikit-Learn Know the basics of deep learning in Python using Keras and PyTorch Be aware of basic data science concepts for measuring a model's performance: understand what AUC is, what precision and recall mean, and more Apply deep learning to semi-supervised and unsupervised anomaly detection Who This Book Is For Data scientists and machine learning engineers interested in learning the basics of deep learning applications in anomaly detection
Category: Computers

Deep Learning With Python

Author : Alex Root
ISBN : 1691414336
Genre :
File Size : 43.28 MB
Format : PDF, ePub
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Do you have some knowledge of Python coding and want to take it further? Interested in learning what Deep Learning is all about? This book offers you everything you need to learn what machine learning is and how to take it further with deep learning. A relatively new field in data science, programmers are only just starting to delve into the possibilities and the potential uses for deep learning but, as we head further into a digital world, a world of technology, this is one subject that is on the fast track. What You Will Learn: What machine learning is? An overview of supervised, unsupervised, and reinforcement learning How machine learning differs from deep learning? Why Python is the language to use? The basics of Keras What deep learning is? What neural networks are and how they work? All about loss functions Image processing Text data processing Word embeddings Real-world applications of deep learning And more I even added in a short glossary to help you understand some of the more common deep learning term! This book is aimed at beginners and even if you don't have a lot of programming knowledge, you can still learn. Interested?Then hit that Buy Now button and start your Deep Learning journey on the right foot.
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Deep Learning With Python

Author : Daniel Geron
ISBN : 1086549953
Genre :
File Size : 50.65 MB
Format : PDF, ePub
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Buy the Paperback Version of this Book and get the Kindle Book Version for FREEDo 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: 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; 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! Would you like to know more? Scroll to the top of the page and select the BUY NOW button!
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Python Machine Learning

Author : Zach Codings
ISBN : 1696563119
Genre :
File Size : 24.50 MB
Format : PDF
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What is machine learning and why would a programmer want to learn how to use it? Is artificial intelligence the same as working with machine learning? Are you interested in becoming a machine learning expert but don't know where to start from? Keep reading... The future of our world is evolving towards an era where interaction with machines form the foundation of most tasks we perform. In light of this, it is important to gain actionable knowledge in machine learning technologies and skills. These skills will be useful in the near future as you maneuver through different career paths. Today data is driving many business processes, and without data, it is impossible to imagine where many of the top businesses would be. Imagine how you used to struggle with search results online back in the day, and how easy it is to look for something online today and get the right results. All this is possible through machine learning models. What you need is a foundational approach to learning the basics of machine learning. You can use this knowledge to build your expertise in machine learning over time. While this is an introductory level book, it introduces you to vast concepts in machine learning that will be important to your career. By the end of the book, you will have learned so much about machine learning and the respective python libraries that you will use when building models all the time. An important aspect of machine learning that we must stress even at this juncture is data analysis. Data is key to the success of machine learning and deep learning models. When implemented properly, the kind of data you have will make a big difference in whether your model succeeds or not. Since we are discussing machine learning and the future of computing as we know it, we will also dedicate some time to discussing the current trends in the world, and how they affect our ability to perform some tasks. In this case, we will look at the Internet of Things (IoT) and how we can use different approaches to integrate machine learning and IoT models. Throughout these pages, you will learn: The Fundamentals of Python for Machine Learning Data Analysis in Python Comparing Deep Learning and Machine Learning Machine Learning with Scikit-Learn Deep Learning with TensorFlow Deep Learning with PyTorch and Keras The Role of Machine Learning in the Internet of Things (IoT) Looking to the Future with Machine Learning And much more... Even if you don't have any background in machine learning and Python programming, this book will give you the tools to develop machine learning models. Arm yourself with all this knowledge! Scroll up and click the BUY NOW BUTTON!
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