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Deep Learning With Tensorflow Keras And Pytorch

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Machine Learning With Python

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Author : Cuantum Technologies
language : en
Publisher:
Release Date : 2024-08-24

Machine Learning With Python written by Cuantum Technologies and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-24 with Computers categories.

This comprehensive guide takes you on an exciting journey from the basics of Python programming to the depths of neural networks and deep learning. It demystifies the complex world of machine learning, making it accessible and understandable, regardless of your background.The book is divided into 14 detailed chapters, each focusing on a specific aspect of machine learning. You'll start with Python and essential libraries, move on to data preprocessing, and then delve into both supervised and unsupervised learning. The book also covers advanced topics like neural networks, deep learning with TensorFlow, Keras, and PyTorch, and even explores future trends and ethical considerations in machine learning.

Deep Learning With Tensorflow Keras And Pytorch

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Author : Jon Krohn
language : en
Publisher:
Release Date : 2020

Deep Learning With Tensorflow Keras And Pytorch written by Jon Krohn and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.

7+ Hours of Video Instruction An intuitive, application-focused introduction to deep learning and TensorFlow, Keras, and PyTorch Overview Deep Learning with TensorFlow, Keras, and PyTorch LiveLessons is an introduction to deep learning that brings the revolutionary machine-learning approach to life with interactive demos from the most popular deep learning library, TensorFlow, and its high-level API, Keras, as well as the hot new library PyTorch. Essential theory is whiteboarded to provide an intuitive understanding of deep learning's underlying foundations; i.e., artificial neural networks. Paired with tips for overcoming common pitfalls and hands-on code run-throughs provided in Python-based Jupyter notebooks, this foundational knowledge empowers individuals with no previous understanding of neural networks to build powerful state-of-the-art deep learning models. About the Instructor Jon Krohn is the Chief Data Scientist at the machine learning company untapt. He presents a popular series of tutorials published by Addison-Wesley and is the author of the acclaimed book Deep Learning Illustrated . Jon teaches his deep learning curriculum in-classroom at the New York City Data Science Academy. He holds a doctorate in neuroscience from Oxford University, lectures at Columbia University, and carries out machine vision research at Columbia's Irving Medical Center. Skill Level Intermediate Learn How To Build deep learning models in all the major libraries: TensorFlow, Keras, and PyTorch Understand the language and theory of artificial neural networks Excel across a broad range of computational problems including machine vision, natural language processing, and reinforcement learning Create algorithms with state-of-the-art performance by fine-tuning model architectures Self-direct and complete your own Deep Learning projects Who Should Take This Course Software engineers, data scientists, analysts, and statisticians with an interest in deep learning. Code examples are provided in Python, so familiarity with it or another object-oriented programming language would be helpful. Previous experience with statistics or machine learning is not necessary. Course Requirements Some experience with any of the following are an asset, but none are essential: Object-oriented programming, specifically Python Simple shell commands; e.g., in Bash Machine learning or statistics Lesson Descriptions Lesson 1: Introduction to Deep Learning and Artifi...

Deep Learning With Python

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Author : Daniel Géron
language : en
Publisher: Daniel Geron
Release Date : 2021-02-19

Deep Learning With Python written by Daniel Géron and has been published by Daniel Geron this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-19 with categories.

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!

Deep Learning With Python

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Author : Daniel Géron
language : en
Publisher: Tiger Gain Limited
Release Date : 2021-01-18

Deep Learning With Python written by Daniel Géron and has been published by Tiger Gain Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-18 with categories.

Python Deep Learning Develop Your First Neural Network In Python Using Tensorflow Keras And Pytorch

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Author : Samuel Burns
language : en
Publisher: Step-By-Step Tutorial for Begi
Release Date : 2019-04-03

Python Deep Learning Develop Your First Neural Network In Python Using Tensorflow Keras And Pytorch written by Samuel Burns and has been published by Step-By-Step Tutorial for Begi this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-03 with Computers categories.

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.

Python Deep Learning

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Author : Ivan Vasilev
language : en
Publisher:
Release Date : 2019

Python Deep Learning written by Ivan Vasilev and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.

Deep Learning With Python

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Author : Mark Graph
language : en
Publisher:
Release Date : 2019-10-15

Deep Learning With Python written by Mark Graph and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-15 with categories.

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.

Python Deep Learning

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Author : Donald R. Brewer
language : en
Publisher: Wiley
Release Date : 2022-02-02

Python Deep Learning written by Donald R. Brewer and has been published by Wiley this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-02 with Computers categories.

We are at crossroads in deep learning. Today, deep learning developers typically utilize one of the top two machine learning frameworks: Tensorflow, developed by Google/Deepmind, and PyTorch, developed by Facebook. In industry, Tensorflow is still more widely adopted. Still, PyTorch is rapidly up-and-coming in the research community, where 70%-80% of recently submitted conference research papers utilize PyTorch instead of Tensorflow. A recent 2020 Stack Overflow survey of the most popular frameworks and libraries reported that PyTorch was selected by an est 30% of respondents vs. 70% for Tensorflow, with PyTorch nearly doubling in popularity over the last two years. In the next couple of years, as these machine learning frameworks become equal in popularity, a book must well verse developers in both so they can choose the right methodology to help solve their deep learning problems. The problem is that most deep learning books published today focus on just one of the machine learning frameworks. Python Deep Learning would identify both frameworks' pros and cons and then teach deep learning concepts utilizing practical examples from the framework best suited for particular problems. This book also features the APIs and libraries integrated with the respective framework, Keras for Tensorflow and fastai for PyTorch, that make application development and deployment even more straightforward. What this Books Covers: Introduction and overview of deep learning concepts Description of the two machine learning frameworks: Tensorflow and PyTorch, as well as successful examples of their usage Detail the pros and cons of each machine learning framework Overview of the supportive libraries and APIs (including Keras and fastai) for each of the frameworks that make application development simpler Chapter-by-chapter review of the top neural network topologies (CNN, RNN, LSTM, MLP, and several newer variants) Interesting code examples of practical applications of the different neural networks, not the same tired MNIST and other examples often utilized today Final series of code examples (in Tensorflow or PyTorch) of real-world deep learning solutions that utilize more exotic neural network topologies

Deep Learning With Pytorch

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Author : Vishnu Subramanian
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-02-23

Deep Learning With Pytorch written by Vishnu Subramanian and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-23 with Computers categories.

Build neural network models in text, vision and advanced analytics using PyTorch Key Features Learn PyTorch for implementing cutting-edge deep learning algorithms. Train your neural networks for higher speed and flexibility and learn how to implement them in various scenarios; Cover various advanced neural network architecture such as ResNet, Inception, DenseNet and more with practical examples; Book Description Deep learning powers the most intelligent systems in the world, such as Google Voice, Siri, and Alexa. Advancements in powerful hardware, such as GPUs, software frameworks such as PyTorch, Keras, Tensorflow, and CNTK along with the availability of big data have made it easier to implement solutions to problems in the areas of text, vision, and advanced analytics. This book will get you up and running with one of the most cutting-edge deep learning libraries—PyTorch. PyTorch is grabbing the attention of deep learning researchers and data science professionals due to its accessibility, efficiency and being more native to Python way of development. You'll start off by installing PyTorch, then quickly move on to learn various fundamental blocks that power modern deep learning. You will also learn how to use CNN, RNN, LSTM and other networks to solve real-world problems. This book explains the concepts of various state-of-the-art deep learning architectures, such as ResNet, DenseNet, Inception, and Seq2Seq, without diving deep into the math behind them. You will also learn about GPU computing during the course of the book. You will see how to train a model with PyTorch and dive into complex neural networks such as generative networks for producing text and images. By the end of the book, you'll be able to implement deep learning applications in PyTorch with ease. What you will learn Use PyTorch for GPU-accelerated tensor computations Build custom datasets and data loaders for images and test the models using torchvision and torchtext Build an image classifier by implementing CNN architectures using PyTorch Build systems that do text classification and language modeling using RNN, LSTM, and GRU Learn advanced CNN architectures such as ResNet, Inception, Densenet, and learn how to use them for transfer learning Learn how to mix multiple models for a powerful ensemble model Generate new images using GAN’s and generate artistic images using style transfer Who this book is for This book is for machine learning engineers, data analysts, data scientists interested in deep learning and are looking to explore implementing advanced algorithms in PyTorch. Some knowledge of machine learning is helpful but not a mandatory need. Working knowledge of Python programming is expected.

Deep Learning And Ai Superhero

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Author : Cuantum Technologies LLC
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-01-20

Deep Learning And Ai Superhero written by Cuantum Technologies LLC and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-20 with Computers categories.

Master TensorFlow, Keras, and PyTorch for deep learning in AI applications. Learn neural networks, CNNs, RNNs, LSTMs, and GANs through hands-on exercises and real-world projects. Key Features TensorFlow, Keras, and PyTorch for diverse deep learning frameworks Neural network concepts with real-world industry relevance Cloud and edge AI deployment techniques for scalable solutions Book DescriptionDive into the world of deep learning with this comprehensive guide that bridges theory and practice. From foundational neural networks to advanced architectures like CNNs, RNNs, and Transformers, this book equips you with the tools to build, train, and optimize AI models using TensorFlow, Keras, and PyTorch. Clear explanations of key concepts such as gradient descent, loss functions, and backpropagation are combined with hands-on exercises to ensure practical understanding. Explore cutting-edge AI frameworks, including generative adversarial networks (GANs) and autoencoders, while mastering real-world applications like image classification, text generation, and natural language processing. Detailed chapters cover transfer learning, fine-tuning pretrained models, and deployment strategies for cloud and edge computing. Practical exercises and projects further solidify your skills as you implement AI solutions for diverse challenges. Whether you're deploying AI models on cloud platforms like AWS or optimizing them for edge devices with TensorFlow Lite, this book provides step-by-step guidance. Designed for developers, AI enthusiasts, and data scientists, it balances theoretical depth with actionable insights, making it the ultimate resource for mastering modern deep learning frameworks and advancing your career in AIWhat you will learn Understand neural network basics Build models using TensorFlow and Keras Train and optimize PyTorch models Apply CNNs for image recognition Use RNNs and LSTMs for sequence tasks Leverage Transformers in NLP Who this book is for This book is for software developers, AI enthusiasts, data scientists, and ML engineers who aim to master deep learning frameworks. A foundational understanding of programming and basic ML concepts is recommended. Ideal for those seeking hands-on experience in real-world AI projects.

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Deep Learning With Tensorflow Keras And Pytorch

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