Getting Started With Deep Learning
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Getting Started With Deep Learning
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Author : Ricardo Calix
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-01-18
Getting Started With Deep Learning written by Ricardo Calix and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-18 with categories.
Ever since 2007 with the explosion in the use of parallel hardware, the field of machine learning has become more exciting and more promising. It seems that the dream of true AI is finally just around the corner. Certainly, there are many companies that are starting to rely heavily on AI for their products. These include companies in search like Facebook, Google, as well as retailers and multimedia companies like Amazon and Netflix. But more recently many others in the health-care and cyber security industries are also interested in what AI and machine learning can do for them. Some of these technologies such as Tensorflow (which came about around 2015) are new and not widely understood. In this book I hope to provide basic discussions of machine learning and in particular deep learning to help readers to quickly get started in using these technologies. The book is not a comprehensive survey on deep learning. There are many topics I do not cover here as too much material can be overwhelming to the un-initiated. There are many good books that cover all the theory in depth and I will mention some of them in the book. Instead, the goal in this book is to help people new to deep learning to quickly get started with these concepts using python and Tensorflow. Therefore, a lot of detail is spent on helping the reader to write his or her first deep network classifier. Additionally, I will try to connect several elements in machine learning which I think are related and are very important for data analysis and automatic classification. In general, I prefer python and I will try to present all examples using this great language. I will also use the more common libraries and the Linux development environment. Many people use SKlearn and I have therefore tried to use this library in the Tensorflow examples so that the focus is mainly on creating the deep layer network architectures.
Deep Learning With Pytorch
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Author : Jason Brownlee
language : en
Publisher: Machine Learning Mastery
Release Date : 2023-03-21
Deep Learning With Pytorch written by Jason Brownlee and has been published by Machine Learning Mastery this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-21 with Computers categories.
Deep learning is currently the most interesting and powerful machine learning technique. PyTorch is one of the dominant libraries for deep learning in the Python ecosystem and is widely used in research. With PyTorch, you can easily tap into the power of deep learning with just a few lines of code. Many deep learning models are created in PyTorch. Therefore, knowing PyTorch opens the door for you to leverage the power of deep learning. This Ebook is written in the friendly Machine Learning Mastery style that you’re used to, learn exactly how to get started and apply deep learning to your own machine learning projects.
Mastering Pytorch
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Author : Ashish Ranjan Jha
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-05-31
Mastering Pytorch written by Ashish Ranjan Jha 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 2024-05-31 with Computers categories.
Master advanced techniques and algorithms for machine learning with PyTorch using real-world examples Updated for PyTorch 2.x, including integration with Hugging Face, mobile deployment, diffusion models, and graph neural networks Get With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader Free Key Features Understand how to use PyTorch to build advanced neural network models Get the best from PyTorch by working with Hugging Face, fastai, PyTorch Lightning, PyTorch Geometric, Flask, and Docker Unlock faster training with multiple GPUs and optimize model deployment using efficient inference frameworks Book DescriptionPyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch deep learning book will help you uncover expert techniques to get the most out of your data and build complex neural network models. You’ll build convolutional neural networks for image classification and recurrent neural networks and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation, using generative models, including diffusion models. You'll not only build and train your own deep reinforcement learning models in PyTorch but also learn to optimize model training using multiple CPUs, GPUs, and mixed-precision training. You’ll deploy PyTorch models to production, including mobile devices. Finally, you’ll discover the PyTorch ecosystem and its rich set of libraries. These libraries will add another set of tools to your deep learning toolbelt, teaching you how to use fastai to prototype models and PyTorch Lightning to train models. You’ll discover libraries for AutoML and explainable AI (XAI), create recommendation systems, and build language and vision transformers with Hugging Face. By the end of this book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.What you will learn Implement text, vision, and music generation models using PyTorch Build a deep Q-network (DQN) model in PyTorch Deploy PyTorch models on mobile devices (Android and iOS) Become well versed in rapid prototyping using PyTorch with fastai Perform neural architecture search effectively using AutoML Easily interpret machine learning models using Captum Design ResNets, LSTMs, and graph neural networks (GNNs) Create language and vision transformer models using Hugging Face Who this book is for This deep learning with PyTorch book is for data scientists, machine learning engineers, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning models using PyTorch. This book is ideal for those looking to switch from TensorFlow to PyTorch. Working knowledge of deep learning with Python is required.
Getting Started In Data Science
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Author : Ayodele Odubela
language : en
Publisher: fullyConnected Inc.
Release Date : 2020-12-01
Getting Started In Data Science written by Ayodele Odubela and has been published by fullyConnected Inc. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-01 with Technology & Engineering categories.
Data Science is one of the "sexiest jobs of the 21st Century", but few resources are geared towards learners with no prior experience. Getting Started in Data Science simplifies the core of the concepts of Data Science and Machine Learning. This book includes perspectives of a Data Science from someone with a non-traditional route to a Data Science career. Getting Started in Data Science creatively weaves in ethical questions and asks readers to question the harm models can cause as they learn new concepts. Unlike many other books for beginners, this book covers bias and accountability in detail as well as career insight that informs readers of what expectations are in industry Data Science.
Getting Started With Deep Learning
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Author : Josh Patterson
language : en
Publisher:
Release Date : 2018
Getting Started With Deep Learning written by Josh Patterson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Artificial intelligence categories.
Deep Learning For Beginners
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Author : Dr. Pablo Rivas
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-09-18
Deep Learning For Beginners written by Dr. Pablo Rivas 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 2020-09-18 with Computers categories.
Implement supervised, unsupervised, and generative deep learning (DL) models using Keras and Dopamine with TensorFlow Key FeaturesUnderstand the fundamental machine learning concepts useful in deep learningLearn the underlying mathematical concepts as you implement deep learning models from scratchExplore easy-to-understand examples and use cases that will help you build a solid foundation in DLBook Description With information on the web exponentially increasing, it has become more difficult than ever to navigate through everything to find reliable content that will help you get started with deep learning. This book is designed to help you if you're a beginner looking to work on deep learning and build deep learning models from scratch, and you already have the basic mathematical and programming knowledge required to get started. The book begins with a basic overview of machine learning, guiding you through setting up popular Python frameworks. You will also understand how to prepare data by cleaning and preprocessing it for deep learning, and gradually go on to explore neural networks. A dedicated section will give you insights into the working of neural networks by helping you get hands-on with training single and multiple layers of neurons. Later, you will cover popular neural network architectures such as CNNs, RNNs, AEs, VAEs, and GANs with the help of simple examples, and learn how to build models from scratch. At the end of each chapter, you will find a question and answer section to help you test what you've learned through the course of the book. By the end of this book, you'll be well-versed with deep learning concepts and have the knowledge you need to use specific algorithms with various tools for different tasks. What you will learnImplement recurrent neural networks (RNNs) and long short-term memory (LSTM) for image classification and natural language processing tasksExplore the role of convolutional neural networks (CNNs) in computer vision and signal processingDiscover the ethical implications of deep learning modelingUnderstand the mathematical terminology associated with deep learningCode a generative adversarial network (GAN) and a variational autoencoder (VAE) to generate images from a learned latent spaceImplement visualization techniques to compare AEs and VAEsWho this book is for This book is for aspiring data scientists and deep learning engineers who want to get started with the fundamentals of deep learning and neural networks. Although no prior knowledge of deep learning or machine learning is required, familiarity with linear algebra and Python programming is necessary to get started.
Machine Learning For Computer Scientists And Data Analysts
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Author : Setareh Rafatirad
language : en
Publisher: Springer Nature
Release Date : 2022-07-09
Machine Learning For Computer Scientists And Data Analysts written by Setareh Rafatirad and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-09 with Technology & Engineering categories.
This textbook introduces readers to the theoretical aspects of machine learning (ML) algorithms, starting from simple neuron basics, through complex neural networks, including generative adversarial neural networks and graph convolution networks. Most importantly, this book helps readers to understand the concepts of ML algorithms and enables them to develop the skills necessary to choose an apt ML algorithm for a problem they wish to solve. In addition, this book includes numerous case studies, ranging from simple time-series forecasting to object recognition and recommender systems using massive databases. Lastly, this book also provides practical implementation examples and assignments for the readers to practice and improve their programming capabilities for the ML applications.
Learning Strategies And Constructionism In Modern Education Settings
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Author : Daniela, Linda
language : en
Publisher: IGI Global
Release Date : 2018-06-08
Learning Strategies And Constructionism In Modern Education Settings written by Daniela, Linda and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-08 with Education categories.
Educational strategies have evolved over the years due to research breakthroughs and the application of technology. By using the latest learning innovations, curriculum and instructional design can be enhanced and strengthened. Also, as learners move away from traditional scholarly media and toward technology-based education, students gain an advantage in learning about their world and how to interact with modern society. Learning Strategies and Constructionism in Modern Education Settings is a critical scholarly resource that enhances the competencies of educational professionals by providing practical advice on providing an innovative educational process to promote the cognitive growth of individuals, regardless of special needs or obstacles. The book features coverage on a variety of topics including integration approaches of digital media in the teaching/learning process, the role of parents for developing digital literacy in their young children, and the effectiveness of using technology tools to teach mathematics. As a publication focused on education advancements through technology, the book serves as a useful resource for academicians, educators, school administrators, and individuals seeking current research on education technologies.
Learning Deep Learning
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Author : Magnus Ekman
language : en
Publisher: Addison-Wesley Professional
Release Date : 2021-07-19
Learning Deep Learning written by Magnus Ekman and has been published by Addison-Wesley Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-19 with Computers categories.
NVIDIA's Full-Color Guide to Deep Learning: All You Need to Get Started and Get Results "To enable everyone to be part of this historic revolution requires the democratization of AI knowledge and resources. This book is timely and relevant towards accomplishing these lofty goals." -- From the foreword by Dr. Anima Anandkumar, Bren Professor, Caltech, and Director of ML Research, NVIDIA "Ekman uses a learning technique that in our experience has proven pivotal to success—asking the reader to think about using DL techniques in practice. His straightforward approach is refreshing, and he permits the reader to dream, just a bit, about where DL may yet take us." -- From the foreword by Dr. Craig Clawson, Director, NVIDIA Deep Learning Institute Deep learning (DL) is a key component of today's exciting advances in machine learning and artificial intelligence. Learning Deep Learning is a complete guide to DL. Illuminating both the core concepts and the hands-on programming techniques needed to succeed, this book is ideal for developers, data scientists, analysts, and others--including those with no prior machine learning or statistics experience. After introducing the essential building blocks of deep neural networks, such as artificial neurons and fully connected, convolutional, and recurrent layers, Magnus Ekman shows how to use them to build advanced architectures, including the Transformer. He describes how these concepts are used to build modern networks for computer vision and natural language processing (NLP), including Mask R-CNN, GPT, and BERT. And he explains how a natural language translator and a system generating natural language descriptions of images. Throughout, Ekman provides concise, well-annotated code examples using TensorFlow with Keras. Corresponding PyTorch examples are provided online, and the book thereby covers the two dominating Python libraries for DL used in industry and academia. He concludes with an introduction to neural architecture search (NAS), exploring important ethical issues and providing resources for further learning. Explore and master core concepts: perceptrons, gradient-based learning, sigmoid neurons, and back propagation See how DL frameworks make it easier to develop more complicated and useful neural networks Discover how convolutional neural networks (CNNs) revolutionize image classification and analysis Apply recurrent neural networks (RNNs) and long short-term memory (LSTM) to text and other variable-length sequences Master NLP with sequence-to-sequence networks and the Transformer architecture Build applications for natural language translation and image captioning NVIDIA's invention of the GPU sparked the PC gaming market. The company's pioneering work in accelerated computing--a supercharged form of computing at the intersection of computer graphics, high-performance computing, and AI--is reshaping trillion-dollar industries, such as transportation, healthcare, and manufacturing, and fueling the growth of many others. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Deep Learning For Beginners
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Author : Steven Cooper
language : en
Publisher: Roland Bind
Release Date : 2018-11-06
Deep Learning For Beginners written by Steven Cooper and has been published by Roland Bind this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-06 with Computers categories.
☆★The Best Deep Learning Book for Beginners★☆ If you are looking for a complete beginners guide to learn deep learning with examples, in just a few hours, then you need to continue reading. This book delves into the basics of deep learning for those who are enthusiasts concerning all things machine learning and artificial intelligence. For those who have seen movies which show computer systems taking over the world like, Terminator, or benevolent systems that watch over the population, i.e. Person of Interest, this should be right up your alley. This book will give you the basics of what deep learning entails. That means frameworks used by coders and significant components and tools used in deep learning, that enable facial recognition, speech recognition, and virtual assistance. Yes, deep learning provides the tools through which systems like Siri became possible. ★★ Grab your copy today and learn ★★ ♦ Deep learning utilizes frameworks which allow people to develop tools which are able to offer better abstraction, along with simplification of hard programming issues. TensorFlow is the most popular tool and is used by corporate giants such as Airbus, Twitter, and even Google. ♦ The book illustrates TensorFlow and Caffe2 as the prime frameworks that are used for development by Google and Facebook. Facebook illustrates Caffe2 as one of the lightweight and modular deep learning frameworks, though TensorFlow is the most popular one, considering it has a lot of popularity, and thus, a big forum, which allows for assistance on main problems. ♦ The book considers several components and tools of deep learning such as the neural networks; CNNs, RNNs, GANs, and auto-encoders. These algorithms create the building blocks which propel deep learning and advance it. ♦ The book also considers several applications, including chatbots and virtual assistants, which have become the main focus for deep learning into the future, as they represent the next frontier in information gathering and connectivity. The Internet of Things is also represented here, as deep learning allows for the integration of various systems via an artificial intelligence system, which is already being used for the home and car functions. ♦ And much more... The use of data science adds a lot of value to businesses, and we will continue to see the need for data scientists grow. This book is probably one of the best books for beginners. It's a step-by-step guide for any person who wants to start learning deep learning and artificial intelligence from scratch. When data science can reduce spending costs by billions of dollars in the healthcare industry, why wait to jump in? If you want to get started on deep learning and the concepts that run artificial technologies, don't wait any longer. Scroll up and click the buy now button to get this book today!