Download Deep Learning Ebook PDF

Deep Learning

Deep Learning
A Book

by Ian Goodfellow,Yoshua Bengio,Aaron Courville

  • Publisher : MIT Press
  • Release : 2016-11-18
  • Pages : 775
  • ISBN : 0262035618
  • Language : En, Es, Fr & De
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An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Deep Learning

Deep Learning
Research and Applications

by Siddhartha Bhattacharyya,Vaclav Snasel,Aboul Ella Hassanien,Satadal Saha,B. K. Tripathy

  • Publisher : Walter de Gruyter GmbH & Co KG
  • Release : 2020-06-22
  • Pages : 161
  • ISBN : 3110670925
  • Language : En, Es, Fr & De
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This book focuses on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it provides an insight of deep neural networks in action with illustrative coding examples. Deep learning is a new area of machine learning research which has been introduced with the objective of moving ML closer to one of its original goals, i.e. artificial intelligence. Deep learning was developed as an ML approach to deal with complex input-output mappings. While traditional methods successfully solve problems where final value is a simple function of input data, deep learning techniques are able to capture composite relations between non-immediately related fields, for example between air pressure recordings and English words, millions of pixels and textual description, brand-related news and future stock prices and almost all real world problems. Deep learning is a class of nature inspired machine learning algorithms that uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The learning may be supervised (e.g. classification) and/or unsupervised (e.g. pattern analysis) manners. These algorithms learn multiple levels of representations that correspond to different levels of abstraction by resorting to some form of gradient descent for training via backpropagation. Layers that have been used in deep learning include hidden layers of an artificial neural network and sets of propositional formulas. They may also include latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep boltzmann machines. Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision, automatic speech recognition (ASR) and human action recognition.

Deep Learning with Structured Data

Deep Learning with Structured Data
A Book

by Mark Ryan

  • Publisher : Manning Publications
  • Release : 2020-12-29
  • Pages : 273
  • ISBN : 1617296724
  • Language : En, Es, Fr & De
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Deep learning offers the potential to identify complex patterns and relationships hidden in data of all sorts. Deep Learning with Structured Data shows you how to apply powerful deep learning analysis techniques to the kind of structured, tabular data you'll find in the relational databases that real-world businesses depend on. Filled with practical, relevant applications, this book teaches you how deep learning can augment your existing machine learning and business intelligence systems. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Deep Learning with PyTorch

Deep Learning with PyTorch
A Book

by Eli Stevens,Luca Antiga,Thomas Viehmann

  • Publisher : Manning Publications
  • Release : 2020-08-04
  • Pages : 520
  • ISBN : 1617295264
  • Language : En, Es, Fr & De
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Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands, providing a comfortable Python experience that gets you started quickly and then grows with you as you—and your deep learning skills—become more sophisticated. Deep Learning with PyTorch will make that journey engaging and fun. Summary Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands, providing a comfortable Python experience that gets you started quickly and then grows with you as you—and your deep learning skills—become more sophisticated. Deep Learning with PyTorch will make that journey engaging and fun. Foreword by Soumith Chintala, Cocreator of PyTorch. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Although many deep learning tools use Python, the PyTorch library is truly Pythonic. Instantly familiar to anyone who knows PyData tools like NumPy and scikit-learn, PyTorch simplifies deep learning without sacrificing advanced features. It’s excellent for building quick models, and it scales smoothly from laptop to enterprise. Because companies like Apple, Facebook, and JPMorgan Chase rely on PyTorch, it’s a great skill to have as you expand your career options. It’s easy to get started with PyTorch. It minimizes cognitive overhead without sacrificing the access to advanced features, meaning you can focus on what matters the most - building and training the latest and greatest deep learning models and contribute to making a dent in the world. PyTorch is also a snap to scale and extend, and it partners well with other Python tooling. PyTorch has been adopted by hundreds of deep learning practitioners and several first-class players like FAIR, OpenAI, FastAI and Purdue. About the book Deep Learning with PyTorch teaches you to create neural networks and deep learning systems with PyTorch. This practical book quickly gets you to work building a real-world example from scratch: a tumor image classifier. Along the way, it covers best practices for the entire DL pipeline, including the PyTorch Tensor API, loading data in Python, monitoring training, and visualizing results. After covering the basics, the book will take you on a journey through larger projects. The centerpiece of the book is a neural network designed for cancer detection. You'll discover ways for training networks with limited inputs and start processing data to get some results. You'll sift through the unreliable initial results and focus on how to diagnose and fix the problems in your neural network. Finally, you'll look at ways to improve your results by training with augmented data, make improvements to the model architecture, and perform other fine tuning. What's inside Training deep neural networks Implementing modules and loss functions Utilizing pretrained models from PyTorch Hub Exploring code samples in Jupyter Notebooks About the reader For Python programmers with an interest in machine learning. About the author Eli Stevens had roles from software engineer to CTO, and is currently working on machine learning in the self-driving-car industry. Luca Antiga is cofounder of an AI engineering company and an AI tech startup, as well as a former PyTorch contributor. Thomas Viehmann is a PyTorch core developer and machine learning trainer and consultant. consultant based in Munich, Germany and a PyTorch core developer. Table of Contents PART 1 - CORE PYTORCH 1 Introducing deep learning and the PyTorch Library 2 Pretrained networks 3 It starts with a tensor 4 Real-world data representation using tensors 5 The mechanics of learning 6 Using a neural network to fit the data 7 Telling birds from airplanes: Learning from images 8 Using convolutions to generalize PART 2 - LEARNING FROM IMAGES IN THE REAL WORLD: EARLY DETECTION OF LUNG CANCER 9 Using PyTorch to fight cancer 10 Combining data sources into a unified dataset 11 Training a classification model to detect suspected tumors 12 Improving training with metrics and augmentation 13 Using segmentation to find suspected nodules 14 End-to-end nodule analysis, and where to go next PART 3 - DEPLOYMENT 15 Deploying to production

Practical Deep Learning for Cloud, Mobile, and Edge

Practical Deep Learning for Cloud, Mobile, and Edge
Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow

by Anirudh Koul,Siddha Ganju,Meher Kasam

  • Publisher : O'Reilly Media
  • Release : 2019-10-14
  • Pages : 620
  • ISBN : 1492034835
  • Language : En, Es, Fr & De
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Whether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral Explore fun projects, from Silicon Valley’s Not Hotdog app to 40+ industry case studies Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning Use transfer learning to train models in minutes Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users

Deep Learning for Coders with fastai and PyTorch

Deep Learning for Coders with fastai and PyTorch
A Book

by Jeremy Howard,Sylvain Gugger

  • Publisher : O'Reilly Media
  • Release : 2020-06-29
  • Pages : 624
  • ISBN : 1492045497
  • Language : En, Es, Fr & De
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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

Deep Learning for the Life Sciences

Deep Learning for the Life Sciences
Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More

by Bharath Ramsundar,Peter Eastman,Patrick Walters,Vijay Pande

  • Publisher : O'Reilly Media
  • Release : 2019-04-10
  • Pages : 238
  • ISBN : 1492039802
  • Language : En, Es, Fr & De
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Deep learning has already achieved remarkable results in many fields. Now it’s making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields. Ideal for practicing developers and scientists ready to apply their skills to scientific applications such as biology, genetics, and drug discovery, this book introduces several deep network primitives. You’ll follow a case study on the problem of designing new therapeutics that ties together physics, chemistry, biology, and medicine—an example that represents one of science’s greatest challenges. Learn the basics of performing machine learning on molecular data Understand why deep learning is a powerful tool for genetics and genomics Apply deep learning to understand biophysical systems Get a brief introduction to machine learning with DeepChem Use deep learning to analyze microscopic images Analyze medical scans using deep learning techniques Learn about variational autoencoders and generative adversarial networks Interpret what your model is doing and how it’s working

Handbook of Research on the Impact of Deep Learning and IoT on Multi-Industry Applications

Handbook of Research on the Impact of Deep Learning and IoT on Multi-Industry Applications
A Book

by Roshani Raut,Albena Dimitrova Mihovska

  • Publisher : Engineering Science Reference
  • Release : 2021-01-29
  • Pages : 400
  • ISBN : 1799875172
  • Language : En, Es, Fr & De
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"This book provides insights on how deep learning, together with IOT, will impact various sectors such as healthcare, agriculture, cyber security, and social media analysis applications offering solutions to various real-world problems using these methods from various researchers' point of views"--

Deep Learning

Deep Learning
A Practitioner's Approach

by Josh Patterson,Adam Gibson

  • Publisher : "O'Reilly Media, Inc."
  • Release : 2017-07-28
  • Pages : 532
  • ISBN : 1491914238
  • Language : En, Es, Fr & De
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How can machine learning--especially deep neural networks--make a real difference in your organization? This hands-on guide not only provides practical information, but helps you get started building efficient deep learning networks. The authors provide the fundamentals of deep learning--tuning, parallelization, vectorization, and building pipelines--that are valid for any library before introducing the open source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you'll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J.

Deep Learning for Image Processing Applications

Deep Learning for Image Processing Applications
A Book

by D.J. Hemanth,V. Vieira Estrela

  • Publisher : IOS Press
  • Release : 2017-12
  • Pages : 284
  • ISBN : 1614998221
  • Language : En, Es, Fr & De
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Deep learning and image processing are two areas of great interest to academics and industry professionals alike. The areas of application of these two disciplines range widely, encompassing fields such as medicine, robotics, and security and surveillance. The aim of this book, ‘Deep Learning for Image Processing Applications’, is to offer concepts from these two areas in the same platform, and the book brings together the shared ideas of professionals from academia and research about problems and solutions relating to the multifaceted aspects of the two disciplines. The first chapter provides an introduction to deep learning, and serves as the basis for much of what follows in the subsequent chapters, which cover subjects including: the application of deep neural networks for image classification; hand gesture recognition in robotics; deep learning techniques for image retrieval; disease detection using deep learning techniques; and the comparative analysis of deep data and big data. The book will be of interest to all those whose work involves the use of deep learning and image processing techniques.

Deep Learning Architectures

Deep Learning Architectures
A Mathematical Approach

by Ovidiu Calin

  • Publisher : Springer Nature
  • Release : 2020-02-13
  • Pages : 760
  • ISBN : 3030367215
  • Language : En, Es, Fr & De
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This book describes how neural networks operate from the mathematical point of view. As a result, neural networks can be interpreted both as function universal approximators and information processors. The book bridges the gap between ideas and concepts of neural networks, which are used nowadays at an intuitive level, and the precise modern mathematical language, presenting the best practices of the former and enjoying the robustness and elegance of the latter. This book can be used in a graduate course in deep learning, with the first few parts being accessible to senior undergraduates. In addition, the book will be of wide interest to machine learning researchers who are interested in a theoretical understanding of the subject.

Functional Brain Network Analysis Based on Unsupervised Deep Learning

Functional Brain Network Analysis Based on Unsupervised Deep Learning
A Book

by Qinglin Dong

  • Publisher : Unknown Publisher
  • Release : 2019
  • Pages : 198
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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In the neuroimaging and brain mapping communities, researchers have proposed a variety of computational methods and tools to learn functional brain network (FBN), such as general linear models (GLM), independent component analysis (ICA) and sparse dictionary learning (SDL). Recently, deep learning has attracted much attention in the fields of machine learning and data mining, and it has been proven that deep learning approach has superb representation power over traditional shallow models. In this research, three deep models, which are volumetric sparse deep belief networks (VS-DBN), neural architecture search based DBN (NAS-DBN) and recurrent autoencoder (RAE), were designed to explore representations of fMRI volumes. The quantitative analysis showed that these deep models have promising capability in learning meaningful FBNs and revealed novel insights into the organizational architecture of human brain.

Hands-On Mathematics for Deep Learning

Hands-On Mathematics for Deep Learning
Build a Solid Mathematical Foundation for Training Efficient Deep Neural Networks

by Jay Dawani

  • Publisher : Unknown Publisher
  • Release : 2020-06-12
  • Pages : 364
  • ISBN : 9781838647292
  • Language : En, Es, Fr & De
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The main aim of this book is to make the advanced mathematical background accessible to someone with a programming background. This book will equip the readers with not only deep learning architectures but the mathematics behind them. With this book, you will understand the relevant mathematics that goes behind building deep learning models.

Advanced Deep Learning with Python

Advanced Deep Learning with Python
Design and implement advanced next-generation AI solutions using TensorFlow and PyTorch

by Ivan Vasilev

  • Publisher : Packt Publishing Ltd
  • Release : 2019-12-12
  • Pages : 468
  • ISBN : 1789952719
  • Language : En, Es, Fr & De
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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.

Deep Learning Illustrated

Deep Learning Illustrated
A Visual, Interactive Guide to Artificial Intelligence

by Jon Krohn,Grant Beyleveld,Aglaé Bassens

  • Publisher : Addison-Wesley Professional
  • Release : 2019-08-05
  • Pages : 416
  • ISBN : 0135121728
  • Language : En, Es, Fr & De
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"The authors’ clear visual style provides a comprehensive look at what’s currently possible with artificial neural networks as well as a glimpse of the magic that’s to come." –Tim Urban, author of Wait But Why Fully Practical, Insightful Guide to Modern Deep Learning Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn. World-class instructor and practitioner Jon Krohn–with visionary content from Grant Beyleveld and beautiful illustrations by Aglaé Bassens–presents straightforward analogies to explain what deep learning is, why it has become so popular, and how it relates to other machine learning approaches. Krohn has created a practical reference and tutorial for developers, data scientists, researchers, analysts, and students who want to start applying it. He illuminates theory with hands-on Python code in accompanying Jupyter notebooks. To help you progress quickly, he focuses on the versatile deep learning library Keras to nimbly construct efficient TensorFlow models; PyTorch, the leading alternative library, is also covered. You’ll gain a pragmatic understanding of all major deep learning approaches and their uses in applications ranging from machine vision and natural language processing to image generation and game-playing algorithms. Discover what makes deep learning systems unique, and the implications for practitioners Explore new tools that make deep learning models easier to build, use, and improve Master essential theory: artificial neurons, training, optimization, convolutional nets, recurrent nets, generative adversarial networks (GANs), deep reinforcement learning, and more Walk through building interactive deep learning applications, and move forward with your own artificial intelligence projects Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

The Deep Learning AI Playbook

The Deep Learning AI Playbook
A Book

by Carlos Perez

  • Publisher : Lulu.com
  • Release : 2021
  • Pages : 329
  • ISBN : 1365879232
  • Language : En, Es, Fr & De
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Deep Learning with R

Deep Learning with R
A Book

by Francois Chollet,J.j. Allaire

  • Publisher : Pearson Professional
  • Release : 2018
  • Pages : 360
  • ISBN : 9781617295546
  • Language : En, Es, Fr & De
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Introduces deep learning systems using the powerful Keras library and its R language interface. The book builds your understanding of deep learning through intuitive explanations and practical examples.

Better Deep Learning

Better Deep Learning
Train Faster, Reduce Overfitting, and Make Better Predictions

by Jason Brownlee

  • Publisher : Machine Learning Mastery
  • Release : 2018-12-13
  • Pages : 575
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Deep learning neural networks have become easy to define and fit, but are still hard to configure. Discover exactly how to improve the performance of deep learning neural network models on your predictive modeling projects. With clear explanations, standard Python libraries, and step-by-step tutorial lessons, you’ll discover how to better train your models, reduce overfitting, and make more accurate predictions.

Deep Learning

Deep Learning
Engage the World Change the World

by Michael Fullan,Joanne Quinn,Joanne McEachen

  • Publisher : Corwin Press
  • Release : 2017-11-06
  • Pages : 208
  • ISBN : 1506368565
  • Language : En, Es, Fr & De
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Engage the World Change the World Deep Learning has claimed the attention of educators and policymakers around the world. This book not only defines what deep learning is, but takes up the question of how to mobilize complex, whole-system change and transform learning for all students. Deep Learning is a global partnership that works to: transform the role of teachers to that of activators who design experiences that build global competencies using real-life problem solving; and supports schools, districts, and systems to shift practice and how to measure learning in authentic ways. This comprehensive strategy incorporates practical tools and processes to engage students, educators, and families in new partnerships and drive deep learning.

Practical Deep Learning with Python

Practical Deep Learning with Python
A Hands-On Introduction

by Ron Kneusel

  • Publisher : No Starch Press
  • Release : 2021-01-28
  • Pages : 464
  • ISBN : 1718500742
  • Language : En, Es, Fr & De
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"An introduction to machine learning and deep learning for beginners. Covers fundamental concepts before presenting classic machine learning models, neural networks, and modern convolutional neural networks. Includes hands-on Python experiments for each model"--