Download or read book Advanced Machine Learning with Python written by John Hearty and published by Packt Publishing Ltd. This book was released on 2016-07-28 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: Solve challenging data science problems by mastering cutting-edge machine learning techniques in Python About This Book Resolve complex machine learning problems and explore deep learning Learn to use Python code for implementing a range of machine learning algorithms and techniques A practical tutorial that tackles real-world computing problems through a rigorous and effective approach Who This Book Is For This title is for Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. If you've ever considered building your own image or text-tagging solution, or of entering a Kaggle contest for instance, this book is for you! Prior experience of Python and grounding in some of the core concepts of machine learning would be helpful. What You Will Learn Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms Apply your new found skills to solve real problems, through clearly-explained code for every technique and test Automate large sets of complex data and overcome time-consuming practical challenges Improve the accuracy of models and your existing input data using powerful feature engineering techniques Use multiple learning techniques together to improve the consistency of results Understand the hidden structure of datasets using a range of unsupervised techniques Gain insight into how the experts solve challenging data problems with an effective, iterative, and validation-focused approach Improve the effectiveness of your deep learning models further by using powerful ensembling techniques to strap multiple models together In Detail Designed to take you on a guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists, this book is just what you need to push your Python algorithms to maximum potential. Clear examples and detailed code samples demonstrate deep learning techniques, semi-supervised learning, and more - all whilst working with real-world applications that include image, music, text, and financial data. The machine learning techniques covered in this book are at the forefront of commercial practice. They are applicable now for the first time in contexts such as image recognition, NLP and web search, computational creativity, and commercial/financial data modeling. Deep Learning algorithms and ensembles of models are in use by data scientists at top tech and digital companies, but the skills needed to apply them successfully, while in high demand, are still scarce. This book is designed to take the reader on a guided tour of the most relevant and powerful machine learning techniques. Clear descriptions of how techniques work and detailed code examples demonstrate deep learning techniques, semi-supervised learning and more, in real world applications. We will also learn about NumPy and Theano. By this end of this book, you will learn a set of advanced Machine Learning techniques and acquire a broad set of powerful skills in the area of feature selection & feature engineering. Style and approach This book focuses on clarifying the theory and code behind complex algorithms to make them practical, useable, and well-understood. Each topic is described with real-world applications, providing both broad contextual coverage and detailed guidance.

Download or read book Advanced Deep Learning with Python written by Ivan Vasilev and published by Packt Publishing Ltd. This book was released on 2019-12-12 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 FeaturesGet to grips with building faster and more robust deep learning architecturesInvestigate and train convolutional neural network (CNN) models with GPU-accelerated libraries such as TensorFlow and PyTorchApply deep neural networks (DNNs) to computer vision problems, NLP, and GANsBook 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 learnCover advanced and state-of-the-art neural network architecturesUnderstand the theory and math behind neural networksTrain DNNs and apply them to modern deep learning problemsUse CNNs for object detection and image segmentationImplement generative adversarial networks (GANs) and variational autoencoders to generate new imagesSolve natural language processing (NLP) tasks, such as machine translation, using sequence-to-sequence modelsUnderstand DL techniques, such as meta-learning and graph neural networksWho 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.

Download or read book Advanced Machine Learning using Python Programming written by SOHARA BANU A R and published by MileStone Research Publications. This book was released on 2023-07-13 with total page 101 pages. Available in PDF, EPUB and Kindle. Book excerpt: THE AUTHOR(S) AND PUBLISHER OF THIS BOOK HAVE USED THEIR BEST EFFORTS IN PREPARING THIS BOOK. THESE EFFORTS INCLUDE THE DEVELOPMENT, RESEARCH ANDTESTING OF THE THEORIES AND PROGRAMS TO DETERMINE THEIR EFFECTIVENESS. THE AUTHORS AND PUBLISHER MAKES NO WARRANTY OF ANY KIND, EXPRESSED OR IMPLIEDWITH REGARD TO THESE PROGRAMS OR THE DOCUMENTATION CONTAINED IN THIS BOOK. THE AUTHORS AND PUBLISHER SHALL NOT BE LIABLE IN ANY EVENT FORINCIDENTAL OR CONSEQUENTIAL DAMAGES IN CONNECTION WITH, OR ARISING OUT OF, THE FURNISHING, PERFORMANCE, OR USE OF THESE PROGRAMS. COPYRIGHTS © 2023 BY MILESTONE RESEARCH PUBLICATIONS, INC. THIS EDITION IS PUBLISHED BY ARRANGEMENT WITH MILESTONE RESEARCH FOUNDATION, INC. THIS BOOK IS SOLD SUBJECT TO THE CONDITION THAT IT SHALL NOT, BY WAY OF TRADE OR OTHERWISE, BE LENT, RESOLD, HIRED OUT, OR OTHERWISE CIRCULATED WITHOUTTHE PUBLISHER'S PRIOR WRITTEN CONSENT IN ANY FORM OF BINDING OR COVER OTHER THAN THAT IN WHICH IT IS PUBLISHED AND WITHOUT A SIMILAR CONDITIONINCLUDING THIS CONDITION BEING IMPOSED ON THE SUBSEQUENT PURCHASER AND WITHOUT LIMITING THE RIGHTS UNDER COPYRIGHT RESERVED ABOVE, NO PART OF THISPUBLICATION MAY BE REPRODUCED, STORED IN OR INTRODUCED INTO RETRIEVAL SYSTEM, OR TRANSMITTED IN ANY FORM OR BY ANY MEANS (ELECTRONIC, MECHANICAL,PHOTOCOPYING, RECORDING AND OTHERWISE) WITHOUT THE PRIOR WRITTEN PERMISSION OF BOTH THE COPYRIGHT OWNER AND THE ABOVE MENTIONED PUBLISHER OFTHIS BOOK.

Download or read book Mathematics for Machine Learning written by Marc Peter Deisenroth and published by Cambridge University Press. This book was released on 2020-04-23 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

Download or read book Python Advanced Guide to Artificial Intelligence written by Giuseppe Bonaccorso and published by Packt Publishing Ltd. This book was released on 2018-12-21 with total page 748 pages. Available in PDF, EPUB and Kindle. Book excerpt: Demystify the complexity of machine learning techniques and create evolving, clever solutions to solve your problems Key FeaturesMaster supervised, unsupervised, and semi-supervised ML algorithms and their implementation Build deep learning models for object detection, image classification, similarity learning, and moreBuild, deploy, and scale end-to-end deep neural network models in a production environmentBook Description This Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms. You'll be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this Learning Path will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries. You'll bring the use of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Next, you'll learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF clusters, deploy production models with TensorFlow Serving. You'll implement different techniques related to object classification, object detection, image segmentation, and more. By the end of this Learning Path, you'll have obtained in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems This Learning Path includes content from the following Packt products: Mastering Machine Learning Algorithms by Giuseppe BonaccorsoMastering TensorFlow 1.x by Armando FandangoDeep Learning for Computer Vision by Rajalingappaa ShanmugamaniWhat you will learnExplore how an ML model can be trained, optimized, and evaluatedWork with Autoencoders and Generative Adversarial NetworksExplore the most important Reinforcement Learning techniquesBuild end-to-end deep learning (CNN, RNN, and Autoencoders) modelsWho this book is for This Learning Path is for data scientists, machine learning engineers, artificial intelligence engineers who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. You will encounter the advanced intricacies and complex use cases of deep learning and AI. A basic knowledge of programming in Python and some understanding of machine learning concepts are required to get the best out of this Learning Path.

Download or read book Machine Learning Algorithms From Scratch with Python written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2016-11-16 with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt: You must understand algorithms to get good at machine learning. The problem is that they are only ever explained using Math. No longer. In this Ebook, finally cut through the math and learn exactly how machine learning algorithms work. Using clear explanations, simple pure Python code (no libraries!) and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill, and implement a suite of linear, nonlinear and ensemble machine learning algorithms from scratch.

Download or read book Hands On Deep Learning Algorithms with Python written by Sudharsan Ravichandiran and published by Packt Publishing Ltd. This book was released on 2019-07-25 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understand basic to advanced deep learning algorithms, the mathematical principles behind them, and their practical applications. Key FeaturesGet up-to-speed with building your own neural networks from scratch Gain insights into the mathematical principles behind deep learning algorithmsImplement popular deep learning algorithms such as CNNs, RNNs, and more using TensorFlowBook 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 learnImplement basic-to-advanced deep learning algorithmsMaster the mathematics behind deep learning algorithmsBecome familiar with gradient descent and its variants, such as AMSGrad, AdaDelta, Adam, and NadamImplement recurrent networks, such as RNN, LSTM, GRU, and seq2seq modelsUnderstand how machines interpret images using CNN and capsule networksImplement different types of generative adversarial network, such as CGAN, CycleGAN, and StackGANExplore various types of autoencoder, such as Sparse autoencoders, DAE, CAE, and VAEWho 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.

Download or read book Machine Learning with Python written by Russel R Russo and published by . This book was released on 2020-10-30 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Are you fascinated by Machine Learning but it seems too complicated?Do you have some coding skills but you want to go deeper in Python and Machine Learning? If this is you, please keep reading: you are in the right place, looking at the right book. Since you are reading this you are probably aware of how important Artificial Intelligence is in these days. In your everyday life Artificial Intelligence is all around you. Every time you buy a product on Amazon, follow a new profile on Instagram, listen to a song on Spotify or reserve a room on Booking, they are learning something out of your behavior. And these are just the most visible aspects of how Machine Learning is having an impact on our lives. Everyone knows (well, almost everyone) how important Machine Learning is for the growth and success of the biggest tech companies, and many people know about the Machine Learning impact in science, medicine and statistics. Also, it is quite commonly known that Artificial Intelligence, Machine Learning, and the mastering of their most important language, Python, can offer a lot of possibilities in work and business. And you yourself are probably thinking "I surely can see that opportunity, but how can I seize it?" Well, if you kept reading so far you are on the right track to answer your question. In Machine Learning with Python you will find: Why python is the best language for Machine Learning How to bring your ideas into a computer The smartest way to approach Machine Learning How to deal with variables and data Tips and tricks for a smooth and painless journey into artificial intelligence The most common myths about Machine Learning debunked So, whether you decided to start now or to go deeper into Artificial Intelligence, Machine Learning and Python Programming, you will only have two unanswered questions right now: "what is the best way to do it? And when is the best time to start?" An easy, clear and complete guide as Machine Learning with Python is the answer to your first question, and about the second one, well, that's an easy one: the best time is NOW! Buy Machine Learning with Python now and start mastering the secrets of Artificial Intelligence.

Download or read book Advanced Python Programming written by Quan Nguyen and published by Packt Publishing Ltd. This book was released on 2022-03-25 with total page 606 pages. Available in PDF, EPUB and Kindle. Book excerpt: Write fast, robust, and highly reusable applications using Python's internal optimization, state-of-the-art performance-benchmarking tools, and cutting-edge libraries Key FeaturesBenchmark, profile, and accelerate Python programs using optimization toolsScale applications to multiple processors with concurrent programmingMake applications robust and reusable using effective design patternsBook Description Python's powerful capabilities for implementing robust and efficient programs make it one of the most sought-after programming languages. In this book, you'll explore the tools that allow you to improve performance and take your Python programs to the next level. This book starts by examining the built-in as well as external libraries that streamline tasks in the development cycle, such as benchmarking, profiling, and optimizing. You'll then get to grips with using specialized tools such as dedicated libraries and compilers to increase your performance at number-crunching tasks, including training machine learning models. The book covers concurrency, a major solution to making programs more efficient and scalable, and various concurrent programming techniques such as multithreading, multiprocessing, and asynchronous programming. You'll also understand the common problems that cause undesirable behavior in concurrent programs. Finally, you'll work with a wide range of design patterns, including creational, structural, and behavioral patterns that enable you to tackle complex design and architecture challenges, making your programs more robust and maintainable. By the end of the book, you'll be exposed to a wide range of advanced functionalities in Python and be equipped with the practical knowledge needed to apply them to your use cases. What you will learnWrite efficient numerical code with NumPy, pandas, and XarrayUse Cython and Numba to achieve native performanceFind bottlenecks in your Python code using profilersOptimize your machine learning models with JAXImplement multithreaded, multiprocessing, and asynchronous programsSolve common problems in concurrent programming, such as deadlocksTackle architecture challenges with design patternsWho this book is for This book is for intermediate to experienced Python programmers who are looking to scale up their applications in a systematic and robust manner. Programmers from a range of backgrounds will find this book useful, including software engineers, scientific programmers, and software architects.

Download or read book Machine Learning with TensorFlow Second Edition written by Mattmann A. Chris and published by Manning Publications. This book was released on 2021-02-02 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library. Summary Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library. Written by NASA JPL Deputy CTO and Principal Data Scientist Chris Mattmann, all examples are accompanied by downloadable Jupyter Notebooks for a hands-on experience coding TensorFlow with Python. New and revised content expands coverage of core machine learning algorithms, and advancements in neural networks such as VGG-Face facial identification classifiers and deep speech classifiers. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Supercharge your data analysis with machine learning! ML algorithms automatically improve as they process data, so results get better over time. You don’t have to be a mathematician to use ML: Tools like Google’s TensorFlow library help with complex calculations so you can focus on getting the answers you need. About the book Machine Learning with TensorFlow, Second Edition is a fully revised guide to building machine learning models using Python and TensorFlow. You’ll apply core ML concepts to real-world challenges, such as sentiment analysis, text classification, and image recognition. Hands-on examples illustrate neural network techniques for deep speech processing, facial identification, and auto-encoding with CIFAR-10. What's inside Machine Learning with TensorFlow Choosing the best ML approaches Visualizing algorithms with TensorBoard Sharing results with collaborators Running models in Docker About the reader Requires intermediate Python skills and knowledge of general algebraic concepts like vectors and matrices. Examples use the super-stable 1.15.x branch of TensorFlow and TensorFlow 2.x. About the author Chris Mattmann is the Division Manager of the Artificial Intelligence, Analytics, and Innovation Organization at NASA Jet Propulsion Lab. The first edition of this book was written by Nishant Shukla with Kenneth Fricklas. Table of Contents PART 1 - YOUR MACHINE-LEARNING RIG 1 A machine-learning odyssey 2 TensorFlow essentials PART 2 - CORE LEARNING ALGORITHMS 3 Linear regression and beyond 4 Using regression for call-center volume prediction 5 A gentle introduction to classification 6 Sentiment classification: Large movie-review dataset 7 Automatically clustering data 8 Inferring user activity from Android accelerometer data 9 Hidden Markov models 10 Part-of-speech tagging and word-sense disambiguation PART 3 - THE NEURAL NETWORK PARADIGM 11 A peek into autoencoders 12 Applying autoencoders: The CIFAR-10 image dataset 13 Reinforcement learning 14 Convolutional neural networks 15 Building a real-world CNN: VGG-Face ad VGG-Face Lite 16 Recurrent neural networks 17 LSTMs and automatic speech recognition 18 Sequence-to-sequence models for chatbots 19 Utility landscape

Download or read book PYTHON PROGRAMMING written by Clive Campbell and published by . This book was released on 2020-12-23 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: You Are About To Discover Why Python Is The Preferred Programming Language In Computer Science, Big Data, Machine Learning, Artificial Intelligence And Other Advanced Computing Stuff, Including How To Actually Use Python In These Fields Of Computing! Python is the primary programming language for advanced computing concepts ranging from machine learning, big data, data analytics, artificial intelligence and many others. And with these concepts slowly becoming mainstream, it makes sense that you have an above average understanding of how to use python to take advantage of such concepts like automation, robotics, data analytics, data science, machine learning and others. So where do you start? What exactly do you need to learn? What's the place of python in big data, artificial intelligence, data science, machine learning, analytics etc.? How do you bring out your A game as a python professional in each of these concepts? If you have these and other related questions, this book is specially written for you, covering everything from basic to advanced stuff that will give you an above average understanding of using python for advanced computing. More precisely, the book covers: Why python is the most preferred programming language for advanced computing stuff like data analysis, big data, deep learning, machine learning, artificial intelligence and more How to handle object-oriented programming and why it is the best kind to handle data analysis. How to perform data analysis, step by step How all the advanced computing concepts like machine learning, deep learning, artificial intelligence and others relate and how python is at the center of it all The best python libraries to use for advanced computing, including sample codes you can write with these libraries How to handle different machine learning algorithms by leveraging the power of python to analyze any data you want Powerful tips and tricks that you can use to handle any problems in code, as they come And MUCH MORE! Indeed; python is powerful, scalable, easy to use and much more, which are important ingredients for unleashing the full capabilities of advanced computing concepts like machine learning, deep learning, artificial intelligence, data science, data analytics and much more. And with the help of this hands on, practical and easy to follow guide to using python for advanced computing, you can rest assured that you will start seeing the fruits of your labor soon! Whether you want to learn python for advanced computing to give your business a competitive edge or want to learn python for advanced computing to add a new skill and possibly climb up the corporate ladder, this book has easy to follow steps to help you throughout the process. Don't wait...Click Buy Now With 1-Click or Buy Now to get started!

Download or read book Python Machine Learning written by Ryan Turner and published by Publishing Factory . This book was released on 2020-04-18 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: Do you need your computer to learn as it works? Would this ability help you in your day to day work? Is Python something you are already using but could improve upon? Machine Learning is the future and is here to stay. As such, you will want to know all the principles behind it, that will allow you to build your very own models and applications. But stuffy and long-winded books take time to read, so you probably want something that’s easier to digest. This book provides the clear and concise information you’ve been looking for. Full of well-defined details, concepts and examples, Python Machine Learning: The Ultimate Expert Guide to Learn Python Machine Learning Step by Step covers all your vital machine learning procedures, with chapters that include: • How advanced tensorflow can be used • Neural network models and how to get the most from them • Machine learning with Generative Adversarial Networks • Translating images with cross domain GANs • TF clusters and how to use them • How to debug TF models • And lots more… If you’ve been using Python for some time and want to become even better at it, then Python Machine Learning: The Ultimate Expert Guide is the first book you should be reading on the subject. Crammed with great tips, advice and strategies for making sure you are at the top of your game, this is a book that will change your Python experience for ever. Get your copy now!

Download or read book Hands On Transfer Learning with Python written by Dipanjan Sarkar and published by Packt Publishing Ltd. This book was released on 2018-08-31 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystem Key Features Build deep learning models with transfer learning principles in Python implement transfer learning to solve real-world research problems Perform complex operations such as image captioning neural style transfer Book Description Transfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems. The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples. The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of real-world case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP). By the end of this book, you will be able to implement both DL and transfer learning principles in your own systems. What you will learn Set up your own DL environment with graphics processing unit (GPU) and Cloud support Delve into transfer learning principles with ML and DL models Explore various DL architectures, including CNN, LSTM, and capsule networks Learn about data and network representation and loss functions Get to grips with models and strategies in transfer learning Walk through potential challenges in building complex transfer learning models from scratch Explore real-world research problems related to computer vision and audio analysis Understand how transfer learning can be leveraged in NLP Who this book is for Hands-On Transfer Learning with Python is for data scientists, machine learning engineers, analysts and developers with an interest in data and applying state-of-the-art transfer learning methodologies to solve tough real-world problems. Basic proficiency in machine learning and Python is required.

Download or read book Deep Learning with Python written by Francois Chollet and published by Simon and Schuster. This book was released on 2017-11-30 with total page 597 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. What's Inside Deep learning from first principles Setting up your own deep-learning environment Image-classification models Deep learning for text and sequences Neural style transfer, text generation, and image generation About the Reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others. Table of Contents PART 1 - FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2 - DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deep-learning best practices Generative deep learning Conclusions appendix A - Installing Keras and its dependencies on Ubuntu appendix B - Running Jupyter notebooks on an EC2 GPU instance

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Download or read book Advanced Deep Learning for Engineers and Scientists written by Kolla Bhanu Prakash and published by Springer Nature. This book was released on 2021-07-24 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a complete illustration of deep learning concepts with case-studies and practical examples useful for real time applications. This book introduces a broad range of topics in deep learning. The authors start with the fundamentals, architectures, tools needed for effective implementation for scientists. They then present technical exposure towards deep learning using Keras, Tensorflow, Pytorch and Python. They proceed with advanced concepts with hands-on sessions for deep learning. Engineers, scientists, researches looking for a practical approach to deep learning will enjoy this book. Presents practical basics to advanced concepts in deep learning and how to apply them through various projects; Discusses topics such as deep learning in smart grids and renewable energy & sustainable development; Explains how to implement advanced techniques in deep learning using Pytorch, Keras, Python programming.

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