Advanced Deep Learning with Python

Design and implement advanced next-generation AI solutions using TensorFlow and PyTorch

Author: Ivan Vasilev

Publisher: Packt Publishing Ltd

ISBN: 1789952719

Category: Computers

Page: 468

View: 7484

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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.
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Advanced Machine Learning with Python

Author: John Hearty

Publisher: Packt Publishing Ltd

ISBN: 1784393835

Category: Computers

Page: 278

View: 4149

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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.
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Advanced Machine Learning

Mastering Level Learning with Python

Author: Thomas Farth

Publisher: N.A

ISBN: 9781790120529

Category:

Page: 85

View: 5793

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Want to Become a Guru of Machine Learning? Have you just completed the book "An Intermediate's Guide to Machine Learning" Now it's time to go in Advanced Machine Learning Knowledge Advanced Machine Learning Hello! Welcome to this guide to advanced machine learning using Python & R. It's possible that you've picked this up with some initial interest, but aren't quite sure what to expect. In a nutshell, there has never been a more exciting time to learn and use machine learning techniques, and working in the field is only getting more rewarding. If you want to get up-to-speed with some of the more advanced Machine Learning techniques and gain experience using them to solve challenging problems, this is a good book for you! You will learn: Unsupervised Machine Learning Artificial Neural Networks & Convolutional Neural Networks Deep Learning Machine Learning with TensorFlow Pattern Recognition, Face Recognition & Image Recognition Python & R Codes for Machine Learning Algorithms Description: Interested in the field of Advanced Machine Learning? Then this book is for you! The rapid development of machine learning applications is fueled by an ongoing struggle to continually innovate, playing out at an array of research labs. The techniques developed by these pioneers are seeding new application areas and experiencing growing public awareness. While some of the innovations sought in AI and applied machine learning are still elusively far from readiness, others are a reality. Self-driving cars, sophisticated image recognition and altering capability, ever-greater strides in genetics research, and perhaps most pervasively of all, increasingly tailored content in our digital stores, e-mail inboxes, and online lives. Download your copy now so you can get started on what is promising to be a most amazing future.
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Hands-On Transfer Learning with Python

Implement advanced deep learning and neural network models using TensorFlow and Keras

Author: Dipanjan Sarkar,Raghav Bali,Tamoghna Ghosh

Publisher: Packt Publishing Ltd

ISBN: 1788839056

Category: Computers

Page: 438

View: 6624

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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.
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Advanced Deep Learning with TensorFlow 2 and Keras

Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more, 2nd Edition

Author: Rowel Atienza

Publisher: Packt Publishing Ltd

ISBN: 183882572X

Category: Computers

Page: 512

View: 6657

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Updated and revised second edition of the bestselling guide to advanced deep learning with TensorFlow 2 and Keras Key Features Explore the most advanced deep learning techniques that drive modern AI results New coverage of unsupervised deep learning using mutual information, object detection, and semantic segmentation Completely updated for TensorFlow 2.x Book Description Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects. Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques. Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can unlock new levels of AI performance. Next, you’ll discover how a variational autoencoder (VAE) is implemented, and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You'll also learn to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI. What you will learn Use mutual information maximization techniques to perform unsupervised learning Use segmentation to identify the pixel-wise class of each object in an image Identify both the bounding box and class of objects in an image using object detection Learn the building blocks for advanced techniques - MLPss, CNN, and RNNs Understand deep neural networks - including ResNet and DenseNet Understand and build autoregressive models – autoencoders, VAEs, and GANs Discover and implement deep reinforcement learning methods Who this book is for This is not an introductory book, so fluency with Python is required. The reader should also be familiar with some machine learning approaches, and practical experience with DL will also be helpful. Knowledge of Keras or TensorFlow 2.0 is not required but is recommended.
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Machine Learning With Python

3 Books in 1 - The Ultimate Beginners Guide & a Comprehensive Guide of Tips and Tricks & Advanced and Effective Strategies Using Machine Learning with Python

Author: Ethan Williams

Publisher: N.A

ISBN: N.A

Category:

Page: 534

View: 344

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Introduction 1MACHINE LEARNING WITH PYTHON - The Ultimate Beginners Guide to Learn Machine Learning with Python Step by StepWe live in a world of data deluge where gigabytes of data are generated daily. It is possible that this data might not be very useful for our daily applications. Major setbacks in the use of such data may be due to the presence of loopholes in data links previously generated or the data might be too vast for the limited human mind. Machine learning in this book presents some of the solutions to the problems above. Being an introductory guide, expect to learn the various basics involved in Machine Learning and Python. This book provides an insight into the new world of big data, then behooves you to learn more about Machine Learning. You will be able to get answers to the following questions: -What is Machine Learning and what does it entail? -How can I apply machine learning to have a glimpse into the new world, power my enterprise or find out how the Internet thinks about my academic research work? Be ready to learn all that it takes to be an expert in the field of Machine Learning!Introduction 2MACHINE LEARNING WITH PYTHON - Comprehensive Guide of Tips and Tricks of using Machine Learning Theories with PythonMachine learning is a branch of artificial intelligence that designs algorithms that improve their performance based on empirical data. Machine learning is one of the most active and exciting fields of computer science today, mainly because of its many application options ranging from pattern recognition and in-depth data analysis to robotics, computational vision, bioinformatics, and computational linguistics. Machine learning is above all a discipline that can contribute to many domains and has very challenging applications. This is the area where most publications in academia are concerned with artificial intelligence, and all major companies, such as Google, Facebook or Microsoft, apply machine learning methods in their applications. This book covers the theory, principles and tricks to machine learning and provides an overview of its applications in Python.In the field of data science, it comes quite natural that you should learn Python. If you're wondering why Python is the answer, the answer is that there are already ready packages (statistical and numerical) for analyzing data such as PyBrain, NumPy, and MySQL. Machine learning integrates computers and statistics that allow computers to learn new tasks. There are Python modules - such as Scikit-learn, Tensorflow, and Theano - that support machine learning so that you can do cool things such as spam detection and fingerprint identification. So these are some of the concepts that you will master reading this book.Introduction 3MACHINE LEARNING WITH PYTHON - Advanced and Effective Strategies Using Machine Learning with Python TheoriesAre you eager to use advanced Machine Learning methods with Python? Are you looking forward to automating simple things using the power of the keyboard, but you have no idea how to achieve it?Machine learning is a vast field and expanding at supersonic speed. Python evolution is an ongoing process and lives up to the hype. The field goes beyond robotics and data finance to finance applications.When you use machine learning and python programming in the right way, they have the capability of changing the lives of people around the world. In this advanced book, we are going to break down the advanced features of this new technology to advance your skills as an IT enthusiast. You will discover: -How we classify machine learning algorithms-How we can apply machine learning in different areas-Understanding the artificial neural networks-The use of convoluted neural networks-Building predictive models-Autoencoders in ML and Python-K-Means techniques and Natural Language Processing-The art of feature engineering-The ensemble methods
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Next-Generation Wireless Networks Meet Advanced Machine Learning Applications

Author: Com?a, Ioan-Sorin,Trestian, Ramona

Publisher: IGI Global

ISBN: 152257459X

Category: Technology & Engineering

Page: 356

View: 2559

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The ever-evolving wireless technology industry is demanding new technologies and standards to ensure a higher quality of experience for global end-users. This developing challenge has enabled researchers to identify the present trend of machine learning as a possible solution, but will it meet business velocity demand? Next-Generation Wireless Networks Meet Advanced Machine Learning Applications is a pivotal reference source that provides emerging trends and insights into various technologies of next-generation wireless networks to enable the dynamic optimization of system configuration and applications within the fields of wireless networks, broadband networks, and wireless communication. Featuring coverage on a broad range of topics such as machine learning, hybrid network environments, wireless communications, and the internet of things; this publication is ideally designed for industry experts, researchers, students, academicians, and practitioners seeking current research on various technologies of next-generation wireless networks.
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Practical Convolutional Neural Networks

Implement advanced deep learning models using Python

Author: Mohit Sewak,Md. Rezaul Karim,Pradeep Pujari

Publisher: Packt Publishing Ltd

ISBN: 1788394143

Category: Computers

Page: 218

View: 2494

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One stop guide to implementing award-winning, and cutting-edge CNN architectures Key Features Fast-paced guide with use cases and real-world examples to get well versed with CNN techniques Implement CNN models on image classification, transfer learning, Object Detection, Instance Segmentation, GANs and more Implement powerful use-cases like image captioning, reinforcement learning for hard attention, and recurrent attention models Book Description Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, medical discoveries, innovative eCommerce and more.You will learn to create innovative solutions around image and video analytics to solve complex machine learning and computer vision related problems and implement real-life CNN models. This book starts with an overview of deep neural networkswith the example of image classification and walks you through building your first CNN for human face detector. We will learn to use concepts like transfer learning with CNN, and Auto-Encoders to build very powerful models, even when not much of supervised training data of labeled images is available. Later we build upon the learning achieved to build advanced vision related algorithms for object detection, instance segmentation, generative adversarial networks, image captioning, attention mechanisms for vision, and recurrent models for vision. By the end of this book, you should be ready to implement advanced, effective and efficient CNN models at your professional project or personal initiatives by working on complex image and video datasets. What you will learn From CNN basic building blocks to advanced concepts understand practical areas they can be applied to Build an image classifier CNN model to understand how different components interact with each other, and then learn how to optimize it Learn different algorithms that can be applied to Object Detection, and Instance Segmentation Learn advanced concepts like attention mechanisms for CNN to improve prediction accuracy Understand transfer learning and implement award-winning CNN architectures like AlexNet, VGG, GoogLeNet, ResNet and more Understand the working of generative adversarial networks and how it can create new, unseen images Who this book is for This book is for data scientists, machine learning and deep learning practitioners, Cognitive and Artificial Intelligence enthusiasts who want to move one step further in building Convolutional Neural Networks. Get hands-on experience with extreme datasets and different CNN architectures to build efficient and smart ConvNet models. Basic knowledge of deep learning concepts and Python programming language is expected.
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Hands-On Deep Learning Algorithms with Python

Master deep learning algorithms with extensive math by implementing them using TensorFlow

Author: Sudharsan Ravichandiran

Publisher: Packt Publishing Ltd

ISBN: 1789344514

Category: Computers

Page: 512

View: 8310

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