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Hands-On Mathematics for Deep Learning

Hands-On Mathematics for Deep Learning PDF Author: Jay Dawani
Publisher: Packt Publishing Ltd
ISBN: 183864184X
Category : Computers
Languages : en
Pages : 364

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Book Description
A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architectures Key Features Understand linear algebra, calculus, gradient algorithms, and other concepts essential for training deep neural networks Learn the mathematical concepts needed to understand how deep learning models function Use deep learning for solving problems related to vision, image, text, and sequence applications Book Description Most programmers and data scientists struggle with mathematics, having either overlooked or forgotten core mathematical concepts. This book uses Python libraries to help you understand the math required to build deep learning (DL) models. You'll begin by learning about core mathematical and modern computational techniques used to design and implement DL algorithms. This book will cover essential topics, such as linear algebra, eigenvalues and eigenvectors, the singular value decomposition concept, and gradient algorithms, to help you understand how to train deep neural networks. Later chapters focus on important neural networks, such as the linear neural network and multilayer perceptrons, with a primary focus on helping you learn how each model works. As you advance, you will delve into the math used for regularization, multi-layered DL, forward propagation, optimization, and backpropagation techniques to understand what it takes to build full-fledged DL models. Finally, you’ll explore CNN, recurrent neural network (RNN), and GAN models and their application. By the end of this book, you'll have built a strong foundation in neural networks and DL mathematical concepts, which will help you to confidently research and build custom models in DL. What you will learn Understand the key mathematical concepts for building neural network models Discover core multivariable calculus concepts Improve the performance of deep learning models using optimization techniques Cover optimization algorithms, from basic stochastic gradient descent (SGD) to the advanced Adam optimizer Understand computational graphs and their importance in DL Explore the backpropagation algorithm to reduce output error Cover DL algorithms such as convolutional neural networks (CNNs), sequence models, and generative adversarial networks (GANs) Who this book is for This book is for data scientists, machine learning developers, aspiring deep learning developers, or anyone who wants to understand the foundation of deep learning by learning the math behind it. Working knowledge of the Python programming language and machine learning basics is required.

Hands-On Mathematics for Deep Learning

Hands-On Mathematics for Deep Learning PDF Author: Jay Dawani
Publisher: Packt Publishing Ltd
ISBN: 183864184X
Category : Computers
Languages : en
Pages : 364

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Book Description
A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architectures Key Features Understand linear algebra, calculus, gradient algorithms, and other concepts essential for training deep neural networks Learn the mathematical concepts needed to understand how deep learning models function Use deep learning for solving problems related to vision, image, text, and sequence applications Book Description Most programmers and data scientists struggle with mathematics, having either overlooked or forgotten core mathematical concepts. This book uses Python libraries to help you understand the math required to build deep learning (DL) models. You'll begin by learning about core mathematical and modern computational techniques used to design and implement DL algorithms. This book will cover essential topics, such as linear algebra, eigenvalues and eigenvectors, the singular value decomposition concept, and gradient algorithms, to help you understand how to train deep neural networks. Later chapters focus on important neural networks, such as the linear neural network and multilayer perceptrons, with a primary focus on helping you learn how each model works. As you advance, you will delve into the math used for regularization, multi-layered DL, forward propagation, optimization, and backpropagation techniques to understand what it takes to build full-fledged DL models. Finally, you’ll explore CNN, recurrent neural network (RNN), and GAN models and their application. By the end of this book, you'll have built a strong foundation in neural networks and DL mathematical concepts, which will help you to confidently research and build custom models in DL. What you will learn Understand the key mathematical concepts for building neural network models Discover core multivariable calculus concepts Improve the performance of deep learning models using optimization techniques Cover optimization algorithms, from basic stochastic gradient descent (SGD) to the advanced Adam optimizer Understand computational graphs and their importance in DL Explore the backpropagation algorithm to reduce output error Cover DL algorithms such as convolutional neural networks (CNNs), sequence models, and generative adversarial networks (GANs) Who this book is for This book is for data scientists, machine learning developers, aspiring deep learning developers, or anyone who wants to understand the foundation of deep learning by learning the math behind it. Working knowledge of the Python programming language and machine learning basics is required.

Hands-On Deep Learning Algorithms with Python

Hands-On Deep Learning Algorithms with Python PDF Author: Sudharsan Ravichandiran
Publisher: Packt Publishing Ltd
ISBN: 1789344514
Category : Computers
Languages : en
Pages : 512

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Book Description
This book introduces basic-to-advanced deep learning algorithms used in a production environment by AI researchers and principal data scientists; it explains algorithms intuitively, including the underlying math, and shows how to implement them using popular Python-based deep learning libraries such as TensorFlow.

Math for Deep Learning

Math for Deep Learning PDF Author: Ronald Kneusel
Publisher:
ISBN: 9781098129101
Category :
Languages : en
Pages : 344

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Book Description
Deep learning is everywhere, making this powerful driver of AI something more STEM professionals need to know. Learning which library commands to use is one thing, but to truly understand the discipline, you need to grasp the mathematical concepts that make it tick. This book will give you a working knowledge of topics in probability, statistics, linear algebra, and differential calculus - the essential math needed to make deep learning comprehensible, which is key to practicing it successfully. Each of the four subfields are contextualized with Python code and hands-on, real-world examples that bridge the gap between pure mathematics and its applications in deep learning. Chapters build upon one another, with foundational topics such as Bayes' theorem followed by more advanced concepts, like training neural networks using vectors, matrices, and derivatives of functions. You'll ultimately put all this math to use as you explore and implement deep learning algorithms, including backpropagation and gradient descent - the foundational algorithms that have enabled the AI revolution. You'll learn: •The rules of probability, probability distributions, and Bayesian probability •The use of statistics for understanding datasets and evaluating models •How to manipulate vectors and matrices, and use both to move data through a neural network •How to use linear algebra to implement principal component analysis and singular value decomposition •How to apply improved versions of gradient descent, like RMSprop, Adagrad and Adadelta Once you understand the core math concepts presented throughout this book through the lens of AI programming, you'll have foundational know-how to easily follow and work with deep learning.

PyTorch Deep Learning Hands-On

PyTorch Deep Learning Hands-On PDF Author: Sherin Thomas
Publisher: Packt Publishing Ltd
ISBN: 1788833430
Category : Computers
Languages : en
Pages : 250

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Book Description
Hands-on projects cover all the key deep learning methods built step-by-step in PyTorch Key Features Internals and principles of PyTorch Implement key deep learning methods in PyTorch: CNNs, GANs, RNNs, reinforcement learning, and more Build deep learning workflows and take deep learning models from prototyping to production Book Description PyTorch Deep Learning Hands-On is a book for engineers who want a fast-paced guide to doing deep learning work with Pytorch. It is not an academic textbook and does not try to teach deep learning principles. The book will help you most if you want to get your hands dirty and put PyTorch to work quickly. PyTorch Deep Learning Hands-On shows how to implement the major deep learning architectures in PyTorch. It covers neural networks, computer vision, CNNs, natural language processing (RNN), GANs, and reinforcement learning. You will also build deep learning workflows with the PyTorch framework, migrate models built in Python to highly efficient TorchScript, and deploy to production using the most sophisticated available tools. Each chapter focuses on a different area of deep learning. Chapters start with a refresher on how the model works, before sharing the code you need to implement them in PyTorch. This book is ideal if you want to rapidly add PyTorch to your deep learning toolset. What you will learn Use PyTorch to build: Simple Neural Networks – build neural networks the PyTorch way, with high-level functions, optimizers, and more Convolutional Neural Networks – create advanced computer vision systems Recurrent Neural Networks – work with sequential data such as natural language and audio Generative Adversarial Networks – create new content with models including SimpleGAN and CycleGAN Reinforcement Learning – develop systems that can solve complex problems such as driving or game playing Deep Learning workflows – move effectively from ideation to production with proper deep learning workflow using PyTorch and its utility packages Production-ready models – package your models for high-performance production environments Who this book is for Machine learning engineers who want to put PyTorch to work.

Neuronale Netze Selbst Programmieren

Neuronale Netze Selbst Programmieren PDF Author: Tariq Rashid
Publisher:
ISBN: 9781492064046
Category :
Languages : de
Pages : 232

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Book Description
Neuronale Netze sind Schlüsselelemente des Deep Learning und der Künstlichen Intelligenz, die heute zu Erstaunlichem in der Lage sind. Dennoch verstehen nur wenige, wie Neuronale Netze tatsächlich funktionieren. Dieses Buch nimmt Sie mit auf eine unterhaltsame Reise, die mit ganz einfachen Ideen beginnt und Ihnen Schritt für Schritt zeigt, wie Neuronale Netze arbeiten. Dafür brauchen Sie keine tieferen Mathematik-Kenntnisse, denn alle mathematischen Konzepte werden behutsam und mit vielen Illustrationen erläutert. Dann geht es in die Praxis: Sie programmieren Ihr eigenes Neuronales Netz mit Python und bringen ihm bei, handgeschriebene Zahlen zu erkennen, bis es eine Performance wie ein professionell entwickeltes Netz erreicht. Zum Schluss lassen Sie das Netz noch auf einem Raspberry Pi Zero laufen. - Tariq Rashid hat eine besondere Fähigkeit, schwierige Konzepte verständlich zu erklären, dadurch werden Neuronale Netze für jeden Interessierten zugänglich und praktisch nachvollziehbar.

Deep Learning By Example

Deep Learning By Example PDF Author: Ahmed Menshawy
Publisher: Packt Publishing Ltd
ISBN: 178839576X
Category : Computers
Languages : en
Pages : 450

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Book Description
Grasp the fundamental concepts of deep learning using Tensorflow in a hands-on manner Key Features Get a first-hand experience of the deep learning concepts and techniques with this easy-to-follow guide Train different types of neural networks using Tensorflow for real-world problems in language processing, computer vision, transfer learning, and more Designed for those who believe in the concept of 'learn by doing', this book is a perfect blend of theory and code examples Book Description Deep learning is a popular subset of machine learning, and it allows you to build complex models that are faster and give more accurate predictions. This book is your companion to take your first steps into the world of deep learning, with hands-on examples to boost your understanding of the topic. This book starts with a quick overview of the essential concepts of data science and machine learning which are required to get started with deep learning. It introduces you to Tensorflow, the most widely used machine learning library for training deep learning models. You will then work on your first deep learning problem by training a deep feed-forward neural network for digit classification, and move on to tackle other real-world problems in computer vision, language processing, sentiment analysis, and more. Advanced deep learning models such as generative adversarial networks and their applications are also covered in this book. By the end of this book, you will have a solid understanding of all the essential concepts in deep learning. With the help of the examples and code provided in this book, you will be equipped to train your own deep learning models with more confidence. What you will learn Understand the fundamentals of deep learning and how it is different from machine learning Get familiarized with Tensorflow, one of the most popular libraries for advanced machine learning Increase the predictive power of your model using feature engineering Understand the basics of deep learning by solving a digit classification problem of MNIST Demonstrate face generation based on the CelebA database, a promising application of generative models Apply deep learning to other domains like language modeling, sentiment analysis, and machine translation Who this book is for This book targets data scientists and machine learning developers who wish to get started with deep learning. If you know what deep learning is but are not quite sure of how to use it, this book will help you as well. An understanding of statistics and data science concepts is required. Some familiarity with Python programming will also be beneficial.

Hands-On Transfer Learning with Python

Hands-On Transfer Learning with Python PDF Author: Dipanjan Sarkar
Publisher: Packt Publishing Ltd
ISBN: 1788839056
Category : Computers
Languages : en
Pages : 438

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Book Description
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.

R Deep Learning Projects

R Deep Learning Projects PDF Author: Yuxi (Hayden) Liu
Publisher: Packt Publishing Ltd
ISBN: 1788474554
Category : Mathematics
Languages : en
Pages : 258

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Book Description
5 real-world projects to help you master deep learning concepts Key Features Master the different deep learning paradigms and build real-world projects related to text generation, sentiment analysis, fraud detection, and more Get to grips with R's impressive range of Deep Learning libraries and frameworks such as deepnet, MXNetR, Tensorflow, H2O, Keras, and text2vec Practical projects that show you how to implement different neural networks with helpful tips, tricks, and best practices Book Description R is a popular programming language used by statisticians and mathematicians for statistical analysis, and is popularly used for deep learning. Deep Learning, as we all know, is one of the trending topics today, and is finding practical applications in a lot of domains. This book demonstrates end-to-end implementations of five real-world projects on popular topics in deep learning such as handwritten digit recognition, traffic light detection, fraud detection, text generation, and sentiment analysis. You'll learn how to train effective neural networks in R—including convolutional neural networks, recurrent neural networks, and LSTMs—and apply them in practical scenarios. The book also highlights how neural networks can be trained using GPU capabilities. You will use popular R libraries and packages—such as MXNetR, H2O, deepnet, and more—to implement the projects. By the end of this book, you will have a better understanding of deep learning concepts and techniques and how to use them in a practical setting. What you will learn Instrument Deep Learning models with packages such as deepnet, MXNetR, Tensorflow, H2O, Keras, and text2vec Apply neural networks to perform handwritten digit recognition using MXNet Get the knack of CNN models, Neural Network API, Keras, and TensorFlow for traffic sign classification -Implement credit card fraud detection with Autoencoders Master reconstructing images using variational autoencoders Wade through sentiment analysis from movie reviews Run from past to future and vice versa with bidirectional Long Short-Term Memory (LSTM) networks Understand the applications of Autoencoder Neural Networks in clustering and dimensionality reduction Who this book is for Machine learning professionals and data scientists looking to master deep learning by implementing practical projects in R will find this book a useful resource. A knowledge of R programming and the basic concepts of deep learning is required to get the best out of this book.

Practical Discrete Mathematics

Practical Discrete Mathematics PDF Author: Ryan T. White
Publisher: Packt Publishing Ltd
ISBN: 1838983503
Category : Mathematics
Languages : en
Pages : 330

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Book Description
A practical guide simplifying discrete math for curious minds and demonstrating its application in solving problems related to software development, computer algorithms, and data science Key FeaturesApply the math of countable objects to practical problems in computer scienceExplore modern Python libraries such as scikit-learn, NumPy, and SciPy for performing mathematicsLearn complex statistical and mathematical concepts with the help of hands-on examples and expert guidanceBook Description Discrete mathematics deals with studying countable, distinct elements, and its principles are widely used in building algorithms for computer science and data science. The knowledge of discrete math concepts will help you understand the algorithms, binary, and general mathematics that sit at the core of data-driven tasks. Practical Discrete Mathematics is a comprehensive introduction for those who are new to the mathematics of countable objects. This book will help you get up to speed with using discrete math principles to take your computer science skills to a more advanced level. As you learn the language of discrete mathematics, you'll also cover methods crucial to studying and describing computer science and machine learning objects and algorithms. The chapters that follow will guide you through how memory and CPUs work. In addition to this, you'll understand how to analyze data for useful patterns, before finally exploring how to apply math concepts in network routing, web searching, and data science. By the end of this book, you'll have a deeper understanding of discrete math and its applications in computer science, and be ready to work on real-world algorithm development and machine learning. What you will learnUnderstand the terminology and methods in discrete math and their usage in algorithms and data problemsUse Boolean algebra in formal logic and elementary control structuresImplement combinatorics to measure computational complexity and manage memory allocationUse random variables, calculate descriptive statistics, and find average-case computational complexitySolve graph problems involved in routing, pathfinding, and graph searches, such as depth-first searchPerform ML tasks such as data visualization, regression, and dimensionality reductionWho this book is for This book is for computer scientists looking to expand their knowledge of discrete math, the core topic of their field. University students looking to get hands-on with computer science, mathematics, statistics, engineering, or related disciplines will also find this book useful. Basic Python programming skills and knowledge of elementary real-number algebra are required to get started with this book.

Hands-On Python Deep Learning for the Web

Hands-On Python Deep Learning for the Web PDF Author: Anubhav Singh
Publisher: Packt Publishing Ltd
ISBN: 1789953790
Category : Computers
Languages : en
Pages : 404

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Book Description
Use the power of deep learning with Python to build and deploy intelligent web applications Key Features Create next-generation intelligent web applications using Python libraries such as Flask and Django Implement deep learning algorithms and techniques for performing smart web automation Integrate neural network architectures to create powerful full-stack web applications Book Description When used effectively, deep learning techniques can help you develop intelligent web apps. In this book, you'll cover the latest tools and technological practices that are being used to implement deep learning in web development using Python. Starting with the fundamentals of machine learning, you'll focus on DL and the basics of neural networks, including common variants such as convolutional neural networks (CNNs). You'll learn how to integrate them into websites with the frontends of different standard web tech stacks. The book then helps you gain practical experience of developing a deep learning-enabled web app using Python libraries such as Django and Flask by creating RESTful APIs for custom models. Later, you'll explore how to set up a cloud environment for deep learning-based web deployments on Google Cloud and Amazon Web Services (AWS). Next, you'll learn how to use Microsoft's intelligent Emotion API, which can detect a person's emotions through a picture of their face. You'll also get to grips with deploying real-world websites, in addition to learning how to secure websites using reCAPTCHA and Cloudflare. Finally, you'll use NLP to integrate a voice UX through Dialogflow on your web pages. By the end of this book, you'll have learned how to deploy intelligent web apps and websites with the help of effective tools and practices. What you will learn Explore deep learning models and implement them in your browser Design a smart web-based client using Django and Flask Work with different Python-based APIs for performing deep learning tasks Implement popular neural network models with TensorFlow.js Design and build deep web services on the cloud using deep learning Get familiar with the standard workflow of taking deep learning models into production Who this book is for This deep learning book is for data scientists, machine learning practitioners, and deep learning engineers who are looking to perform deep learning techniques and methodologies on the web. You will also find this book useful if you’re a web developer who wants to implement smart techniques in the browser to make it more interactive. Working knowledge of the Python programming language and basic machine learning techniques will be beneficial.