The Deep Learning Workshop

Download The Deep Learning Workshop full books in PDF, epub, and Kindle. Read online The Deep Learning Workshop ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

The Deep Learning Workshop

Author : Mirza Rahim Baig
Publisher : Packt Publishing Ltd
ISBN 13 : 1839210567
Total Pages : 473 pages
Book Rating : 4.8/5 (392 download)

DOWNLOAD NOW!

Book Synopsis The Deep Learning Workshop by : Mirza Rahim Baig

Download or read book The Deep Learning Workshop written by Mirza Rahim Baig and published by Packt Publishing Ltd. This book was released on 2020-07-31 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take a hands-on approach to understanding deep learning and build smart applications that can recognize images and interpret text Key Features Understand how to implement deep learning with TensorFlow and Keras Learn the fundamentals of computer vision and image recognition Study the architecture of different neural networks Book Description Are you fascinated by how deep learning powers intelligent applications such as self-driving cars, virtual assistants, facial recognition devices, and chatbots to process data and solve complex problems? Whether you are familiar with machine learning or are new to this domain, The Deep Learning Workshop will make it easy for you to understand deep learning with the help of interesting examples and exercises throughout. The book starts by highlighting the relationship between deep learning, machine learning, and artificial intelligence and helps you get comfortable with the TensorFlow 2.0 programming structure using hands-on exercises. You'll understand neural networks, the structure of a perceptron, and how to use TensorFlow to create and train models. The book will then let you explore the fundamentals of computer vision by performing image recognition exercises with convolutional neural networks (CNNs) using Keras. As you advance, you'll be able to make your model more powerful by implementing text embedding and sequencing the data using popular deep learning solutions. Finally, you'll get to grips with bidirectional recurrent neural networks (RNNs) and build generative adversarial networks (GANs) for image synthesis. By the end of this deep learning book, you'll have learned the skills essential for building deep learning models with TensorFlow and Keras. What you will learn Understand how deep learning, machine learning, and artificial intelligence are different Develop multilayer deep neural networks with TensorFlow Implement deep neural networks for multiclass classification using Keras Train CNN models for image recognition Handle sequence data and use it in conjunction with RNNs Build a GAN to generate high-quality synthesized images Who this book is for If you are interested in machine learning and want to create and train deep learning models using TensorFlow and Keras, this workshop is for you. A solid understanding of Python and its packages, along with basic machine learning concepts, will help you to learn the topics quickly.

The The Machine Learning Workshop

Author : Hyatt Saleh
Publisher : Packt Publishing Ltd
ISBN 13 : 1838985468
Total Pages : 285 pages
Book Rating : 4.8/5 (389 download)

DOWNLOAD NOW!

Book Synopsis The The Machine Learning Workshop by : Hyatt Saleh

Download or read book The The Machine Learning Workshop written by Hyatt Saleh and published by Packt Publishing Ltd. This book was released on 2020-07-22 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take a comprehensive and step-by-step approach to understanding machine learning Key FeaturesDiscover how to apply the scikit-learn uniform API in all types of machine learning modelsUnderstand the difference between supervised and unsupervised learning modelsReinforce your understanding of machine learning concepts by working on real-world examplesBook Description Machine learning algorithms are an integral part of almost all modern applications. To make the learning process faster and more accurate, you need a tool flexible and powerful enough to help you build machine learning algorithms quickly and easily. With The Machine Learning Workshop, you'll master the scikit-learn library and become proficient in developing clever machine learning algorithms. The Machine Learning Workshop begins by demonstrating how unsupervised and supervised learning algorithms work by analyzing a real-world dataset of wholesale customers. Once you've got to grips with the basics, you’ll develop an artificial neural network using scikit-learn and then improve its performance by fine-tuning hyperparameters. Towards the end of the workshop, you'll study the dataset of a bank's marketing activities and build machine learning models that can list clients who are likely to subscribe to a term deposit. You'll also learn how to compare these models and select the optimal one. By the end of The Machine Learning Workshop, you'll not only have learned the difference between supervised and unsupervised models and their applications in the real world, but you'll also have developed the skills required to get started with programming your very own machine learning algorithms. What you will learnUnderstand how to select an algorithm that best fits your dataset and desired outcomeExplore popular real-world algorithms such as K-means, Mean-Shift, and DBSCANDiscover different approaches to solve machine learning classification problemsDevelop neural network structures using the scikit-learn packageUse the NN algorithm to create models for predicting future outcomesPerform error analysis to improve your model's performanceWho this book is for The Machine Learning Workshop is perfect for machine learning beginners. You will need Python programming experience, though no prior knowledge of scikit-learn and machine learning is necessary.

The Deep Learning with Keras Workshop

Author : Matthew Moocarme
Publisher : Packt Publishing Ltd
ISBN 13 : 1800564759
Total Pages : 495 pages
Book Rating : 4.8/5 (5 download)

DOWNLOAD NOW!

Book Synopsis The Deep Learning with Keras Workshop by : Matthew Moocarme

Download or read book The Deep Learning with Keras Workshop written by Matthew Moocarme and published by Packt Publishing Ltd. This book was released on 2020-07-29 with total page 495 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover how to leverage Keras, the powerful and easy-to-use open source Python library for developing and evaluating deep learning models Key FeaturesGet to grips with various model evaluation metrics, including sensitivity, specificity, and AUC scoresExplore advanced concepts such as sequential memory and sequential modelingReinforce your skills with real-world development, screencasts, and knowledge checksBook Description New experiences can be intimidating, but not this one! This beginner's guide to deep learning is here to help you explore deep learning from scratch with Keras, and be on your way to training your first ever neural networks. What sets Keras apart from other deep learning frameworks is its simplicity. With over two hundred thousand users, Keras has a stronger adoption in industry and the research community than any other deep learning framework. The Deep Learning with Keras Workshop starts by introducing you to the fundamental concepts of machine learning using the scikit-learn package. After learning how to perform the linear transformations that are necessary for building neural networks, you'll build your first neural network with the Keras library. As you advance, you'll learn how to build multi-layer neural networks and recognize when your model is underfitting or overfitting to the training data. With the help of practical exercises, you'll learn to use cross-validation techniques to evaluate your models and then choose the optimal hyperparameters to fine-tune their performance. Finally, you'll explore recurrent neural networks and learn how to train them to predict values in sequential data. By the end of this book, you'll have developed the skills you need to confidently train your own neural network models. What you will learnGain insights into the fundamentals of neural networksUnderstand the limitations of machine learning and how it differs from deep learningBuild image classifiers with convolutional neural networksEvaluate, tweak, and improve your models with techniques such as cross-validationCreate prediction models to detect data patterns and make predictionsImprove model accuracy with L1, L2, and dropout regularizationWho this book is for If you know the basics of data science and machine learning and want to get started with advanced machine learning technologies like artificial neural networks and deep learning, then this is the book for you. To grasp the concepts explained in this deep learning book more effectively, prior experience in Python programming and some familiarity with statistics and logistic regression are a must.

The The Deep Learning with PyTorch Workshop

Author : Hyatt Saleh
Publisher : Packt Publishing Ltd
ISBN 13 : 1838981845
Total Pages : 329 pages
Book Rating : 4.8/5 (389 download)

DOWNLOAD NOW!

Book Synopsis The The Deep Learning with PyTorch Workshop by : Hyatt Saleh

Download or read book The The Deep Learning with PyTorch Workshop written by Hyatt Saleh and published by Packt Publishing Ltd. This book was released on 2020-07-22 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get a head start in the world of AI and deep learning by developing your skills with PyTorch Key FeaturesLearn how to define your own network architecture in deep learningImplement helpful methods to create and train a model using PyTorch syntaxDiscover how intelligent applications using features like image recognition and speech recognition really process your dataBook Description Want to get to grips with one of the most popular machine learning libraries for deep learning? The Deep Learning with PyTorch Workshop will help you do just that, jumpstarting your knowledge of using PyTorch for deep learning even if you’re starting from scratch. It's no surprise that deep learning's popularity has risen steeply in the past few years, thanks to intelligent applications such as self-driving vehicles, chatbots, and voice-activated assistants that are making our lives easier. This book will take you inside the world of deep learning, where you'll use PyTorch to understand the complexity of neural network architectures. The Deep Learning with PyTorch Workshop starts with an introduction to deep learning and its applications. You'll explore the syntax of PyTorch and learn how to define a network architecture and train a model. Next, you'll learn about three main neural network architectures - convolutional, artificial, and recurrent - and even solve real-world data problems using these networks. Later chapters will show you how to create a style transfer model to develop a new image from two images, before finally taking you through how RNNs store memory to solve key data issues. By the end of this book, you'll have mastered the essential concepts, tools, and libraries of PyTorch to develop your own deep neural networks and intelligent apps. What you will learnExplore the different applications of deep learningUnderstand the PyTorch approach to building neural networksCreate and train your very own perceptron using PyTorchSolve regression problems using artificial neural networks (ANNs)Handle computer vision problems with convolutional neural networks (CNNs)Perform language translation tasks using recurrent neural networks (RNNs)Who this book is for This deep learning book is ideal for anyone who wants to create and train deep learning models using PyTorch. A solid understanding of the Python programming language and its packages will help you grasp the topics covered in the book more quickly.

The TensorFlow Workshop

Author : Matthew Moocarme
Publisher : Packt Publishing Ltd
ISBN 13 : 1800200226
Total Pages : 601 pages
Book Rating : 4.8/5 (2 download)

DOWNLOAD NOW!

Book Synopsis The TensorFlow Workshop by : Matthew Moocarme

Download or read book The TensorFlow Workshop written by Matthew Moocarme and published by Packt Publishing Ltd. This book was released on 2021-12-15 with total page 601 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get started with TensorFlow fundamentals to build and train deep learning models with real-world data, practical exercises, and challenging activities Key FeaturesUnderstand the fundamentals of tensors, neural networks, and deep learningDiscover how to implement and fine-tune deep learning models for real-world datasetsBuild your experience and confidence with hands-on exercises and activitiesBook Description Getting to grips with tensors, deep learning, and neural networks can be intimidating and confusing for anyone, no matter their experience level. The breadth of information out there, often written at a very high level and aimed at advanced practitioners, can make getting started even more challenging. If this sounds familiar to you, The TensorFlow Workshop is here to help. Combining clear explanations, realistic examples, and plenty of hands-on practice, it'll quickly get you up and running. You'll start off with the basics – learning how to load data into TensorFlow, perform tensor operations, and utilize common optimizers and activation functions. As you progress, you'll experiment with different TensorFlow development tools, including TensorBoard, TensorFlow Hub, and Google Colab, before moving on to solve regression and classification problems with sequential models. Building on this solid foundation, you'll learn how to tune models and work with different types of neural network, getting hands-on with real-world deep learning applications such as text encoding, temperature forecasting, image augmentation, and audio processing. By the end of this deep learning book, you'll have the skills, knowledge, and confidence to tackle your own ambitious deep learning projects with TensorFlow. What you will learnGet to grips with TensorFlow's mathematical operationsPre-process a wide variety of tabular, sequential, and image dataUnderstand the purpose and usage of different deep learning layersPerform hyperparameter-tuning to prevent overfitting of training dataUse pre-trained models to speed up the development of learning modelsGenerate new data based on existing patterns using generative modelsWho this book is for This TensorFlow book is for anyone who wants to develop their understanding of deep learning and get started building neural networks with TensorFlow. Basic knowledge of Python programming and its libraries, as well as a general understanding of the fundamentals of data science and machine learning, will help you grasp the topics covered in this book more easily.

The The Reinforcement Learning Workshop

Author : Alessandro Palmas
Publisher : Packt Publishing Ltd
ISBN 13 : 1800209967
Total Pages : 821 pages
Book Rating : 4.8/5 (2 download)

DOWNLOAD NOW!

Book Synopsis The The Reinforcement Learning Workshop by : Alessandro Palmas

Download or read book The The Reinforcement Learning Workshop written by Alessandro Palmas and published by Packt Publishing Ltd. This book was released on 2020-08-18 with total page 821 pages. Available in PDF, EPUB and Kindle. Book excerpt: Start with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and policy-based methods with this practical guide Key FeaturesUse TensorFlow to write reinforcement learning agents for performing challenging tasksLearn how to solve finite Markov decision problemsTrain models to understand popular video games like BreakoutBook Description Various intelligent applications such as video games, inventory management software, warehouse robots, and translation tools use reinforcement learning (RL) to make decisions and perform actions that maximize the probability of the desired outcome. This book will help you to get to grips with the techniques and the algorithms for implementing RL in your machine learning models. Starting with an introduction to RL, you’ll be guided through different RL environments and frameworks. You’ll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once you’ve explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, you’ll understand when to apply the different deep learning methods in RL and advance to deep Q-learning. The book will even help you understand the different stages of machine-based problem-solving by using DARQN on a popular video game Breakout. Finally, you’ll find out when to use a policy-based method to tackle an RL problem. By the end of The Reinforcement Learning Workshop, you’ll be equipped with the knowledge and skills needed to solve challenging problems using reinforcement learning. What you will learnUse OpenAI Gym as a framework to implement RL environmentsFind out how to define and implement reward functionExplore Markov chain, Markov decision process, and the Bellman equationDistinguish between Dynamic Programming, Monte Carlo, and Temporal Difference LearningUnderstand the multi-armed bandit problem and explore various strategies to solve itBuild a deep Q model network for playing the video game BreakoutWho this book is for If you are a data scientist, machine learning enthusiast, or a Python developer who wants to learn basic to advanced deep reinforcement learning algorithms, this workshop is for you. A basic understanding of the Python language is necessary.

The Unsupervised Learning Workshop

Author : Aaron Jones
Publisher : Packt Publishing Ltd
ISBN 13 : 1800206240
Total Pages : 549 pages
Book Rating : 4.8/5 (2 download)

DOWNLOAD NOW!

Book Synopsis The Unsupervised Learning Workshop by : Aaron Jones

Download or read book The Unsupervised Learning Workshop written by Aaron Jones and published by Packt Publishing Ltd. This book was released on 2020-07-29 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning how to apply unsupervised algorithms on unlabeled datasets from scratch can be easier than you thought with this beginner's workshop, featuring interesting examples and activities Key FeaturesGet familiar with the ecosystem of unsupervised algorithmsLearn interesting methods to simplify large amounts of unorganized dataTackle real-world challenges, such as estimating the population density of a geographical areaBook Description Do you find it difficult to understand how popular companies like WhatsApp and Amazon find valuable insights from large amounts of unorganized data? The Unsupervised Learning Workshop will give you the confidence to deal with cluttered and unlabeled datasets, using unsupervised algorithms in an easy and interactive manner. The book starts by introducing the most popular clustering algorithms of unsupervised learning. You'll find out how hierarchical clustering differs from k-means, along with understanding how to apply DBSCAN to highly complex and noisy data. Moving ahead, you'll use autoencoders for efficient data encoding. As you progress, you'll use t-SNE models to extract high-dimensional information into a lower dimension for better visualization, in addition to working with topic modeling for implementing natural language processing (NLP). In later chapters, you'll find key relationships between customers and businesses using Market Basket Analysis, before going on to use Hotspot Analysis for estimating the population density of an area. By the end of this book, you'll be equipped with the skills you need to apply unsupervised algorithms on cluttered datasets to find useful patterns and insights. What you will learnDistinguish between hierarchical clustering and the k-means algorithmUnderstand the process of finding clusters in dataGrasp interesting techniques to reduce the size of dataUse autoencoders to decode dataExtract text from a large collection of documents using topic modelingCreate a bag-of-words model using the CountVectorizerWho this book is for If you are a data scientist who is just getting started and want to learn how to implement machine learning algorithms to build predictive models, then this book is for you. To expedite the learning process, a solid understanding of the Python programming language is recommended, as you'll be editing classes and functions instead of creating them from scratch.

Optimization for Machine Learning

Author : Suvrit Sra
Publisher : MIT Press
ISBN 13 : 026201646X
Total Pages : 509 pages
Book Rating : 4.2/5 (62 download)

DOWNLOAD NOW!

Book Synopsis Optimization for Machine Learning by : Suvrit Sra

Download or read book Optimization for Machine Learning written by Suvrit Sra and published by MIT Press. This book was released on 2012 with total page 509 pages. Available in PDF, EPUB and Kindle. Book excerpt: An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community.

The The Applied Artificial Intelligence Workshop

Author : Anthony So
Publisher : Packt Publishing Ltd
ISBN 13 : 180020373X
Total Pages : 419 pages
Book Rating : 4.8/5 (2 download)

DOWNLOAD NOW!

Book Synopsis The The Applied Artificial Intelligence Workshop by : Anthony So

Download or read book The The Applied Artificial Intelligence Workshop written by Anthony So and published by Packt Publishing Ltd. This book was released on 2020-07-22 with total page 419 pages. Available in PDF, EPUB and Kindle. Book excerpt: With knowledge and information shared by experts, take your first steps towards creating scalable AI algorithms and solutions in Python, through practical exercises and engaging activities Key FeaturesLearn about AI and ML algorithms from the perspective of a seasoned data scientistGet practical experience in ML algorithms, such as regression, tree algorithms, clustering, and moreDesign neural networks that emulate the human brainBook Description You already know that artificial intelligence (AI) and machine learning (ML) are present in many of the tools you use in your daily routine. But do you want to be able to create your own AI and ML models and develop your skills in these domains to kickstart your AI career? The Applied Artificial Intelligence Workshop gets you started with applying AI with the help of practical exercises and useful examples, all put together cleverly to help you gain the skills to transform your career. The book begins by teaching you how to predict outcomes using regression. You’ll then learn how to classify data using techniques such as k-nearest neighbor (KNN) and support vector machine (SVM) classifiers. As you progress, you'll explore various decision trees by learning how to build a reliable decision tree model that can help your company find cars that clients are likely to buy. The final chapters will introduce you to deep learning and neural networks. Through various activities, such as predicting stock prices and recognizing handwritten digits, you'll learn how to train and implement convolutional neural networks (CNNs) and recurrent neural networks (RNNs). By the end of this applied AI book, you'll have learned how to predict outcomes and train neural networks and be able to use various techniques to develop AI and ML models. What you will learnCreate your first AI game in Python with the minmax algorithmImplement regression techniques to simplify real-world dataExperiment with classification techniques to label real-world dataPerform predictive analysis in Python using decision trees and random forestsUse clustering algorithms to group data without manual supportLearn how to use neural networks to process and classify labeled imagesWho this book is for The Applied Artificial Intelligence Workshop is designed for software developers and data scientists who want to enrich their projects with machine learning. Although you do not need any prior experience in AI, it is recommended that you have knowledge of high school-level mathematics and at least one programming language, preferably Python. Although this is a beginner's book, experienced students and programmers can improve their Python skills by implementing the practical applications given in this book.

Data-Driven Science and Engineering

Author : Steven L. Brunton
Publisher : Cambridge University Press
ISBN 13 : 1009098489
Total Pages : 615 pages
Book Rating : 4.0/5 (9 download)

DOWNLOAD NOW!

Book Synopsis Data-Driven Science and Engineering by : Steven L. Brunton

Download or read book Data-Driven Science and Engineering written by Steven L. Brunton and published by Cambridge University Press. This book was released on 2022-05-05 with total page 615 pages. Available in PDF, EPUB and Kindle. Book excerpt: A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.

The Applied TensorFlow and Keras Workshop

Author : Harveen Singh Chadha
Publisher : Packt Publishing Ltd
ISBN 13 : 1800204078
Total Pages : 173 pages
Book Rating : 4.8/5 (2 download)

DOWNLOAD NOW!

Book Synopsis The Applied TensorFlow and Keras Workshop by : Harveen Singh Chadha

Download or read book The Applied TensorFlow and Keras Workshop written by Harveen Singh Chadha and published by Packt Publishing Ltd. This book was released on 2020-07-30 with total page 173 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cut through the noise and get real results with this workshop for beginners. Use a project-based approach to exploring machine learning with TensorFlow and Keras. Key FeaturesUnderstand the nuances of setting up a deep learning programming environmentGain insights into the common components of a neural network and its essential operationsGet to grips with deploying a machine learning model as an interactive web application with FlaskBook Description Machine learning gives computers the ability to learn like humans. It is becoming increasingly transformational to businesses in many forms, and a key skill to learn to prepare for the future digital economy. As a beginner, you'll unlock a world of opportunities by learning the techniques you need to contribute to the domains of machine learning, deep learning, and modern data analysis using the latest cutting-edge tools. The Applied TensorFlow and Keras Workshop begins by showing you how neural networks work. After you've understood the basics, you will train a few networks by altering their hyperparameters. To build on your skills, you'll learn how to select the most appropriate model to solve the problem in hand. While tackling advanced concepts, you'll discover how to assemble a deep learning system by bringing together all the essential elements necessary for building a basic deep learning system - data, model, and prediction. Finally, you'll explore ways to evaluate the performance of your model, and improve it using techniques such as model evaluation and hyperparameter optimization. By the end of this book, you'll have learned how to build a Bitcoin app that predicts future prices, and be able to build your own models for other projects. What you will learnFamiliarize yourself with the components of a neural networkUnderstand the different types of problems that can be solved using neural networksExplore different ways to select the right architecture for your modelMake predictions with a trained model using TensorBoardDiscover the components of Keras and ways to leverage its features in your modelExplore how you can deal with new data by learning ways to retrain your modelWho this book is for If you are a data scientist or a machine learning and deep learning enthusiast, who is looking to design, train, and deploy TensorFlow and Keras models into real-world applications, then this workshop is for you. Knowledge of computer science and machine learning concepts and experience in analyzing data will help you to understand the topics explained in this book with ease.

The Artificial Intelligence Infrastructure Workshop

Author : Chinmay Arankalle
Publisher : Packt Publishing Ltd
ISBN 13 : 1800206992
Total Pages : 731 pages
Book Rating : 4.8/5 (2 download)

DOWNLOAD NOW!

Book Synopsis The Artificial Intelligence Infrastructure Workshop by : Chinmay Arankalle

Download or read book The Artificial Intelligence Infrastructure Workshop written by Chinmay Arankalle and published by Packt Publishing Ltd. This book was released on 2020-08-17 with total page 731 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore how a data storage system works – from data ingestion to representation Key FeaturesUnderstand how artificial intelligence, machine learning, and deep learning are different from one anotherDiscover the data storage requirements of different AI apps using case studiesExplore popular data solutions such as Hadoop Distributed File System (HDFS) and Amazon Simple Storage Service (S3)Book Description Social networking sites see an average of 350 million uploads daily - a quantity impossible for humans to scan and analyze. Only AI can do this job at the required speed, and to leverage an AI application at its full potential, you need an efficient and scalable data storage pipeline. The Artificial Intelligence Infrastructure Workshop will teach you how to build and manage one. The Artificial Intelligence Infrastructure Workshop begins taking you through some real-world applications of AI. You'll explore the layers of a data lake and get to grips with security, scalability, and maintainability. With the help of hands-on exercises, you'll learn how to define the requirements for AI applications in your organization. This AI book will show you how to select a database for your system and run common queries on databases such as MySQL, MongoDB, and Cassandra. You'll also design your own AI trading system to get a feel of the pipeline-based architecture. As you learn to implement a deep Q-learning algorithm to play the CartPole game, you'll gain hands-on experience with PyTorch. Finally, you'll explore ways to run machine learning models in production as part of an AI application. By the end of the book, you'll have learned how to build and deploy your own AI software at scale, using various tools, API frameworks, and serialization methods. What you will learnGet to grips with the fundamentals of artificial intelligenceUnderstand the importance of data storage and architecture in AI applicationsBuild data storage and workflow management systems with open source toolsContainerize your AI applications with tools such as DockerDiscover commonly used data storage solutions and best practices for AI on Amazon Web Services (AWS)Use the AWS CLI and AWS SDK to perform common data tasksWho this book is for If you are looking to develop the data storage skills needed for machine learning and AI and want to learn AI best practices in data engineering, this workshop is for you. Experienced programmers can use this book to advance their career in AI. Familiarity with programming, along with knowledge of exploratory data analysis and reading and writing files using Python will help you to understand the key concepts covered.

The Deep Learning with PyTorch Workshop

Author : Hyatt Saleh
Publisher :
ISBN 13 : 9781838989217
Total Pages : 330 pages
Book Rating : 4.9/5 (892 download)

DOWNLOAD NOW!

Book Synopsis The Deep Learning with PyTorch Workshop by : Hyatt Saleh

Download or read book The Deep Learning with PyTorch Workshop written by Hyatt Saleh and published by . This book was released on 2020-07-20 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt:

The Deep Learning with Keras Workshop

Author : Matthew Moocarme
Publisher :
ISBN 13 : 9781801071185
Total Pages : 0 pages
Book Rating : 4.0/5 (711 download)

DOWNLOAD NOW!

Book Synopsis The Deep Learning with Keras Workshop by : Matthew Moocarme

Download or read book The Deep Learning with Keras Workshop written by Matthew Moocarme and published by . This book was released on 2020-07-29 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover how to leverage Keras, the powerful and easy-to-use open source Python library for developing and evaluating deep learning models Key Features Get to grips with various model evaluation metrics, including sensitivity, specificity, and AUC scores Explore advanced concepts such as sequential memory and sequential modeling Reinforce your skills with real-world development, screencasts, and knowledge checks Book Description New experiences can be intimidating, but not this one! This beginner's guide to deep learning is here to help you explore deep learning from scratch with Keras, and be on your way to training your first ever neural networks. What sets Keras apart from other deep learning frameworks is its simplicity. With over two hundred thousand users, Keras has a stronger adoption in industry and the research community than any other deep learning framework. The Deep Learning with Keras Workshop starts by introducing you to the fundamental concepts of machine learning using the scikit-learn package. After learning how to perform the linear transformations that are necessary for building neural networks, you'll build your first neural network with the Keras library. As you advance, you'll learn how to build multi-layer neural networks and recognize when your model is underfitting or overfitting to the training data. With the help of practical exercises, you'll learn to use cross-validation techniques to evaluate your models and then choose the optimal hyperparameters to fine-tune their performance. Finally, you'll explore recurrent neural networks and learn how to train them to predict values in sequential data. By the end of this book, you'll have developed the skills you need to confidently train your own neural network models. What you will learn Gain insights into the fundamentals of neural networks Understand the limitations of machine learning and how it differs from deep learning Build image classifiers with convolutional neural networks Evaluate, tweak, and improve your models with techniques such as cross-validation Create prediction models to detect data patterns and make predictions Improve model accuracy with L1, L2, and dropout regularization Who this book is for If you know the basics of data science and machine learning and want to get started with advanced machine learning technologies like artificial neural networks and deep learning, then this is the book for you. To grasp the concepts explained in this deep learnin ...

The The Computer Vision Workshop

Author : Hafsa Asad
Publisher : Packt Publishing Ltd
ISBN 13 : 180020714X
Total Pages : 567 pages
Book Rating : 4.8/5 (2 download)

DOWNLOAD NOW!

Book Synopsis The The Computer Vision Workshop by : Hafsa Asad

Download or read book The The Computer Vision Workshop written by Hafsa Asad and published by Packt Publishing Ltd. This book was released on 2020-07-27 with total page 567 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore the potential of deep learning techniques in computer vision applications using the Python ecosystem, and build real-time systems for detecting human behavior Key FeaturesUnderstand OpenCV and select the right algorithm to solve real-world problemsDiscover techniques for image and video processingLearn how to apply face recognition in videos to automatically extract key informationBook Description Computer Vision (CV) has become an important aspect of AI technology. From driverless cars to medical diagnostics and monitoring the health of crops to fraud detection in banking, computer vision is used across all domains to automate tasks. The Computer Vision Workshop will help you understand how computers master the art of processing digital images and videos to mimic human activities. Starting with an introduction to the OpenCV library, you'll learn how to write your first script using basic image processing operations. You'll then get to grips with essential image and video processing techniques such as histograms, contours, and face processing. As you progress, you'll become familiar with advanced computer vision and deep learning concepts, such as object detection, tracking, and recognition, and finally shift your focus from 2D to 3D visualization. This CV course will enable you to experiment with camera calibration and explore both passive and active canonical 3D reconstruction methods. By the end of this book, you'll have developed the practical skills necessary for building powerful applications to solve computer vision problems. What you will learnAccess and manipulate pixels in OpenCV using BGR and grayscale imagesCreate histograms to better understand image contentUse contours for shape analysis, object detection, and recognitionTrack objects in videos using a variety of trackers available in OpenCVDiscover how to apply face recognition tasks using computer vision techniquesVisualize 3D objects in point clouds and polygon meshes using Open3DWho this book is for If you are a researcher, developer, or data scientist looking to automate everyday tasks using computer vision, this workshop is for you. A basic understanding of Python and deep learning will help you to get the most out of this workshop.

Strengthening Deep Neural Networks

Author : Katy Warr
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492044903
Total Pages : 246 pages
Book Rating : 4.4/5 (92 download)

DOWNLOAD NOW!

Book Synopsis Strengthening Deep Neural Networks by : Katy Warr

Download or read book Strengthening Deep Neural Networks written by Katy Warr and published by "O'Reilly Media, Inc.". This book was released on 2019-07-03 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: As deep neural networks (DNNs) become increasingly common in real-world applications, the potential to deliberately "fool" them with data that wouldn’t trick a human presents a new attack vector. This practical book examines real-world scenarios where DNNs—the algorithms intrinsic to much of AI—are used daily to process image, audio, and video data. Author Katy Warr considers attack motivations, the risks posed by this adversarial input, and methods for increasing AI robustness to these attacks. If you’re a data scientist developing DNN algorithms, a security architect interested in how to make AI systems more resilient to attack, or someone fascinated by the differences between artificial and biological perception, this book is for you. Delve into DNNs and discover how they could be tricked by adversarial input Investigate methods used to generate adversarial input capable of fooling DNNs Explore real-world scenarios and model the adversarial threat Evaluate neural network robustness; learn methods to increase resilience of AI systems to adversarial data Examine some ways in which AI might become better at mimicking human perception in years to come

Deep Learning and Data Labeling for Medical Applications

Author : Gustavo Carneiro
Publisher : Springer
ISBN 13 : 3319469762
Total Pages : 280 pages
Book Rating : 4.3/5 (194 download)

DOWNLOAD NOW!

Book Synopsis Deep Learning and Data Labeling for Medical Applications by : Gustavo Carneiro

Download or read book Deep Learning and Data Labeling for Medical Applications written by Gustavo Carneiro and published by Springer. This book was released on 2016-10-07 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of two workshops held at the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, in Athens, Greece, in October 2016: the First Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016, and the Second International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016. The 28 revised regular papers presented in this book were carefully reviewed and selected from a total of 52 submissions. The 7 papers selected for LABELS deal with topics from the following fields: crowd-sourcing methods; active learning; transfer learning; semi-supervised learning; and modeling of label uncertainty.The 21 papers selected for DLMIA span a wide range of topics such as image description; medical imaging-based diagnosis; medical signal-based diagnosis; medical image reconstruction and model selection using deep learning techniques; meta-heuristic techniques for fine-tuning parameter in deep learning-based architectures; and applications based on deep learning techniques.