Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide PDF Download

Author: Willem Meints
Publisher: Packt Publishing Ltd
ISBN: 1789803195
Category : Computers
Languages : en
Pages : 202

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Book Description
Learn how to train popular deep learning architectures such as autoencoders, convolutional and recurrent neural networks while discovering how you can use deep learning models in your software applications with Microsoft Cognitive Toolkit Key FeaturesUnderstand the fundamentals of Microsoft Cognitive Toolkit and set up the development environment Train different types of neural networks using Cognitive Toolkit and deploy it to productionEvaluate the performance of your models and improve your deep learning skillsBook Description Cognitive Toolkit is a very popular and recently open sourced deep learning toolkit by Microsoft. Cognitive Toolkit is used to train fast and effective deep learning models. This book will be a quick introduction to using Cognitive Toolkit and will teach you how to train and validate different types of neural networks, such as convolutional and recurrent neural networks. This book will help you understand the basics of deep learning. You will learn how to use Microsoft Cognitive Toolkit to build deep learning models and discover what makes this framework unique so that you know when to use it. This book will be a quick, no-nonsense introduction to the library and will teach you how to train different types of neural networks, such as convolutional neural networks, recurrent neural networks, autoencoders, and more, using Cognitive Toolkit. Then we will look at two scenarios in which deep learning can be used to enhance human capabilities. The book will also demonstrate how to evaluate your models' performance to ensure it trains and runs smoothly and gives you the most accurate results. Finally, you will get a short overview of how Cognitive Toolkit fits in to a DevOps environment What you will learnSet up your deep learning environment for the Cognitive Toolkit on Windows and LinuxPre-process and feed your data into neural networksUse neural networks to make effcient predictions and recommendationsTrain and deploy effcient neural networks such as CNN and RNNDetect problems in your neural network using TensorBoardIntegrate Cognitive Toolkit with Azure ML Services for effective deep learningWho this book is for Data Scientists, Machine learning developers, AI developers who wish to train and deploy effective deep learning models using Microsoft CNTK will find this book to be useful. Readers need to have experience in Python or similar object-oriented language like C# or Java.

Author: Md. Rezaul Karim
Publisher: Packt Publishing Ltd
ISBN: 1789345413
Category : Mathematics
Languages : en
Pages : 215

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Book Description
Supervised and unsupervised machine learning made easy in Scala with this quick-start guide. Key FeaturesConstruct and deploy machine learning systems that learn from your data and give accurate predictionsUnleash the power of Spark ML along with popular machine learning algorithms to solve complex tasks in Scala.Solve hands-on problems by combining popular neural network architectures such as LSTM and CNN using Scala with DeepLearning4j libraryBook Description Scala is a highly scalable integration of object-oriented nature and functional programming concepts that make it easy to build scalable and complex big data applications. This book is a handy guide for machine learning developers and data scientists who want to develop and train effective machine learning models in Scala. The book starts with an introduction to machine learning, while covering deep learning and machine learning basics. It then explains how to use Scala-based ML libraries to solve classification and regression problems using linear regression, generalized linear regression, logistic regression, support vector machine, and Naïve Bayes algorithms. It also covers tree-based ensemble techniques for solving both classification and regression problems. Moving ahead, it covers unsupervised learning techniques, such as dimensionality reduction, clustering, and recommender systems. Finally, it provides a brief overview of deep learning using a real-life example in Scala. What you will learnGet acquainted with JVM-based machine learning libraries for Scala such as Spark ML and Deeplearning4jLearn RDDs, DataFrame, and Spark SQL for analyzing structured and unstructured dataUnderstand supervised and unsupervised learning techniques with best practices and pitfallsLearn classification and regression analysis with linear regression, logistic regression, Naïve Bayes, support vector machine, and tree-based ensemble techniques Learn effective ways of clustering analysis with dimensionality reduction techniquesLearn recommender systems with collaborative filtering approachDelve into deep learning and neural network architecturesWho this book is for This book is for machine learning developers looking to train machine learning models in Scala without spending too much time and effort. Some fundamental knowledge of Scala programming and some basics of statistics and linear algebra is all you need to get started with this book.

Author: Dejan Sarka
Publisher: Packt Publishing Ltd
ISBN: 1789537134
Category : Computers
Languages : en
Pages : 196

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Book Description
Get unique insights from your data by combining the power of SQL Server, R and Python Key Features Use the features of SQL Server 2017 to implement the data science project life cycle Leverage the power of R and Python to design and develop efficient data models find unique insights from your data with powerful techniques for data preprocessing and analysis Book Description SQL Server only started to fully support data science with its two most recent editions. If you are a professional from both worlds, SQL Server and data science, and interested in using SQL Server and Machine Learning (ML) Services for your projects, then this is the ideal book for you. This book is the ideal introduction to data science with Microsoft SQL Server and In-Database ML Services. It covers all stages of a data science project, from businessand data understanding,through data overview, data preparation, modeling and using algorithms, model evaluation, and deployment. You will learn to use the engines and languages that come with SQL Server, including ML Services with R and Python languages and Transact-SQL. You will also learn how to choose which algorithm to use for which task, and learn the working of each algorithm. What you will learn Use the popular programming languages,T-SQL, R, and Python, for data science Understand your data with queries and introductory statistics Create and enhance the datasets for ML Visualize and analyze data using basic and advanced graphs Explore ML using unsupervised and supervised models Deploy models in SQL Server and perform predictions Who this book is for SQL Server professionals who want to start with data science, and data scientists who would like to start using SQL Server in their projects will find this book to be useful. Prior exposure to SQL Server will be helpful.

Author: Michael Bernico
Publisher: Packt Publishing Ltd
ISBN: 1788838912
Category : Computers
Languages : en
Pages : 261

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Book Description
Dive deeper into neural networks and get your models trained, optimized with this quick reference guide Key Features A quick reference to all important deep learning concepts and their implementations Essential tips, tricks, and hacks to train a variety of deep learning models such as CNNs, RNNs, LSTMs, and more Supplemented with essential mathematics and theory, every chapter provides best practices and safe choices for training and fine-tuning your models in Keras and Tensorflow. Book Description Deep learning has become an essential necessity to enter the world of artificial intelligence. With this book deep learning techniques will become more accessible, practical, and relevant to practicing data scientists. It moves deep learning from academia to the real world through practical examples. You will learn how Tensor Board is used to monitor the training of deep neural networks and solve binary classification problems using deep learning. Readers will then learn to optimize hyperparameters in their deep learning models. The book then takes the readers through the practical implementation of training CNN's, RNN's, and LSTM's with word embeddings and seq2seq models from scratch. Later the book explores advanced topics such as Deep Q Network to solve an autonomous agent problem and how to use two adversarial networks to generate artificial images that appear real. For implementation purposes, we look at popular Python-based deep learning frameworks such as Keras and Tensorflow, Each chapter provides best practices and safe choices to help readers make the right decision while training deep neural networks. By the end of this book, you will be able to solve real-world problems quickly with deep neural networks. What you will learn Solve regression and classification challenges with TensorFlow and Keras Learn to use Tensor Board for monitoring neural networks and its training Optimize hyperparameters and safe choices/best practices Build CNN's, RNN's, and LSTM's and using word embedding from scratch Build and train seq2seq models for machine translation and chat applications. Understanding Deep Q networks and how to use one to solve an autonomous agent problem. Explore Deep Q Network and address autonomous agent challenges. Who this book is for If you are a Data Scientist or a Machine Learning expert, then this book is a very useful read in training your advanced machine learning and deep learning models. You can also refer this book if you are stuck in-between the neural network modeling and need immediate assistance in getting accomplishing the task smoothly. Some prior knowledge of Python and tight hold on the basics of machine learning is required.

Author: Mario A. B. Capurso
Publisher: Mario Capurso
ISBN:
Category : Computers
Languages : en
Pages : 278

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Book Description
This work follows the 2021 curriculum of the Association for Computing Machinery for specialists in Data Sciences, with the aim of producing a manual that collects notions in a simplified form, facilitating a personal training path starting from specialized skills in Computer Science or Mathematics or Statistics. It has a bibliography with links to quality material but freely usable for your own training and contextual practical exercises. Part in a series of texts, it first summarizes the standard CRISP DM working methodology used in this work and in Data Science projects. As this text uses Orange for the application aspects, it describes its installation and widgets. The data modeling phase is considered from the perspective of machine learning by summarizing machine learning types, model types, problem types, and algorithm types. Advanced aspects associated with modeling are described such as loss and optimization functions such as gradient descent, techniques to analyze model performance such as Bootstrapping and Cross Validation. Deployment scenarios and the most common platforms are analyzed, with application examples. Mechanisms are proposed to automate machine learning and to support the interpretability of models and results such as Partial Dependence Plot, Permuted Feature Importance and others. The exercises are described with Orange and Python using the Keras/Tensorflow library. The text is accompanied by supporting material and it is possible to download the examples and the test data.

Author: Prof. Hr Ansari
Publisher:
ISBN:
Category :
Languages : en
Pages : 133

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Book Description
The Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.This book provides a high-level overview of the toolkit. This book will be useful for everyone who either has an interest in Deep Learning.

Author: John Biggs
Publisher: O'Reilly Media
ISBN: 1492052299
Category : Computers
Languages : en
Pages : 154

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Book Description
Serverless computing is radically changing the way we build and deploy applications. With cloud providers running servers and managing machine resources, companies now can focus solely on the application’s business logic and functionality. This hands-on book shows experienced programmers how to build and deploy scalable machine learning and deep learning models using serverless architectures with Microsoft Azure. You’ll learn step-by-step how to code machine learning into your projects using Python and pre-trained models that include tools such as image recognition, speech recognition, and classification. You’ll also examine issues around deployment and continuous delivery including scaling, security, and monitoring. This book is divided into four parts: Cloud-based development: learn the basics of serverless computing with machine learning, functions as a service (FaaS), and the use of APIs Adding intelligence: create serverless applications using Azure Functions; learn how to use pre-built machine-learning and deep-learning models Deployment and continuous delivery: get up to speed with Azure Kubernetes Service, as well as Azure Security Center, and Azure Monitoring Application examples: deliver data at the edge, build conversational interfaces, and use convolutional neural networks for image classification

Author: Daneyal Anis
Publisher:
ISBN:
Category :
Languages : en
Pages : 200

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Book Description
*Start your Data Science career using Python today!*Are you ready to start your new exciting career? Ready to master artificial intelligence and deep learning concepts?Are you overwhelmed with complexity of the books on this subject?Then let this breezy and fun little book on Python, Machine Learning and Deep Learning models make you a Data Scientist in 7 days!This book continues from where the first book in the series, Ultimate Step by Step Guide to Machine Learning Using Python, left of. In the first book you were introduced to Python concepts such as: -Data Structures like Pandas -Foundational libraries like Numpy, Seaborn and Scikit-Learn-Regression analysis-Classification-Clustering-Association Learning-Dimension ReductionThis book builds on those concepts to expand on Machine Learning algorithms like: -Linear and Logistical regression-Decision tree-Support vector machines (SVM)After that, this book takes you on a journey into Deep Learning and Neural Networks with important concepts and libraries like: -Convolutional and Recurrent Neural Networks-TensorFlow-Keras-PyTorch-Keras-Apache MXNet-Microsoft Cognitive Toolkit (CNTK)The final part of the book covers all foundational concepts that are required for Amazon Web Services (AWS) Certified Machine Learning Specialization by explaining how to deploy your models at scale on Cloud technologies. While AWS is used in the book for illustrative purposes, Microsoft Azure and Google Cloud are also introduced as alternative cloud technologies. After reading this book you will be able to: -Code in Python with confidence-Build new machine learning and deep learning models from scratch-Know how to clean and prepare your data for analytics-Speak confidently about statistical analysis techniquesData Science was ranked the fast-growing field by LinkedIn and Data Scientist is one of the most highly sought after and lucrative careers in the world!If you are on the fence about making the leap to a new and lucrative career, this is the book for you!What sets this book apart from other books on the topic of Python and Machine learning: -Step by step code examples and explanation-Complex concepts explained visually-Real world applicability of the machine learning and deep learning models introducedWhat do I need to get started?You will have a step by step action plan in place once you finish this book and finally feel that you, can master data science and artificial intelligence and start a lucrative and rewarding career! Ready to dive in to the exciting world of Python and Deep Learning?Then scroll up to the top and hit that BUY BUTTON!

Author: Kaushik Balakrishnan
Publisher: Packt Publishing Ltd
ISBN: 1789533449
Category : Computers
Languages : en
Pages : 175

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Book Description
Leverage the power of Tensorflow to Create powerful software agents that can self-learn to perform real-world tasks Key FeaturesExplore efficient Reinforcement Learning algorithms and code them using TensorFlow and PythonTrain Reinforcement Learning agents for problems, ranging from computer games to autonomous driving.Formulate and devise selective algorithms and techniques in your applications in no time.Book Description Advances in reinforcement learning algorithms have made it possible to use them for optimal control in several different industrial applications. With this book, you will apply Reinforcement Learning to a range of problems, from computer games to autonomous driving. The book starts by introducing you to essential Reinforcement Learning concepts such as agents, environments, rewards, and advantage functions. You will also master the distinctions between on-policy and off-policy algorithms, as well as model-free and model-based algorithms. You will also learn about several Reinforcement Learning algorithms, such as SARSA, Deep Q-Networks (DQN), Deep Deterministic Policy Gradients (DDPG), Asynchronous Advantage Actor-Critic (A3C), Trust Region Policy Optimization (TRPO), and Proximal Policy Optimization (PPO). The book will also show you how to code these algorithms in TensorFlow and Python and apply them to solve computer games from OpenAI Gym. Finally, you will also learn how to train a car to drive autonomously in the Torcs racing car simulator. By the end of the book, you will be able to design, build, train, and evaluate feed-forward neural networks and convolutional neural networks. You will also have mastered coding state-of-the-art algorithms and also training agents for various control problems. What you will learnUnderstand the theory and concepts behind modern Reinforcement Learning algorithmsCode state-of-the-art Reinforcement Learning algorithms with discrete or continuous actionsDevelop Reinforcement Learning algorithms and apply them to training agents to play computer gamesExplore DQN, DDQN, and Dueling architectures to play Atari's Breakout using TensorFlowUse A3C to play CartPole and LunarLanderTrain an agent to drive a car autonomously in a simulatorWho this book is for Data scientists and AI developers who wish to quickly get started with training effective reinforcement learning models in TensorFlow will find this book very useful. Prior knowledge of machine learning and deep learning concepts (as well as exposure to Python programming) will be useful.

Author: Anurag Bhardwaj
Publisher: Packt Publishing Ltd
ISBN: 1785887777
Category : Computers
Languages : en
Pages : 271

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Book Description
Get to grips with the essentials of deep learning by leveraging the power of Python Key Features Your one-stop solution to get started with the essentials of deep learning and neural network modeling Train different kinds of neural networks to tackle various problems in Natural Language Processing, computer vision, speech recognition, and more Covers popular Python libraries such as Tensorflow, Keras, and more, along with tips on training, deploying and optimizing your deep learning models in the best possible manner Book Description Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master. This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train, and deploy different kinds of neural networks such as Convolutional Neural Network, Recurrent Neural Network, and will see some of their applications in real-world domains including computer vision, natural language processing, speech recognition, and so on. You will build practical projects such as chatbots, implement reinforcement learning to build smart games, and develop expert systems for image captioning and processing. Popular Python library such as TensorFlow is used in this book to build the models. This book also covers solutions for different problems you might come across while training models, such as noisy datasets, small datasets, and more. This book does not assume any prior knowledge of deep learning. By the end of this book, you will have a firm understanding of the basics of deep learning and neural network modeling, along with their practical applications. What you will learn Get to grips with the core concepts of deep learning and neural networks Set up deep learning library such as TensorFlow Fine-tune your deep learning models for NLP and Computer Vision applications Unify different information sources, such as images, text, and speech through deep learning Optimize and fine-tune your deep learning models for better performance Train a deep reinforcement learning model that plays a game better than humans Learn how to make your models get the best out of your GPU or CPU Who this book is for Aspiring data scientists and machine learning experts who have limited or no exposure to deep learning will find this book to be very useful. If you are looking for a resource that gets you up and running with the fundamentals of deep learning and neural networks, this book is for you. As the models in the book are trained using the popular Python-based libraries such as Tensorflow and Keras, it would be useful to have sound programming knowledge of Python.