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Author : AJIT DASH
Publisher :
ISBN 13 : 9781312196186
Total Pages : 0 pages
Book Rating : 4.1/5 (961 download)
Download or read book The STEP-BY-STEP GUIDE FOR OPENAI AND AZURE OPENAI written by AJIT DASH and published by . This book was released on with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Miao, Fengchun
Publisher : UNESCO Publishing
ISBN 13 : 9231004476
Total Pages : 50 pages
Book Rating : 4.2/5 (31 download)
Download or read book AI and education written by Miao, Fengchun and published by UNESCO Publishing. This book was released on 2021-04-08 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence (AI) has the potential to address some of the biggest challenges in education today, innovate teaching and learning practices, and ultimately accelerate the progress towards SDG 4. However, these rapid technological developments inevitably bring multiple risks and challenges, which have so far outpaced policy debates and regulatory frameworks. This publication offers guidance for policy-makers on how best to leverage the opportunities and address the risks, presented by the growing connection between AI and education. It starts with the essentials of AI: definitions, techniques and technologies. It continues with a detailed analysis of the emerging trends and implications of AI for teaching and learning, including how we can ensure the ethical, inclusive and equitable use of AI in education, how education can prepare humans to live and work with AI, and how AI can be applied to enhance education. It finally introduces the challenges of harnessing AI to achieve SDG 4 and offers concrete actionable recommendations for policy-makers to plan policies and programmes for local contexts. [Publisher summary, ed]
Download or read book The Future Computed written by and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Paul Roetzer
Publisher : BenBella Books
ISBN 13 : 1637740794
Total Pages : 257 pages
Book Rating : 4.6/5 (377 download)
Download or read book Marketing Artificial Intelligence written by Paul Roetzer and published by BenBella Books. This book was released on 2022-06-28 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence is forecasted to have trillions of dollars of impact on businesses and the economy, yet many marketers struggle to understand what it is and how to apply it in their marketing efforts. The truth is, AI possesses the power to change everything. While AI-powered marketing technologies may never achieve the sci-fi vision of self-running, self-improving autonomous systems, a little bit of AI can go a long way toward dramatically increasing productivity, efficiency, and performance. Marketing AI Institute’s Founder & CEO, Paul Roetzer, and Chief Content Officer, Mike Kaput, join forces to show marketers how to embrace AI and make it their competitive advantage. Marketing Artificial Intelligence draws on years of research and dozens of interviews with AI marketers, executives, engineers, and entrepreneurs. Roetzer and Kaput present the current potential of AI, as well as a glimpse into a near future in which marketers and machines work seamlessly to run personalized campaigns of unprecedented complexity with unimaginable simplicity. As the amount of data exponentially increases, marketers’ abilities to filter through the noise and turn information into actionable intelligence remain limited. Roetzer and Kaput show you how to make breaking through that noise your superpower. So, come along on a journey of exploration and enlightenment. Marketing Artificial Intelligence is the blueprint for understanding and applying AI, giving you just the edge in your career you’ve been waiting for.
Author : Joe Mayo
Publisher : Microsoft Press
ISBN 13 : 1509305025
Total Pages : 974 pages
Book Rating : 4.5/5 (93 download)
Download or read book Programming the Microsoft Bot Framework written by Joe Mayo and published by Microsoft Press. This book was released on 2017-11-21 with total page 974 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the only comprehensive, authoritative guide to building Conversational User Interfaces (CUI, a.k.a. bots, chatbots, or chatterbots) with the Microsoft Bot Framework. Reflecting the next radical revolution in human-computer interaction, it will help you leverage advanced artificial intelligence (AI) and natural language processing to empower new and existing applications with stunningly intuitive conversational interfaces. Long-time Microsoft MVP Joe Mayo begins with high-level explanations of what Microsoft Bot Framework is, what you can do with it, and why it matters so much. Next, he presents the foundational knowledge you need to start creating real bots and CUIs. Step by step, you'll learn how to build message dialogs, manage conversations, interact with framework APIs, and incorporate powerful natural language processing with Microsoft's advanced Language Understanding Intelligent Service (LUIS). Mayo also offers detailed guidance on deploying your customized bots to key platforms such as Slack, Skype, and Facebook Messenger. Throughout, Mayo's practical examples combine code with clear explanations of when and why you would perform each task. From start to finish, Programming the Microsoft Bot Framework is relentlessly practical, helping you translate the advanced "magic" of intelligent bots into real solutions right now.
Author : Nazia Habib
Publisher : Packt Publishing Ltd
ISBN 13 : 1789345758
Total Pages : 200 pages
Book Rating : 4.7/5 (893 download)
Download or read book Hands-On Q-Learning with Python written by Nazia Habib and published by Packt Publishing Ltd. This book was released on 2019-04-19 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage the power of reward-based training for your deep learning models with Python Key FeaturesUnderstand Q-learning algorithms to train neural networks using Markov Decision Process (MDP)Study practical deep reinforcement learning using Q-NetworksExplore state-based unsupervised learning for machine learning modelsBook Description Q-learning is a machine learning algorithm used to solve optimization problems in artificial intelligence (AI). It is one of the most popular fields of study among AI researchers. This book starts off by introducing you to reinforcement learning and Q-learning, in addition to helping you get familiar with OpenAI Gym as well as libraries such as Keras and TensorFlow. A few chapters into the book, you will gain insights into modelfree Q-learning and use deep Q-networks and double deep Q-networks to solve complex problems. This book will guide you in exploring use cases such as self-driving vehicles and OpenAI Gym’s CartPole problem. You will also learn how to tune and optimize Q-networks and their hyperparameters. As you progress, you will understand the reinforcement learning approach to solving real-world problems. You will also explore how to use Q-learning and related algorithms in real-world applications such as scientific research. Toward the end, you’ll gain a sense of what’s in store for reinforcement learning. By the end of this book, you will be equipped with the skills you need to solve reinforcement learning problems using Q-learning algorithms with OpenAI Gym, Keras, and TensorFlow. What you will learnExplore the fundamentals of reinforcement learning and the state-action-reward processUnderstand Markov decision processesGet well versed with libraries such as Keras, and TensorFlowCreate and deploy model-free learning and deep Q-learning agents with TensorFlow, Keras, and OpenAI GymChoose and optimize a Q-Network’s learning parameters and fine-tune its performanceDiscover real-world applications and use cases of Q-learningWho this book is for If you are a machine learning developer, engineer, or professional who wants to delve into the deep learning approach for a complex environment, then this is the book for you. Proficiency in Python programming and basic understanding of decision-making in reinforcement learning is assumed.
Author : Sudharsan Ravichandiran
Publisher : Packt Publishing Ltd
ISBN 13 : 1838640142
Total Pages : 484 pages
Book Rating : 4.8/5 (386 download)
Download or read book Python Reinforcement Learning written by Sudharsan Ravichandiran and published by Packt Publishing Ltd. This book was released on 2019-04-18 with total page 484 pages. Available in PDF, EPUB and Kindle. Book excerpt: Apply modern reinforcement learning and deep reinforcement learning methods using Python and its powerful libraries Key FeaturesYour entry point into the world of artificial intelligence using the power of PythonAn example-rich guide to master various RL and DRL algorithmsExplore the power of modern Python libraries to gain confidence in building self-trained applicationsBook Description Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. This Learning Path will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms. The Learning Path starts with an introduction to RL followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. You'll also work on various datasets including image, text, and video. This example-rich guide will introduce you to deep RL algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will gain experience in several domains, including gaming, image processing, and physical simulations. You'll explore TensorFlow and OpenAI Gym to implement algorithms that also predict stock prices, generate natural language, and even build other neural networks. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many of the recent advancements in RL. By the end of the Learning Path, you will have all the knowledge and experience needed to implement RL and deep RL in your projects, and you enter the world of artificial intelligence to solve various real-life problems. This Learning Path includes content from the following Packt products: Hands-On Reinforcement Learning with Python by Sudharsan RavichandiranPython Reinforcement Learning Projects by Sean Saito, Yang Wenzhuo, and Rajalingappaa ShanmugamaniWhat you will learnTrain an agent to walk using OpenAI Gym and TensorFlowSolve multi-armed-bandit problems using various algorithmsBuild intelligent agents using the DRQN algorithm to play the Doom gameTeach your agent to play Connect4 using AlphaGo ZeroDefeat Atari arcade games using the value iteration methodDiscover how to deal with discrete and continuous action spaces in various environmentsWho this book is for If you’re an ML/DL enthusiast interested in AI and want to explore RL and deep RL from scratch, this Learning Path is for you. Prior knowledge of linear algebra is expected.
Author : Alberto Artasanchez
Publisher : Packt Publishing Ltd
ISBN 13 : 1839216077
Total Pages : 619 pages
Book Rating : 4.8/5 (392 download)
Download or read book Artificial Intelligence with Python written by Alberto Artasanchez and published by Packt Publishing Ltd. This book was released on 2020-01-31 with total page 619 pages. Available in PDF, EPUB and Kindle. Book excerpt: New edition of the bestselling guide to artificial intelligence with Python, updated to Python 3.x, with seven new chapters that cover RNNs, AI and Big Data, fundamental use cases, chatbots, and more. Key FeaturesCompletely updated and revised to Python 3.xNew chapters for AI on the cloud, recurrent neural networks, deep learning models, and feature selection and engineeringLearn more about deep learning algorithms, machine learning data pipelines, and chatbotsBook Description Artificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications. This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data. Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques. What you will learnUnderstand what artificial intelligence, machine learning, and data science areExplore the most common artificial intelligence use casesLearn how to build a machine learning pipelineAssimilate the basics of feature selection and feature engineeringIdentify the differences between supervised and unsupervised learningDiscover the most recent advances and tools offered for AI development in the cloudDevelop automatic speech recognition systems and chatbotsApply AI algorithms to time series dataWho this book is for The intended audience for this book is Python developers who want to build real-world Artificial Intelligence applications. Basic Python programming experience and awareness of machine learning concepts and techniques is mandatory.
Author : Francois Chollet
Publisher : Simon and Schuster
ISBN 13 : 1638352046
Total Pages : 597 pages
Book Rating : 4.6/5 (383 download)
Download or read book Deep Learning with Python written by Francois Chollet and published by Simon and Schuster. This book was released on 2017-11-30 with total page 597 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. What's Inside Deep learning from first principles Setting up your own deep-learning environment Image-classification models Deep learning for text and sequences Neural style transfer, text generation, and image generation About the Reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others. Table of Contents PART 1 - FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2 - DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deep-learning best practices Generative deep learning Conclusions appendix A - Installing Keras and its dependencies on Ubuntu appendix B - Running Jupyter notebooks on an EC2 GPU instance
Author : Rowel Atienza
Publisher : Packt Publishing Ltd
ISBN 13 : 178862453X
Total Pages : 369 pages
Book Rating : 4.7/5 (886 download)
Download or read book Advanced Deep Learning with Keras written by Rowel Atienza and published by Packt Publishing Ltd. This book was released on 2018-10-31 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding and coding advanced deep learning algorithms with the most intuitive deep learning library in existence Key Features Explore the most advanced deep learning techniques that drive modern AI results Implement deep neural networks, autoencoders, GANs, VAEs, and deep reinforcement learning A wide study of GANs, including Improved GANs, Cross-Domain GANs, and Disentangled Representation GANs Book DescriptionRecent developments in deep learning, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Deep Reinforcement Learning (DRL) are creating impressive AI results in our news headlines - such as AlphaGo Zero beating world chess champions, and generative AI that can create art paintings that sell for over $400k because they are so human-like. Advanced Deep Learning with Keras is a comprehensive guide to the advanced deep learning techniques available today, so you can create your own cutting-edge AI. Using Keras as an open-source deep learning library, you'll find hands-on projects throughout that show you how to create more effective AI with the latest techniques. The journey begins with an overview of MLPs, CNNs, and RNNs, which are the building blocks for the more advanced techniques in the book. You’ll learn how to implement deep learning models with Keras and TensorFlow 1.x, and move forwards to advanced techniques, as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You then learn all about GANs, and how they can open new levels of AI performance. Next, you’ll get up to speed with how VAEs are implemented, and you’ll see how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans - a major stride forward for modern AI. To complete this set of advanced techniques, you'll learn how 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 Cutting-edge techniques in human-like AI performance Implement advanced deep learning models using Keras The building blocks for advanced techniques - MLPs, CNNs, and RNNs Deep neural networks – ResNet and DenseNet Autoencoders and Variational Autoencoders (VAEs) Generative Adversarial Networks (GANs) and creative AI techniques Disentangled Representation GANs, and Cross-Domain GANs Deep reinforcement learning methods and implementation Produce industry-standard applications using OpenAI Gym Deep Q-Learning and Policy Gradient Methods Who this book is for Some fluency with Python is assumed. As an advanced book, you'll be familiar with some machine learning approaches, and some practical experience with DL will be helpful. Knowledge of Keras or TensorFlow 1.x is not required but would be helpful.
Author : Taweh Beysolow II
Publisher : Apress
ISBN 13 : 148425127X
Total Pages : 177 pages
Book Rating : 4.4/5 (842 download)
Download or read book Applied Reinforcement Learning with Python written by Taweh Beysolow II and published by Apress. This book was released on 2019-08-23 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: Delve into the world of reinforcement learning algorithms and apply them to different use-cases via Python. This book covers important topics such as policy gradients and Q learning, and utilizes frameworks such as Tensorflow, Keras, and OpenAI Gym. Applied Reinforcement Learning with Python introduces you to the theory behind reinforcement learning (RL) algorithms and the code that will be used to implement them. You will take a guided tour through features of OpenAI Gym, from utilizing standard libraries to creating your own environments, then discover how to frame reinforcement learning problems so you can research, develop, and deploy RL-based solutions. What You'll Learn Implement reinforcement learning with Python Work with AI frameworks such as OpenAI Gym, Tensorflow, and KerasDeploy and train reinforcement learning–based solutions via cloud resourcesApply practical applications of reinforcement learning Who This Book Is For Data scientists, machine learning engineers and software engineers familiar with machine learning and deep learning concepts.
Author : Ken Cherven
Publisher : Packt Publishing Ltd
ISBN 13 : 1783987359
Total Pages : 378 pages
Book Rating : 4.7/5 (839 download)
Download or read book Mastering Gephi Network Visualization written by Ken Cherven and published by Packt Publishing Ltd. This book was released on 2015-01-28 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is intended for anyone interested in advanced network analysis. If you wish to master the skills of analyzing and presenting network graphs effectively, then this is the book for you. No coding experience is required to use this book, although some familiarity with the Gephi user interface will be helpful.
Author : Josh Kalin
Publisher : Packt Publishing Ltd
ISBN 13 : 1789139589
Total Pages : 261 pages
Book Rating : 4.7/5 (891 download)
Download or read book Generative Adversarial Networks Cookbook written by Josh Kalin and published by Packt Publishing Ltd. This book was released on 2018-12-31 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt: Simplify next-generation deep learning by implementing powerful generative models using Python, TensorFlow and Keras Key FeaturesUnderstand the common architecture of different types of GANsTrain, optimize, and deploy GAN applications using TensorFlow and KerasBuild generative models with real-world data sets, including 2D and 3D dataBook Description Developing Generative Adversarial Networks (GANs) is a complex task, and it is often hard to find code that is easy to understand. This book leads you through eight different examples of modern GAN implementations, including CycleGAN, simGAN, DCGAN, and 2D image to 3D model generation. Each chapter contains useful recipes to build on a common architecture in Python, TensorFlow and Keras to explore increasingly difficult GAN architectures in an easy-to-read format. The book starts by covering the different types of GAN architecture to help you understand how the model works. This book also contains intuitive recipes to help you work with use cases involving DCGAN, Pix2Pix, and so on. To understand these complex applications, you will take different real-world data sets and put them to use. By the end of this book, you will be equipped to deal with the challenges and issues that you may face while working with GAN models, thanks to easy-to-follow code solutions that you can implement right away. What you will learnStructure a GAN architecture in pseudocodeUnderstand the common architecture for each of the GAN models you will buildImplement different GAN architectures in TensorFlow and KerasUse different datasets to enable neural network functionality in GAN modelsCombine different GAN models and learn how to fine-tune themProduce a model that can take 2D images and produce 3D modelsDevelop a GAN to do style transfer with Pix2PixWho this book is for This book is for data scientists, machine learning developers, and deep learning practitioners looking for a quick reference to tackle challenges and tasks in the GAN domain. Familiarity with machine learning concepts and working knowledge of Python programming language will help you get the most out of the book.
Author : Jason Brownlee
Publisher : Machine Learning Mastery
ISBN 13 :
Total Pages : 572 pages
Book Rating : 4./5 ( download)
Download or read book Deep Learning for Time Series Forecasting written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2018-08-30 with total page 572 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning methods offer a lot of promise for time series forecasting, such as the automatic learning of temporal dependence and the automatic handling of temporal structures like trends and seasonality. With clear explanations, standard Python libraries, and step-by-step tutorial lessons you’ll discover how to develop deep learning models for your own time series forecasting projects.
Author : Sarah Hall
Publisher :
ISBN 13 : 9781364857745
Total Pages : pages
Book Rating : 4.8/5 (577 download)
Download or read book #FuturePRoof written by Sarah Hall and published by . This book was released on 2015-11-01 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The largest ever conversation about the future of public relations. Thirty-five leading practitioners debate PR's role as a management discipline, the value it adds to modern day organisations and the skills public relations professionals need to stay relevant both now and in the future.
Author : Antonio Gulli
Publisher : Packt Publishing Ltd
ISBN 13 : 1787129039
Total Pages : 310 pages
Book Rating : 4.7/5 (871 download)
Download or read book Deep Learning with Keras written by Antonio Gulli and published by Packt Publishing Ltd. This book was released on 2017-04-26 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get to grips with the basics of Keras to implement fast and efficient deep-learning models About This Book Implement various deep-learning algorithms in Keras and see how deep-learning can be used in games See how various deep-learning models and practical use-cases can be implemented using Keras A practical, hands-on guide with real-world examples to give you a strong foundation in Keras Who This Book Is For If you are a data scientist with experience in machine learning or an AI programmer with some exposure to neural networks, you will find this book a useful entry point to deep-learning with Keras. A knowledge of Python is required for this book. What You Will Learn Optimize step-by-step functions on a large neural network using the Backpropagation Algorithm Fine-tune a neural network to improve the quality of results Use deep learning for image and audio processing Use Recursive Neural Tensor Networks (RNTNs) to outperform standard word embedding in special cases Identify problems for which Recurrent Neural Network (RNN) solutions are suitable Explore the process required to implement Autoencoders Evolve a deep neural network using reinforcement learning In Detail This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing with recognition of hand written digit images, classification of images into different categories, and advanced objects recognition with related image annotations. An example of identification of salient points for face detection is also provided. Next you will be introduced to Recurrent Networks, which are optimized for processing sequence data such as text, audio or time series. Following that, you will learn about unsupervised learning algorithms such as Autoencoders and the very popular Generative Adversarial Networks (GAN). You will also explore non-traditional uses of neural networks as Style Transfer. Finally, you will look at Reinforcement Learning and its application to AI game playing, another popular direction of research and application of neural networks. Style and approach This book is an easy-to-follow guide full of examples and real-world applications to help you gain an in-depth understanding of Keras. This book will showcase more than twenty working Deep Neural Networks coded in Python using Keras.
Author : Christian Cote
Publisher : Packt Publishing Ltd
ISBN 13 : 1786460874
Total Pages : 551 pages
Book Rating : 4.7/5 (864 download)
Download or read book SQL Server 2017 Integration Services Cookbook written by Christian Cote and published by Packt Publishing Ltd. This book was released on 2017-06-30 with total page 551 pages. Available in PDF, EPUB and Kindle. Book excerpt: Harness the power of SQL Server 2017 Integration Services to build your data integration solutions with ease About This Book Acquaint yourself with all the newly introduced features in SQL Server 2017 Integration Services Program and extend your packages to enhance their functionality This detailed, step-by-step guide covers everything you need to develop efficient data integration and data transformation solutions for your organization Who This Book Is For This book is ideal for software engineers, DW/ETL architects, and ETL developers who need to create a new, or enhance an existing, ETL implementation with SQL Server 2017 Integration Services. This book would also be good for individuals who develop ETL solutions that use SSIS and are keen to learn the new features and capabilities in SSIS 2017. What You Will Learn Understand the key components of an ETL solution using SQL Server 2016-2017 Integration Services Design the architecture of a modern ETL solution Have a good knowledge of the new capabilities and features added to Integration Services Implement ETL solutions using Integration Services for both on-premises and Azure data Improve the performance and scalability of an ETL solution Enhance the ETL solution using a custom framework Be able to work on the ETL solution with many other developers and have common design paradigms or techniques Effectively use scripting to solve complex data issues In Detail SQL Server Integration Services is a tool that facilitates data extraction, consolidation, and loading options (ETL), SQL Server coding enhancements, data warehousing, and customizations. With the help of the recipes in this book, you'll gain complete hands-on experience of SSIS 2017 as well as the 2016 new features, design and development improvements including SCD, Tuning, and Customizations. At the start, you'll learn to install and set up SSIS as well other SQL Server resources to make optimal use of this Business Intelligence tools. We'll begin by taking you through the new features in SSIS 2016/2017 and implementing the necessary features to get a modern scalable ETL solution that fits the modern data warehouse. Through the course of chapters, you will learn how to design and build SSIS data warehouses packages using SQL Server Data Tools. Additionally, you'll learn to develop SSIS packages designed to maintain a data warehouse using the Data Flow and other control flow tasks. You'll also be demonstrated many recipes on cleansing data and how to get the end result after applying different transformations. Some real-world scenarios that you might face are also covered and how to handle various issues that you might face when designing your packages. At the end of this book, you'll get to know all the key concepts to perform data integration and transformation. You'll have explored on-premises Big Data integration processes to create a classic data warehouse, and will know how to extend the toolbox with custom tasks and transforms. Style and approach This cookbook follows a problem-solution approach and tackles all kinds of data integration scenarios by using the capabilities of SQL Server 2016 Integration Services. This book is well supplemented with screenshots, tips, and tricks. Each recipe focuses on a particular task and is written in a very easy-to-follow manner.
