Computer Science Price list in India

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Python Deep Learning

Python Deep Learning

Take your machine learning skills to the next level by mastering Deep Learning concepts and algorithms using Python. About This Book. Explore and create intelligent systems using cutting-edge deep learning techniques. Implement deep learning algorithms and work with revolutionary libraries in Python. Get real-world examples and easy-to-follow tutorials on Theano, TensorFlow, H2O and more Who This Book Is For This book is for Data Science practitioners as well as aspirants who have a basic foundational understanding of Machine Learning concepts and some programming experience with Python. A mathematical background with a conceptual understanding of calculus and statistics is also desired. What You Will Learn. Get a practical deep dive into deep learning algorithms. Explore deep learning further with Theano, Caffe, Keras, and TensorFlow. Learn about two of the most powerful techniques at the core of many practical deep learning implementations: Auto-Encoders and Restricted Boltzmann Machines. Dive into Deep Belief Nets and Deep Neural Networks. Discover more deep learning algorithms with Dropout and Convolutional Neural Networks. Get to know device strategies so you can use deep learning algorithms and libraries in the real world In Detail With an increasing interest in AI around the world, deep learning has attracted a great deal of public attention. Every day, deep learning algorithms are used broadly across different industries. The book will give you all the practical information available on the subject, including the best practices, using real-world use cases. You will learn to recognize and extract information to increase predictive accuracy and optimize results. Starting with a quick recap of important machine learning concepts, the book will delve straight into deep learning principles using Sci-kit learn. Moving ahead, you will learn to use the latest open source libraries such as Theano, Keras, Googles TensorFlow, and H2. Use this guide to uncover the difficulties of pattern recognition, scaling data with greater accuracy and discussing deep learning algorithms and techniques. Whether you want to dive deeper into Deep Learning, or want to investigate how to get more out of this powerful technology, youll find everything inside. Style and approach Python Machine Learning by example follows practical hands on approach. It walks you through the key elements of Python and its powerful machine learning libraries with the help of real world projects. About the Author Valentino Zocca graduated with a PhD in mathematics from the University of Maryland, USA, with a dissertation in symplectic geometry, after having graduated with a laurea in mathematics from the University of Rome. He spent a semester at the University of Warwick. After a post-doc in Paris, Valentino started working on hightech projects in the Washington, D.C. area and played a central role in the design, development, and realization of an advanced stereo 3D Earth visualization software with head tracking at Autometric, a company later bought by Boeing. At Boeing, he developed many mathematical algorithms and predictive models, and using Hadoop, he has also automated several satellite-imagery visualization programs. He has since become an expert on machine learning and deep learning and has worked at the U.S. Census Bureau and as an independent consultant both in the US and in Italy. He has also held seminars on the subject of machine and deep learning in Milan and New York. Currently, Valentino lives in New York and works as an independent consultant to a large financial company, where he develops econometric models and uses machine learning and deep learning to create predictive models. But he often travels back to Rome and Milan to visit his family and friends. Gianmario Spacagna is a senior data scientist at Pirelli, processing sensors and telemetry data for IoT and connected-vehicle applications. He works closely with tyre mechanics, engineers, and business units to analyze and formulate hybrid, physics-driven, and data-driven automotive models. His main expertise is in building machine learning systems and end-to-end solutions for data products. He is the coauthor of the Professional Data Science Manifesto (datasciencemanifesto.org) and founder of the Data Science Milan meetup community (datasciencemilan.org). Gianmario loves evangelizing his passion for best practices and effective methodologies in the community. He holds a masters degree in telematics from the Polytechnic of Turin and software engineering of distributed systems from KTH, Stockholm. Prior to Pirelli, he worked in retail and business banking (Barclays), cyber security (Cisco), predictive marketing (AgilOne), and some occasional freelancing. Daniel Slater started programming at age 11, developing mods for the id Software game Quake. His obsession led him to become a developer working in the gaming industry on the hit computer game series Championship Manager. He then moved into finance, working on risk- and high-performance messaging systems. He now is a staff engineer, working on big data at Skimlinks to understand online user behavior. He spends his spare time training AI to beat computer games. He talks at tech conferences about deep learning and reinforcement learning; his blog can be found at www.danielslater.net. His work in this field has been cited by Google. Peter Roelants holds a masters in computer science with a specialization in artificial intelligence from KU Leuven. He works on applying deep learning to a variety of problems, such as spectral imaging, speech recognition, text understanding, and document information extraction. He currently works at Onfido as a team lead for the data extraction research team, focusing on data extraction from official documents.

Deep Learning for Computer Vision: Expert techniques to train advanced neural networks using TensorFlow and Keras

Deep Learning for Computer Vision: Expert techniques to train advanced neural networks using TensorFlow and Keras

Learn how to model and train advanced neural networks to implement a variety of Computer Vision tasks Key Features Train different kinds of deep learning model from scratch to solve specific problems in Computer Vision Combine the power of Python, Keras, and TensorFlow to build deep learning models for object detection, image classification, similarity learning, image captioning, and more Includes tips on optimizing and improving the performance of your models under various constraints Book DescriptionDeep learning has shown its power in several application areas of Artificial Intelligence, especially in Computer Vision. Computer Vision is the science of understanding and manipulating images, and finds enormous applications in the areas of robotics, automation, and so on. This book will also show you, with practical examples, how to develop Computer Vision applications by leveraging the power of deep learning. In this book, you will learn different techniques related to object classification, object detection, image segmentation, captioning, image generation, face analysis, and more. You will also explore their applications using popular Python libraries such as TensorFlow and Keras. This book will help you master state-of-the-art, deep learning algorithms and their implementation. What you will learn Set up an environment for deep learning with Python, TensorFlow, and Keras Define and train a model for image and video classification Use features from a pre-trained Convolutional Neural Network model for image retrieval Understand and implement object detection using the real-world Pedestrian Detection scenario Learn about various problems in image captioning and how to overcome them by training images and text together Implement similarity matching and train a model for face recognition Understand the concept of generative models and use them for image generation Deploy your deep learning models and optimize them for high performance Who this book is forThis book is targeted at data scientists and Computer Vision practitioners who wish to apply the concepts of Deep Learning to overcome any problem related to Computer Vision. A basic knowledge of programming in Python-and some understanding of machine learning concepts-is required to get the best out of this book.

How Computers Work

How Computers Work

Having sold more than 2 million copies over its lifetime, it is the definitive illustrated guide to the world of PCs and technology. In this new edition, you’ll find detailed information not just about PCs, but about how changes in technology have evolved the giant, expensive computer dinosaurs of last century into the smaller but more powerful smartphones, tablets, and wearable computing of today. Whether your interest is in business, gaming, digital photography, entertainment, communications, or security, you’ll learn how computing is evolving the way you live. Only the accomplished and award-winning team of writer has the unique ability to meld descriptive text with one-of-a-kind visuals to fully explain how the electronic gear we depend on every day is made possible. In addition to all the content you’ve come to expect from prior editions, this newly revised edition includes all-new coverage of topics such as:
This full-color, fully illustrated guide to the world of technology assumes nothing and explains everything

  • How Computers Remember
  • How a Little Microprocessor Does Big Things
  • How Multi-Core Processors Work
  • How Motherboards / Software / Compiler Work
  • How Databases Track Everything & Make Connections
  • How Spread sheets (Excel) Solve Formulas
  • How Numbers Become Pictures
  • How Imaging Software Paints by Numbers
  • How Games Created and How Computer Creates 3D World
  • How Security Software Fights Off Invaders
  • How Computer Hackers Break In
  • How Viruses Invade Your Computer
  • How Antivirus Software Fights Back
  • How Firewalls Keep Hackers Out & How Spammers Find You
  • How USB Really Is Universal
  • How File Compression Makes Files Smaller
  • How Optical Disc Drives Write with Light
  • How Computers Get Smaller… and Better
  • How iPods serves Media
  • How eInk Puts Words on Your e-reader (KINDLE)
  • How All These Smart features Got Packed into a Smartphone
  • How Advanced Cooling Refrigerates Your PC
  • How Digital Cameras Capture the Moment
  • How Your Smartphone Knows Where You Are
  • How Devices Recognize Our Touch (TOUCH SCREEN)
  • How Optical Character Recognition Works
  • How an LCD/ Plasma/ DLP/ OLED works
  • How Your Device Listens
  • How 3D Audio Surrounds
  • How Networks Tie Computers Together
  • How Wi-Fi / Bluetooth works
  • How the Internet Brings Us the World
  • How Broadband / DSl/ Cables Brings the Internet to Your Neighbourhood
  • How Fibre Optics Lights Up the Future
  • How Computers Make Phone Calls
  • How Information Travels the Internet
  • How Movies Flow into Your Home
  • How Google Knows Everything
  • How eBay Sells Everything
  • How Email/ Facebook/ Twitter works
  • How Internet File Sharing Works
  • How Bit Torrents / Clouds Works
  • How Black and White / Colour Printing Works
  • How a Laser Printer Creates in Color
  • How Printers Create in 3D and lots more….

This is the perfect book about computing to capture your imagination, delight your eyes, and expand your mind, no matter what your technical level! Beautifully detailed illustrations and jargon-free explanations walk you through the technology that is shaping our lives. See the hidden workings inside computers, smartphones, tablets, Google Glass, and the latest tech inventions.

Computer Repair with Diagnostic Flowcharts Third Edition: Troubleshooting PC Hardware Problems from Boot Failure to Poor Performance

Computer Repair with Diagnostic Flowcharts Third Edition: Troubleshooting PC Hardware Problems from Boot Failure to Poor Performance

The updated edition of the classic visual manual for troubleshooting PC hardware problems. Morris Rosenthal creates a visual expert system for diagnosing component failure and identifying conflicts. The seventeen diagnostic flowcharts at the core of this book are intended for the intermediate to advanced hobbyist, or the beginning technician. Following a structured approach to troubleshooting hardware reduces the false diagnoses and parts wastage typical of the "swap 'till you drop" school of thought. Flowcharts include: Power Supply Failure, Video Failure, Video Performance, Motherboard, CPU, RAM Failure, Motherboard, CPU, RAM Performance, IDE Drive Failure, Hard Drive Boot and Performance, CD, DVD or Blu-ray Playback, CD or DVD Recording Problem, Modem Failure, Modem Performance, Sound Failure, Sound and Game Controller Performance, Network Failure, Peripheral Failure, SCSI Failure, and Conflict Resolution. Computer Repair with Diagnostic Flowcharts is used as a classroom text in colleges and technical schools and by the U.S. government for training forensic technicians. It's also a favorite reference with consumers and technicians all over the world.

Network Management: Principles and Practice, 2e

Network Management: Principles and Practice, 2e

This edition is thoroughly updated and expanded to address broadband network management and the latest trends in the network management technology and standards. The author's unique approach thoroughly illustrates the theoretical and practical aspects of network management and the technologies and the tools that academics and network managers simply must know.

The Algorithm Design Manual

The Algorithm Design Manual

This newly expanded and updated second edition of the best-selling classic continues to take the "mystery" out of designing algorithms, and analyzing their efficacy and efficiency. Expanding on the first edition, the book now serves as the primary textbook of choice for algorithm design courses while maintaining its status as the premier practical reference guide to algorithms for programmers, researchers, and students.

The reader-friendly Algorithm Design Manual provides straightforward access to combinatorial algorithms technology, stressing design over analysis. The first part, Techniques, provides accessible instruction on methods for designing and analyzing computer algorithms. The second part, Resources, is intended for browsing and reference, and comprises the catalog of algorithmic resources, implementations and an extensive bibliography.

NEW to the second edition:

• Doubles the tutorial material and exercises over the first edition

• Provides full online support for lecturers, and a completely updated and improved website component with lecture slides, audio and video

• Contains a unique catalog identifying the 75 algorithmic problems that arise most often in practice, leading the reader down the right path to solve them

• Includes several NEW "war stories" relating experiences from real-world applications

 Provides up-to-date links leading to the very best algorithm implementations available in C, C++, and Java

Algorithms Illuminated (Part 3): Greedy Algorithms and Dynamic Programming

Algorithms Illuminated (Part 3): Greedy Algorithms and Dynamic Programming

Algorithms are the heart and soul of computer science. Their applications range from network routing and computational genomics to public-key cryptography and machine learning. Studying algorithms can make you a better programmer, a clearer thinker, and a master of technical interviews. Algorithms Illuminated is an accessible introduction to the subject for anyone with at least a little programming experience. The exposition emphasizes the big picture and conceptual understanding over low-level implementation and mathematical details---like a transcript of what an expert algorithms tutor would say over a series of one-on-one lessons. Part 3 covers greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes) and dynamic programming (knapsack, sequence alignment, shortest paths, optimal search trees).

Algorithms Illuminated (Part 2): Graph Algorithms and Data Structures: Volume 2

Algorithms Illuminated (Part 2): Graph Algorithms and Data Structures: Volume 2

Algorithms are the heart and soul of computer science. Their applications range from network routing and computational genomics to public-key cryptography and machine learning. Studying algorithms can make you a better programmer, a clearer thinker, and a master of technical interviews. Algorithms Illuminated is an accessible introduction to the subject for anyone with at least a little programming experience. The exposition emphasizes the big picture and conceptual understanding over low-level implementation and mathematical details---like a transcript of what an expert algorithms tutor would say over a series of one-on-one lessons. Part 2 covers graph search and applications, shortest paths, and the usage and implementation of several data structures (heaps, search trees, hash tables, and bloom filters).

Microsoft Outlook 2016 Step By Step

Microsoft Outlook 2016 Step By Step

The quick way to learn outlook! - This is learning made easy. Get productive fast with outlook 2016 and jump in wherever you need answers-briskk lessons and colorful screen shots show you exactly what to do, step by step. - stay organized and stay connected with outlook - Set up email and social media accounts - manage one or more Calendars and share your schedule with others - track your tasks and to-do lists - send, search, filter and organize messages.

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