Rahul Kumar

This book has been prepared chapter wise on the basis of questions asked in English in various competitive exams. It's very useful for candidates which preparing competition exams like : Bank PO, Clerk, MBA, SSC, MAT, LIC, GIC, RBI, and others competitive Examinations.

Easy English

Matthew Lamons

Abhishek Nagaraja

Insightful projects to master deep learning and neural network architectures using Python and Keras Key FeaturesExplore deep learning across computer vision, natural language processing (NLP), and image processingDiscover best practices for the training of deep neural networks and their deploymentAccess popular deep learning models as well as widely used neural network architecturesBook Description Deep learning has been gradually revolutionizing every field of artificial intelligence, making application development easier. Python Deep Learning Projects imparts all the knowledge needed to implement complex deep learning projects in the field of computational linguistics and computer vision. Each of these projects is unique, helping you progressively master the subject. You’ll learn how to implement a text classifier system using a recurrent neural network (RNN) model and optimize it to understand the shortcomings you might experience while implementing a simple deep learning system. Similarly, you’ll discover how to develop various projects, including word vector representation, open domain question answering, and building chatbots using seq-to-seq models and language modeling. In addition to this, you’ll cover advanced concepts, such as regularization, gradient clipping, gradient normalization, and bidirectional RNNs, through a series of engaging projects. By the end of this book, you will have gained knowledge to develop your own deep learning systems in a straightforward way and in an efficient way What you will learnSet up a deep learning development environment on Amazon Web Services (AWS)Apply GPU-powered instances as well as the deep learning AMIImplement seq-to-seq networks for modeling natural language processing (NLP)Develop an end-to-end speech recognition systemBuild a system for pixel-wise semantic labeling of an imageCreate a system that generates images and their regionsWho this book is for Python Deep Learning Projects is for you if you want to get insights into deep learning, data science, and artificial intelligence. This book is also for those who want to break into deep learning and develop their own AI projects. It is assumed that you have sound knowledge of Python programming

9 projects demystifying neural network and deep learning models for building intelligent systems

Python Deep Learning Projects

R. Jay Wallace

Samuel Freeman

Reasons and Recognition brings together fourteen new papers on an array of topics from the many areas to which philosopher Thomas Scanlon has made path-breaking contributions, each of which develops a distinctive and independent position while critically engaging with central themes from Scanlon's own work in the area.

Essays on the Philosophy of T.M. Scanlon

Reasons and Recognition

Your hands-on reference guide to developing, training, and optimizing your machine learning models Key Features Your guide to learning efficient machine learning processes from scratch Explore expert techniques and hacks for a variety of machine learning concepts Write effective code in R, Python, Scala, and Spark to solve all your machine learning problems Book Description Machine learning makes it possible to learn about the unknowns and gain hidden insights into your datasets by mastering many tools and techniques. This book guides you to do just that in a very compact manner. After giving a quick overview of what machine learning is all about, Machine Learning Quick Reference jumps right into its core algorithms and demonstrates how they can be applied to real-world scenarios. From model evaluation to optimizing their performance, this book will introduce you to the best practices in machine learning. Furthermore, you will also look at the more advanced aspects such as training neural networks and work with different kinds of data, such as text, time-series, and sequential data. Advanced methods and techniques such as causal inference, deep Gaussian processes, and more are also covered. By the end of this book, you will be able to train fast, accurate machine learning models at your fingertips, which you can easily use as a point of reference. What you will learn Get a quick rundown of model selection, statistical modeling, and cross-validation Choose the best machine learning algorithm to solve your problem Explore kernel learning, neural networks, and time-series analysis Train deep learning models and optimize them for maximum performance Briefly cover Bayesian techniques and sentiment analysis in your NLP solution Implement probabilistic graphical models and causal inferences Measure and optimize the performance of your machine learning models Who this book is for If you’re a machine learning practitioner, data scientist, machine learning developer, or engineer, this book will serve as a reference point in building machine learning solutions. You will also find this book useful if you’re an intermediate machine learning developer or data scientist looking for a quick, handy reference to all the concepts of machine learning. You’ll need some exposure to machine learning to get the best out of this book.

Quick and essential machine learning hacks for training smart data models

Machine Learning Quick Reference

Yuvnesh Modi

Alok Kothari

Entrepreneurship is not about breaking free from the 9 to 5 humdrum, not about being your own boss, and definitely not glorious. The entrepreneur shuns the comfort of a cushy corporate job and six figure salaries to set sail on unchartered waters with a single minded zeal and only an idea as an anchor. But it is this idea and passion that makes all the difference and catapults them into a world of infinite possibilities. The Game Changers brings to you 20 success stories of IITians who went on to live the big dream. These include: Suhas Patil, Vijay Kumar, Vinod Gupta, Sam Dalal, Sridhar Mitta, Arjun Malhotra, Kiran Seth, Prabhakant Sinha, Ranbir Singh Gupta, Bikram Dasgupta founder of Globsyn, Praful Kulkarni, Sunil Gaitonde, Anand Deshpande, Arvind Kejriwal, Harish Hande, Anuradha Acharya, Venkata Subramanian, Bikash Barai, Vikram Kumar, and Krishna Mehra. With a foreword by Dr Duvvuri Subbarao, Governor, Reserve Bank of India, and introduction by Damodar Acharya, Director, IIT Karagpur, this book marks the 60 golden years of India’s finest institute. Come, be a part of their journey, get inspired to dream and make your own story.

20 extraordinary success stories of enterpreneurs from IIT Kharagpur

The Game Changers

In this book "Why Business Fail?"; your all queries related to the failure of business to million dollar business, will resolve quickly. This book provides you various ways to build your venture from scratch. The life principles that "Rahul Kumar" has gained so far in terms of Life and Business, he explained various strategies and action oriented steps which help you in building your successful business visit: http://www.merahulkumar.com

Don't let your dream fade away

Why Business Fail

Existing human beings stand in a unique relationship of asymmetrical influence over future generations. Our choices now can settle whether there are any human beings in the further future; how many will exist; what capacities and abilities they might have; and what the character of the natural world they inhabit is like. This volume, with contributions from both new voices and prominent, established figures in moral and political philosophy, examines three generally underexplored themes concerning morality and our relationship to future generations. First, would it be morally wrong to allow humanity to go extinct? Or do we have moral reasons to try and ensure that humanity continues into the indefinite future? Second, if humanity is to continue into the future, how many people should there be? And is it morally important whether they have lives that are of high quality or are just barely worth living? And third, how can we best make sense of the intuitive idea that by not taking action on climate change and preserving natural resources, we are in some way wronging future generations? This book was originally published as a special issue of the Canadian Journal of Philosophy.

Rahul Kumar

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