Rahul Kumar

Every level of our life presents different challenges to us. To Face the challenges at each level, we are required to become a different us. In this book, correct diagnosis for the various issues that an Indian youth faces in his life have been made and solutions in simple language have been suggested for the same.Common myths regarding various challenges have also been broken down that often cause stagnation and regress in our life. Young mind always strives to do away with the mark practices and keep going forward while determining the peripheries of the new times. This book is an ode to the spirit of the youthful valour.Another beautiful feature of this book is that the views presented by its author have been made in the form of beautiful poems and songs.This book demonstrates the hopeful overview of the youthful mind and aspires to attract those of kind.

Your hands-on reference guide to developing, training, and optimizing your machine learning modelsKey FeaturesYour guide to learning efficient machine learning processes from scratchExplore expert techniques and hacks for a variety of machine learning conceptsWrite effective code in R, Python, Scala, and Spark to solve all your machine learning problemsBook DescriptionMachine 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 learnGet a quick rundown of model selection, statistical modeling, and cross-validationChoose the best machine learning algorithm to solve your problemExplore kernel learning, neural networks, and time-series analysisTrain deep learning models and optimize them for maximum performanceBriefly cover Bayesian techniques and sentiment analysis in your NLP solutionImplement probabilistic graphical models and causal inferencesMeasure and optimize the performance of your machine learning modelsWho this book is forIf 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.

Insightful projects to master deep learning and neural network architectures using Python and KerasKey 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 DescriptionDeep 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 wayWhat 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 forPython 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

This book presents and argues for a suitably articulated version of consensualism as a form of Kantian moral theory with an ability to powerfully illuminate the moral intuitions to which Kantian and utilitarian theories have traditionally appealed.