Powerful, independent recipes to build deep learning models in different application areas using R libraries About This Book * Master intricacies of R deep learning packages such as mxnet & tensorflow * Learn application on deep learning in different domains using practical examples from text, image and speech * Guide to set-up deep learning models using CPU and GPU Who This Book Is For Data science professionals or analysts who have performed machine learning tasks and now want to explore deep learning and want a quick reference that could address the pain points while implementing deep learning. Those who wish to have an edge over other deep learning professionals will find this book quite useful. What You Will Learn * Build deep learning models in different application areas using TensorFlow, H2O, and MXnet. * Analyzing a Deep boltzmann machine * Setting up and Analysing Deep belief networks * Building supervised model using various machine learning algorithms * Set up variants of basic convolution function * Represent data using Autoencoders. * Explore generative models available in Deep Learning. * Discover sequence modeling using Recurrent nets * Learn fundamentals of Reinforcement Leaning * Learn the steps involved in applying Deep Learning in text mining * Explore application of deep learning in signal processing * Utilize Transfer learning for utilizing pre-trained model * Train a deep learning model on a GPU In Detail Deep Learning is the next big thing. It is a part of machine learning. It's favorable results in applications with huge and complex data is remarkable. Simultaneously, R programming language is very popular amongst the data miners and statisticians. This book will help you to get through the problems that you face during the execution of different tasks and Understand hacks in deep learning, neural networks, and advanced machine learning techniques. It will also take you through complex deep learning algorithms and various deep learning packages and libraries in R. It will be starting with different packages in Deep Learning to neural networks and structures. You will also encounter the applications in text mining and processing along with a comparison between CPU and GPU performance. By the end of the book, you will have a logical understanding of Deep learning and different deep learning packages to have the most appropriate solutions for your problems. Style and approach Collection of hands-on recipes that would act as your all-time reference for your deep learning needs

Increase speed and performance of your applications with efficient data structures and algorithms About This Book * See how to use data structures such as arrays, stacks, trees, lists, and graphs through real-world examples * Find out about important and advanced data structures such as searching and sorting algorithms * Understand important concepts such as big-o notation, dynamic programming, and functional data structured Who This Book Is For This book is for R developers who want to use data structures efficiently. Basic knowledge of R is expected. What You Will Learn * Understand the rationality behind data structures and algorithms * Understand computation evaluation of a program featuring asymptotic and empirical algorithm analysis * Get to know the fundamentals of arrays and linked-based data structures * Analyze types of sorting algorithms * Search algorithms along with hashing * Understand linear and tree-based indexing * Be able to implement a graph including topological sort, shortest path problem, and Prim's algorithm * Understand dynamic programming (Knapsack) and randomized algorithms In Detail In this book, we cover not only classical data structures, but also functional data structures. We begin by answering the fundamental question: why data structures? We then move on to cover the relationship between data structures and algorithms, followed by an analysis and evaluation of algorithms. We introduce the fundamentals of data structures, such as lists, stacks, queues, and dictionaries, using real-world examples. We also cover topics such as indexing, sorting, and searching in depth. Later on, you will be exposed to advanced topics such as graph data structures, dynamic programming, and randomized algorithms. You will come to appreciate the intricacies of high performance and scalable programming using R. We also cover special R data structures such as vectors, data frames, and atomic vectors. With this easy-to-read book, you will be able to understand the power of linked lists, double linked lists, and circular linked lists. We will also explore the application of binary search and will go in depth into sorting algorithms such as bubble sort, selection sort, insertion sort, and merge sort. Style and approach This easy-to-read book with its fast-paced nature will improve the productivity of an R programmer and improve the performance of R applications. It is packed with real-world examples.

Step by Step guide filled with real world practical examples. About This Book * Get your first experience with data analysis with one of the most powerful types of analysis-time-series. * Find patterns in your data and predict the future pattern based on historical data. * Learn the statistics, theory, and implementation of Time-series methods using this example-rich guide Who This Book Is For This book is for anyone who wants to analyze data over time and/or frequency. A statistical background is necessary to quickly learn the analysis methods. What You Will Learn * Understand the basic concepts of Time Series Analysis and appreciate its importance for the success of a data science project * Develop an understanding of loading, exploring, and visualizing time-series data * Explore auto-correlation and gain knowledge of statistical techniques to deal with non-stationarity time series * Take advantage of exponential smoothing to tackle noise in time series data * Learn how to use auto-regressive models to make predictions using time-series data * Build predictive models on time series using techniques based on auto-regressive moving averages * Discover recent advancements in deep learning to build accurate forecasting models for time series * Gain familiarity with the basics of Python as a powerful yet simple to write programming language In Detail Time Series Analysis allows us to analyze data which is generated over a period of time and has sequential interdependencies between the observations. This book describes special mathematical tricks and techniques which are geared towards exploring the internal structures of time series data and generating powerful descriptive and predictive insights. Also, the book is full of real-life examples of time series and their analyses using cutting-edge solutions developed in Python. The book starts with descriptive analysis to create insightful visualizations of internal structures such as trend, seasonality and autocorrelation. Next, the statistical methods of dealing with autocorrelation and non-stationary time series are described. This is followed by exponential smoothing to produce meaningful insights from noisy time series data. At this point, we shift focus towards predictive analysis and introduce autoregressive models such as ARMA and ARIMA for time series forecasting. Later, powerful deep learning methods are presented, to develop accurate forecasting models for complex time series, and under the availability of little domain knowledge. All the topics are illustrated with real-life problem scenarios and their solutions by best-practice implementations in Python. The book concludes with the Appendix, with a brief discussion of programming and solving data science problems using Python. Style and approach This book takes the readers from the basic to advance level of Time series analysis in a very practical and real world use cases.