Prakash pks (59 risultati)

Condizione: Very Good. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

paperback. Condizione: Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority.

Condizione: very_good.

Condizione: good. May show signs of wear, highlighting, writing, and previous use. This item may be a former library book with typical markings. No guarantee on products that contain supplements Your satisfaction is 100% guaranteed. Twenty-five year bookseller with shipments to over fifty million happy customers.…

paperback. Condizione: Good. Ships in a BOX from Central Missouri! May not include working access code. Will not include dust jacket. Has used sticker(s) and some writing and/or highlighting. UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).

paperback. Condizione: Good. Ships in a BOX from Central Missouri! May not include working access code. Will not include dust jacket. Has used sticker(s) and some writing or highlighting. UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).

Paperback. Condizione: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.

Condizione: New.

Paperback or Softback. Condizione: New. R Data Structures and Algorithms: Increase speed and performance of your applications with effi cient data structures and algorithms. Book.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Condizione: New.

Paperback or Softback. Condizione: New. R Deep Learning Cookbook: Solve complex neural net problems with TensorFlow, H2O and MXNet. Book.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Digital. Condizione: New. Powerful, independent recipes to build deep learning models in different application areas using R librariesAbout This Book. Master intricacies of R deep learning packages such as mxnet and 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 GPUWho This Book Is ForData 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 GPUIn DetailDeep 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 approachCollection of hands-on recipes that would act as your all-time reference for your deep learning needs.…

Digital. Condizione: New. 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 guideWho This Book Is ForThis 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 languageIn DetailTime 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 approachThis book takes the readers from the basic to advance level of Time series analysis in a very practical and real world use cases.…

Condizione: New.

Condizione: New. pp. 244.

Digital. Condizione: New. Powerful, independent recipes to build deep learning models in different application areas using R librariesAbout This Book. Master intricacies of R deep learning packages such as mxnet and 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 GPUWho This Book Is ForData 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 GPUIn DetailDeep 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 approachCollection of hands-on recipes that would act as your all-time reference for your deep learning needs.…

Condizione: New. pp. 288.

paperback. Condizione: New.

Paperback. Condizione: New.

Condizione: New.

Paperback. Condizione: New.

Condizione: New. In English.

Condizione: New.