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Numerical Python, 2nd Edition

Numerical Python, 2nd Edition

by Robert Johansson

Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute solutions and mathematically model applications in big data, cloud computin...

Price:  $35.25  |  Publisher:  Apress  |  Release:  2019

Grokking Deep Learning

Grokking Deep Learning

by Andrew W. Trask

Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks.Deep learning, a branch of artificial intelligence, teaches computers to learn by using neural networks, technology inspired by the human brain. Online ...

Price:  $39.99  |  Publisher:  Manning  |  Release:  2019

Python Data Analytics, 2nd Edition

Python Data Analytics, 2nd Edition

by Fabio Nelli

Explore the latest Python tools and techniques to help you tackle the world of data acquisition and analysis. You'll review scientific computing with NumPy, visualization with matplotlib, and machine learning with scikit-learn.This revision is fully updated with new content on social media data analysis, image analysis with OpenCV, and deep learning libraries. Each chapter includes multiple examples demonst...

Price:  $30.41  |  Publisher:  Apress  |  Release:  2018

PySpark Recipes

PySpark Recipes

by Raju Kumar Mishra

Quickly find solutions to common programming problems encountered while processing big data. Content is presented in the popular problem-solution format. Look up the programming problem that you want to solve. Read the solution. Apply the solution directly in your own code. Problem solved!PySpark Recipes covers Hadoop and its shortcomings. The architecture of Spark, PySpark, and RDD are presented. You will ...

Price:  $33.35  |  Publisher:  Apress  |  Release:  2018

Applied Deep Learning

Applied Deep Learning

by Umberto Michelucci

Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You'll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid, and Swish), seeing how to perform linear and logistic regression using TensorFlow, and ch...

Price:  $22.50  |  Publisher:  Apress  |  Release:  2018

Python for Data Mining Quick Syntax Reference

Python for Data Mining Quick Syntax Reference

by Valentina Porcu

Learn how to use Python and its structures, how to install Python, and which tools are best suited for data analyst work. This book provides you with a handy reference and tutorial on topics ranging from basic Python concepts through to data mining, manipulating and importing datasets, and data analysis.Python for Data Mining Quick Syntax Reference covers each concept concisely, with many illustrative examp...

Price:  $23.88  |  Publisher:  Apress  |  Release:  2018

Practical Computer Vision Applications Using Deep Learning with CNNs

Practical Computer Vision Applications Using Deep Learning with CNNs

by Ahmed Fawzy Gad

Deploy deep learning applications into production across multiple platforms. You will work on computer vision applications that use the convolutional neural network (CNN) deep learning model and Python. This book starts by explaining the traditional machine-learning pipeline, where you will analyze an image dataset. Along the way you will cover artificial neural networks (ANNs), building one from scratch in...

Price:  $44.99  |  Publisher:  Apress  |  Release:  2018

Feature Engineering for Machine Learning

Feature Engineering for Machine Learning

by Alice Zheng, Amanda Casari

Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you'll learn techniques for extracting and transforming features - the numeric representations of raw data - into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these...

Price:  $43.95  |  Publisher:  O'Reilly Media  |  Release:  2018

Think Complexity, 2nd Edition

Think Complexity, 2nd Edition

by Allen Downey

Complexity science uses computation to explore the physical and social sciences. In Think Complexity, you'll use graphs, cellular automata, and agent-based models to study topics in physics, biology, and economics.Whether you're an intermediate-level Python programmer or a student of computational modeling, you'll delve into examples of complex systems through a series of worked examples, exercises, case st...

Price:  $39.31  |  Publisher:  O'Reilly Media  |  Release:  2018

Exploring Data with Python

Exploring Data with Python

by Naomi Ceder

Python has become a required skill for data science, and it's easy to see why. It's powerful, easy to learn, and includes the libraries like Pandas, Numpy, and Scikit that help you slice, scrub, munge, and wrangle your data. Even with a great language and fantastic tools though, there's plenty to learn!Exploring Data with Python is a collection of chapters from three Manning books, hand-picked by Naomi Cede...

Publisher:  Manning  |  Release:  2018

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