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 PySpark SQL Recipes: With HiveQL, Dataframe and Graphframes
Carry out data analysis with PySpark SQL, graphframes, and graph data processing using a problem-solution approach. This book provides solutions to problems related to dataframes, data manipulation summarization, and exploratory analysis. You will improve your skills in graph data analysis using graphframes and see how to ... | | Foundations of PyGTK Development: GUI Creation with Python
One of the most important aspects of an application is the interface that is provided to interact with the user. With the unprecedented popularity of computers in society today, people have come to expect those user interfaces to be graphical, and the question of which graphical toolkit to use quickly arises for any developer. For many, the ... | | Quick Start Guide to Penetration Testing: With NMAP, OpenVAS and Metasploit
Get started with NMAP, OpenVAS, and Metasploit in this short book and understand how NMAP, OpenVAS, and Metasploit can be integrated with each other for greater flexibility and efficiency. You will begin by working with NMAP and ZENMAP and learning the basic scanning and enumeration process. After getting to know the differences ... |
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Propeller Programming: Using Assembler, Spin, and C
Learn to program the Propeller in Spin and C and how to map the Propeller Assembler (PASM) language from other high-level languages you might know.
The overall task you will pursue in the book is to implement a Delta Compression algorithm: first in Spin, then in PASM, then in C. Along the way, you'll review ... | | | | Regex Quick Syntax Reference: Understanding and Using Regular Expressions
This quick guide to regular expressions is a condensed code and syntax reference for an important programming technique. It demonstrates regex syntax in a well-organized format that can be used as a handy reference, showing you how to execute regexes in many languages, including JavaScript, Python, Java, and ... |
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| | Advanced Deep Learning with Keras: Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more
A comprehensive guide to advanced deep learning techniques, including autoencoders, GANs, VAEs, and deep reinforcement learning that drive today's most impressive AI results
Key Features
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Explore the most advanced deep learning techniques that drive modern AI results
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