The Hackers Guide To Scaling Python

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The Hacker's Guide to Scaling Python

Author : Julien Danjou
Publisher : Julien Danjou
ISBN 13 : 1387379321
Total Pages : 300 pages
Book Rating : 4.3/5 (873 download)

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Book Synopsis The Hacker's Guide to Scaling Python by : Julien Danjou

Download or read book The Hacker's Guide to Scaling Python written by Julien Danjou and published by Julien Danjou. This book was released on 2017 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: Python is a wonderful programming language that allows writing applications quickly. But how do you make those applications scale for thousands of users and requests? It takes years of practice, research, trial and errors to build experience and knowledge along the way. Simple questions such as "How do I make my code faster?" or "How do I make sure there is no bottleneck?" cost hours to find good answers. Without enough background on the topic, you'll never be sure that any answer you'll come up with will be correct. The Hacker's Guide to Scaling Python will help you solve that by providing guidelines, tips and best practice. Adding a few interviews of experts on the subject, you will learn how you can distribute your Python application so it is able to process thousands of requests.

Serious Python

Author : Julien Danjou
Publisher : No Starch Press
ISBN 13 : 1593278799
Total Pages : 242 pages
Book Rating : 4.5/5 (932 download)

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Book Synopsis Serious Python by : Julien Danjou

Download or read book Serious Python written by Julien Danjou and published by No Starch Press. This book was released on 2018-12-31 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: An indispensable collection of practical tips and real-world advice for tackling common Python problems and taking your code to the next level. Features interviews with high-profile Python developers who share their tips, tricks, best practices, and real-world advice gleaned from years of experience. Sharpen your Python skills as you dive deep into the Python programming language with Serious Python. You'll cover a range of advanced topics like multithreading and memorization, get advice from experts on things like designing APIs and dealing with databases, and learn Python internals to help you gain a deeper understanding of the language itself. Written for developers and experienced programmers, Serious Python brings together over 15 years of Python experience to teach you how to avoid common mistakes, write code more efficiently, and build better programs in less time. As you make your way through the book's extensive tutorials, you'll learn how to start a project and tackle topics like versioning, layouts, coding style, and automated checks. You'll learn how to package your software for distribution, optimize performance, use the right data structures, define functions efficiently, pick the right libraries, build future-proof programs, and optimize your programs down to the bytecode. You'll also learn how to: - Make and use effective decorators and methods, including abstract, static, and class methods - Employ Python for functional programming using generators, pure functions, and functional functions - Extend flake8 to work with the abstract syntax tree (AST) to introduce more sophisticated automatic checks into your programs - Apply dynamic performance analysis to identify bottlenecks in your code - Work with relational databases and effectively manage and stream data with PostgreSQL If you've been looking for a way to take your Python skills from good to great, Serious Python will help you get there. Learn from the experts and get seriously good at Python with Serious Python!

Machine Learning for Hackers

Author : Drew Conway
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1449330533
Total Pages : 323 pages
Book Rating : 4.4/5 (493 download)

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Book Synopsis Machine Learning for Hackers by : Drew Conway

Download or read book Machine Learning for Hackers written by Drew Conway and published by "O'Reilly Media, Inc.". This book was released on 2012-02-13 with total page 323 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation. Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research. Develop a naïve Bayesian classifier to determine if an email is spam, based only on its text Use linear regression to predict the number of page views for the top 1,000 websites Learn optimization techniques by attempting to break a simple letter cipher Compare and contrast U.S. Senators statistically, based on their voting records Build a “whom to follow” recommendation system from Twitter data

The Hacker's Guide to Python

Author : Julien Danjou
Publisher : Julien Danjou
ISBN 13 :
Total Pages : 290 pages
Book Rating : 4./5 ( download)

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Book Synopsis The Hacker's Guide to Python by : Julien Danjou

Download or read book The Hacker's Guide to Python written by Julien Danjou and published by Julien Danjou. This book was released on 2016-05-04 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: Python is a wonderful programming language that is being used more and more in many different industries. It is fast, flexible, and it comes with batteries included. Most of the books you read about Python will teach you the language basics – but once you've learnt them, you’re on your own in designing your application and discovering best practice. In this book, we'll see how you can leverage Python to efficiently tackle your problems and build great Python applications.

Python for Data Analysis

Author : Wes McKinney
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491957611
Total Pages : 553 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Python for Data Analysis by : Wes McKinney

Download or read book Python for Data Analysis written by Wes McKinney and published by "O'Reilly Media, Inc.". This book was released on 2017-09-25 with total page 553 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples

Python for Scientists

Author : John M. Stewart
Publisher : Cambridge University Press
ISBN 13 : 1316641236
Total Pages : 272 pages
Book Rating : 4.3/5 (166 download)

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Book Synopsis Python for Scientists by : John M. Stewart

Download or read book Python for Scientists written by John M. Stewart and published by Cambridge University Press. This book was released on 2017-07-20 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: Scientific Python is taught from scratch in this book via copious, downloadable, useful and adaptable code snippets. Everything the working scientist needs to know is covered, quickly providing researchers and research students with the skills to start using Python effectively.

The Mac Hacker's Handbook

Author : Charlie Miller
Publisher : John Wiley & Sons
ISBN 13 : 1118080335
Total Pages : 386 pages
Book Rating : 4.1/5 (18 download)

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Book Synopsis The Mac Hacker's Handbook by : Charlie Miller

Download or read book The Mac Hacker's Handbook written by Charlie Miller and published by John Wiley & Sons. This book was released on 2011-03-21 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: As more and more vulnerabilities are found in the Mac OS X (Leopard) operating system, security researchers are realizing the importance of developing proof-of-concept exploits for those vulnerabilities. This unique tome is the first book to uncover the flaws in the Mac OS X operating system—and how to deal with them. Written by two white hat hackers, this book is aimed at making vital information known so that you can find ways to secure your Mac OS X systems, and examines the sorts of attacks that are prevented by Leopard’s security defenses, what attacks aren’t, and how to best handle those weaknesses.

Machine Learning

Author : Jason Bell
Publisher : John Wiley & Sons
ISBN 13 : 1119642191
Total Pages : 497 pages
Book Rating : 4.1/5 (196 download)

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Book Synopsis Machine Learning by : Jason Bell

Download or read book Machine Learning written by Jason Bell and published by John Wiley & Sons. This book was released on 2020-02-17 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dig deep into the data with a hands-on guide to machine learning with updated examples and more! Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML variant, explaining how it works and how it is used within certain industries, allowing readers to incorporate the presented techniques into their own work as they follow along. A core tenant of machine learning is a strong focus on data preparation, and a full exploration of the various types of learning algorithms illustrates how the proper tools can help any developer extract information and insights from existing data. The book includes a full complement of Instructor's Materials to facilitate use in the classroom, making this resource useful for students and as a professional reference. At its core, machine learning is a mathematical, algorithm-based technology that forms the basis of historical data mining and modern big data science. Scientific analysis of big data requires a working knowledge of machine learning, which forms predictions based on known properties learned from training data. Machine Learning is an accessible, comprehensive guide for the non-mathematician, providing clear guidance that allows readers to: Learn the languages of machine learning including Hadoop, Mahout, and Weka Understand decision trees, Bayesian networks, and artificial neural networks Implement Association Rule, Real Time, and Batch learning Develop a strategic plan for safe, effective, and efficient machine learning By learning to construct a system that can learn from data, readers can increase their utility across industries. Machine learning sits at the core of deep dive data analysis and visualization, which is increasingly in demand as companies discover the goldmine hiding in their existing data. For the tech professional involved in data science, Machine Learning: Hands-On for Developers and Technical Professionals provides the skills and techniques required to dig deeper.

Mastering Machine Learning for Penetration Testing

Author : Chiheb Chebbi
Publisher : Packt Publishing Ltd
ISBN 13 : 178899311X
Total Pages : 264 pages
Book Rating : 4.7/5 (889 download)

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Book Synopsis Mastering Machine Learning for Penetration Testing by : Chiheb Chebbi

Download or read book Mastering Machine Learning for Penetration Testing written by Chiheb Chebbi and published by Packt Publishing Ltd. This book was released on 2018-06-27 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: Become a master at penetration testing using machine learning with Python Key Features Identify ambiguities and breach intelligent security systems Perform unique cyber attacks to breach robust systems Learn to leverage machine learning algorithms Book Description Cyber security is crucial for both businesses and individuals. As systems are getting smarter, we now see machine learning interrupting computer security. With the adoption of machine learning in upcoming security products, it’s important for pentesters and security researchers to understand how these systems work, and to breach them for testing purposes. This book begins with the basics of machine learning and the algorithms used to build robust systems. Once you’ve gained a fair understanding of how security products leverage machine learning, you'll dive into the core concepts of breaching such systems. Through practical use cases, you’ll see how to find loopholes and surpass a self-learning security system. As you make your way through the chapters, you’ll focus on topics such as network intrusion detection and AV and IDS evasion. We’ll also cover the best practices when identifying ambiguities, and extensive techniques to breach an intelligent system. By the end of this book, you will be well-versed with identifying loopholes in a self-learning security system and will be able to efficiently breach a machine learning system. What you will learn Take an in-depth look at machine learning Get to know natural language processing (NLP) Understand malware feature engineering Build generative adversarial networks using Python libraries Work on threat hunting with machine learning and the ELK stack Explore the best practices for machine learning Who this book is for This book is for pen testers and security professionals who are interested in learning techniques to break an intelligent security system. Basic knowledge of Python is needed, but no prior knowledge of machine learning is necessary.

Architecture Patterns with Python

Author : Harry Percival
Publisher : O'Reilly Media
ISBN 13 : 1492052175
Total Pages : 304 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Architecture Patterns with Python by : Harry Percival

Download or read book Architecture Patterns with Python written by Harry Percival and published by O'Reilly Media. This book was released on 2020-03-05 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: As Python continues to grow in popularity, projects are becoming larger and more complex. Many Python developers are now taking an interest in high-level software design patterns such as hexagonal/clean architecture, event-driven architecture, and the strategic patterns prescribed by domain-driven design (DDD). But translating those patterns into Python isn’t always straightforward. With this hands-on guide, Harry Percival and Bob Gregory from MADE.com introduce proven architectural design patterns to help Python developers manage application complexity—and get the most value out of their test suites. Each pattern is illustrated with concrete examples in beautiful, idiomatic Python, avoiding some of the verbosity of Java and C# syntax. Patterns include: Dependency inversion and its links to ports and adapters (hexagonal/clean architecture) Domain-driven design’s distinction between entities, value objects, and aggregates Repository and Unit of Work patterns for persistent storage Events, commands, and the message bus Command-query responsibility segregation (CQRS) Event-driven architecture and reactive microservices

Hacking the Planet with Notcurses

Author : Nick Black
Publisher :
ISBN 13 :
Total Pages : 160 pages
Book Rating : 4.6/5 (2 download)

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Book Synopsis Hacking the Planet with Notcurses by : Nick Black

Download or read book Hacking the Planet with Notcurses written by Nick Black and published by . This book was released on 2020-03-31 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nick Black, prolific Free Software developer and designer of Notcurses, introduces character graphics and Text User Interface design. The examples use the modern Notcurses library, but many of the lessons are applicable to TUI programming using NCURSES or Newt. Topics include the history of and current practice of terminals, Unicode (through 2020's Unicode 13.0), handling input from keyboards and mice, effective use of RGB DirectColor, palette-indexed pseudocolor, and alpha blending, loading images and video, construction of reusable TUI widgets, and more. These concepts are developed using rich examples. Nick graduated with a handful of degrees from the Georgia Institute of Technology, and has hacked away in the code mines of NVIDIA, Google, and several successful startups. He is a Senior Member of the ACM, and a consulting scientist at Dirty South Supercomputing and Waffles.

Machine Learning with Python for Everyone

Author : Mark Fenner
Publisher : Addison-Wesley Professional
ISBN 13 : 0134845641
Total Pages : 1376 pages
Book Rating : 4.1/5 (348 download)

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Book Synopsis Machine Learning with Python for Everyone by : Mark Fenner

Download or read book Machine Learning with Python for Everyone written by Mark Fenner and published by Addison-Wesley Professional. This book was released on 2019-07-30 with total page 1376 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Complete Beginner’s Guide to Understanding and Building Machine Learning Systems with Python Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you’re an absolute beginner. If you can write some Python code, this book is for you, no matter how little college-level math you know. Principal instructor Mark E. Fenner relies on plain-English stories, pictures, and Python examples to communicate the ideas of machine learning. Mark begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you’ll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field’s most sophisticated and exciting techniques. Whether you’re a student, analyst, scientist, or hobbyist, this guide’s insights will be applicable to every learning system you ever build or use. Understand machine learning algorithms, models, and core machine learning concepts Classify examples with classifiers, and quantify examples with regressors Realistically assess performance of machine learning systems Use feature engineering to smooth rough data into useful forms Chain multiple components into one system and tune its performance Apply machine learning techniques to images and text Connect the core concepts to neural networks and graphical models Leverage the Python scikit-learn library and other powerful tools Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Clean Code in Python

Author : Mariano Anaya
Publisher : Packt Publishing Ltd
ISBN 13 : 1800562098
Total Pages : 423 pages
Book Rating : 4.8/5 (5 download)

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Book Synopsis Clean Code in Python by : Mariano Anaya

Download or read book Clean Code in Python written by Mariano Anaya and published by Packt Publishing Ltd. This book was released on 2021-01-06 with total page 423 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tackle inefficiencies and errors the Pythonic way Key Features Enhance your coding skills using the new features introduced in Python 3.9 Implement the refactoring techniques and SOLID principles in Python Apply microservices to your legacy systems by implementing practical techniques Book Description Experienced professionals in every field face several instances of disorganization, poor readability, and testability due to unstructured code. With updated code and revised content aligned to the new features of Python 3.9, this second edition of Clean Code in Python will provide you with all the tools you need to overcome these obstacles and manage your projects successfully. The book begins by describing the basic elements of writing clean code and how it plays a key role in Python programming. You will learn about writing efficient and readable code using the Python standard library and best practices for software design. The book discusses object-oriented programming in Python and shows you how to use objects with descriptors and generators. It will also show you the design principles of software testing and how to resolve problems by implementing software design patterns in your code. In the concluding chapter, we break down a monolithic application into a microservices-based one starting from the code as the basis for a solid platform. By the end of this clean code book, you will be proficient in applying industry-approved coding practices to design clean, sustainable, and readable real-world Python code. What you will learn Set up a productive development environment by leveraging automatic tools Leverage the magic methods in Python to write better code, abstracting complexity away and encapsulating details Create advanced object-oriented designs using unique features of Python, such as descriptors Eliminate duplicated code by creating powerful abstractions using software engineering principles of object-oriented design Create Python-specific solutions using decorators and descriptors Refactor code effectively with the help of unit tests Build the foundations for solid architecture with a clean code base as its cornerstone Who this book is for This book is designed to benefit new as well as experienced programmers. It will appeal to team leads, software architects and senior software engineers who would like to write Pythonic code to save on costs and improve efficiency. The book assumes that you have a strong understanding of programming

Python Data Science Handbook

Author : Jake VanderPlas
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491912138
Total Pages : 609 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Python Data Science Handbook by : Jake VanderPlas

Download or read book Python Data Science Handbook written by Jake VanderPlas and published by "O'Reilly Media, Inc.". This book was released on 2016-11-21 with total page 609 pages. Available in PDF, EPUB and Kindle. Book excerpt: For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

Computer Programming and Cyber Security for Beginners

Author : Zach Codings
Publisher :
ISBN 13 : 9781801444378
Total Pages : 410 pages
Book Rating : 4.4/5 (443 download)

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Book Synopsis Computer Programming and Cyber Security for Beginners by : Zach Codings

Download or read book Computer Programming and Cyber Security for Beginners written by Zach Codings and published by . This book was released on 2021-02-05 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: 55% OFF for bookstores! Do you feel that informatics is indispensable in today's increasingly digital world? Your customers never stop to use this book!

Hack the Stack

Author : Stephen Watkins
Publisher : Elsevier
ISBN 13 : 0080507743
Total Pages : 481 pages
Book Rating : 4.0/5 (85 download)

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Book Synopsis Hack the Stack by : Stephen Watkins

Download or read book Hack the Stack written by Stephen Watkins and published by Elsevier. This book was released on 2006-11-06 with total page 481 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book looks at network security in a new and refreshing way. It guides readers step-by-step through the "stack" -- the seven layers of a network. Each chapter focuses on one layer of the stack along with the attacks, vulnerabilities, and exploits that can be found at that layer. The book even includes a chapter on the mythical eighth layer: The people layer. This book is designed to offer readers a deeper understanding of many common vulnerabilities and the ways in which attacker's exploit, manipulate, misuse, and abuse protocols and applications. The authors guide the readers through this process by using tools such as Ethereal (sniffer) and Snort (IDS). The sniffer is used to help readers understand how the protocols should work and what the various attacks are doing to break them. IDS is used to demonstrate the format of specific signatures and provide the reader with the skills needed to recognize and detect attacks when they occur. What makes this book unique is that it presents the material in a layer by layer approach which offers the readers a way to learn about exploits in a manner similar to which they most likely originally learned networking. This methodology makes this book a useful tool to not only security professionals but also for networking professionals, application programmers, and others. All of the primary protocols such as IP, ICMP, TCP are discussed but each from a security perspective. The authors convey the mindset of the attacker by examining how seemingly small flaws are often the catalyst of potential threats. The book considers the general kinds of things that may be monitored that would have alerted users of an attack.* Remember being a child and wanting to take something apart, like a phone, to see how it worked? This book is for you then as it details how specific hacker tools and techniques accomplish the things they do. * This book will not only give you knowledge of security tools but will provide you the ability to design more robust security solutions * Anyone can tell you what a tool does but this book shows you how the tool works

Machine Learning with Python Cookbook

Author : Chris Albon
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491989335
Total Pages : 285 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Machine Learning with Python Cookbook by : Chris Albon

Download or read book Machine Learning with Python Cookbook written by Chris Albon and published by "O'Reilly Media, Inc.". This book was released on 2018-03-09 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt: This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you’re comfortable with Python and its libraries, including pandas and scikit-learn, you’ll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics. Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications. You’ll find recipes for: Vectors, matrices, and arrays Handling numerical and categorical data, text, images, and dates and times Dimensionality reduction using feature extraction or feature selection Model evaluation and selection Linear and logical regression, trees and forests, and k-nearest neighbors Support vector machines (SVM), naïve Bayes, clustering, and neural networks Saving and loading trained models