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Packt's Book Sales
Hello people,

Christmas is coming, the wallets are getting fat,
Packt has got gifts for you at $5 flat!
We've got a variety of gifts you could choose,
For a bundle of 5, $25 is all you gotta use!

Yes! This Christmas, Packt is the Santa Claus and it has eBooks, Videos and Courses in its bag (library) available for $5 each. That’s not it, you can choose a variety of bundles encompassing diverse technologies which gives you a comprehensive outlook on a particular topic for just $25.

Grab all the gifts while you can and gear up with the latest technologies. Take up the new year’s resolution to enhance your skills and master them with a dash of a price.

Merry Christmas!
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Python Deep Learning - Second Edition


This book 'Python Deep Learning - Second Edition' by Ivan Vasilev et al. (January 2019) will help in learning advanced state-of-the-art deep learning techniques and their applications using popular Python libraries.

With the surge in artificial intelligence in applications catering to both business and consumer needs, deep learning is more important than ever for meeting current and future market demands. With this book, you’ll explore deep learning, and learn how to put machine learning to use in your projects.

What You Will Learn:

1. Grasp the mathematical theory behind neural networks and deep learning processes
2. Investigate and resolve computer vision challenges using convolutional networks and capsule networks
3. Solve generative tasks using variational autoencoders and Generative Adversarial Networks
4. Implement complex NLP tasks using recurrent networks (LSTM and GRU) and attention models
5. Explore reinforcement learning and understand how agents behave in a complex environment
6. Get up to date with applications of deep learning in autonomous vehicles

Author Bio:

Ivan Vasilev
Ivan Vasilev started working on the first open source Java Deep Learning library with GPU support in 2013. The library was acquired by a German company, where he continued its development. He has also worked as a machine learning engineer and researcher in the area of medical image classification and segmentation with deep neural networks. Since 2017 he has focused on financial machine learning. He is working on a Python open source algorithmic trading library, which provides the infrastructure to experiment with different ML algorithms. The author holds an MSc degree in Artificial Intelligence from The University of Sofia, St. Kliment Ohridski.

Daniel Slater
Daniel Slater started programming at age 11, developing mods for the id Software game Quake. His obsession led him to become a developer working in the gaming industry on the hit computer game series Championship Manager. He then moved into finance, working on risk- and high-performance messaging systems. He now is a staff engineer working on big data at Skimlinks to understand online user behavior. He spends his spare time training AI to beat computer games. He talks at tech conferences about deep learning and reinforcement learning; and the name of his blog is Daniel Slater's blog. His work in this field has been cited by Google.

Gianmario Spacagna
Gianmario Spacagna is a senior data scientist at Pirelli, processing sensors and telemetry data for internet of things (IoT) and connected-vehicle applications. He works closely with tire mechanics, engineers, and business units to analyze and formulate hybrid, physics-driven, and data-driven automotive models. His main expertise is in building ML systems and end-to-end solutions for data products. He holds a master's degree in telematics from the Polytechnic of Turin, as well as one in software engineering of distributed systems from KTH, Stockholm. Prior to Pirelli, he worked in retail and business banking (Barclays), cyber security (Cisco), predictive marketing (AgilOne), and did some occasional freelancing.

Peter Roelants
Peter Roelants holds a master's in computer science with a specialization in AI from KU Leuven. He works on applying deep learning to a variety of problems, such as spectral imaging, speech recognition, text understanding, and document information extraction. He currently works at Onfido as a team leader for the data extraction research team, focusing on data extraction from official documents.

Valentino Zocca
Valentino Zocca has a PhD degree and graduated with a Laurea in mathematics from the University of Maryland, USA, and University of Rome, respectively, and spent a semester at the University of Warwick. He started working on high-tech projects of an advanced stereo 3D Earth visualization software with head tracking at Autometric, a company later bought by Boeing. There he developed many mathematical algorithms and predictive models, and using Hadoop he automated several satellite-imagery visualization programs. He has worked as an independent consultant at the U.S. Census Bureau, in the USA and in Italy. Currently, Valentino lives in New York and works as an independent consultant to a large financial company.
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OpenCV 4 Computer Vision with Python Recipes [Video]


This video 'OpenCV 4 Computer Vision with Python Recipes' by Muhammad Hamza Javed (January 2019) will expand your OpenCV knowledge & use of machine learning to your advantage with this practical hands-on course!

What You Will Learn:

1. How to build real-world Computer Vision applications.
2. Deploy Face and Eyes Detection with HAAR Cascade Classifiers.
3. Recognize Age, Gender and Emotions and Roadside Landmarks.
4. Develop Fast QR Code Detection and Decoding application.
5. Create DNN based Image Classifier.
6. Train an Object Detection Model and Detect Persons and Vehicles.


Muhammad Hamza Javed

Muhammad Hamza Javed is a self-taught Machine Learning engineer, an entrepreneur and an author having over five years of industrial experience. He and his team has been working on several Computer Vision and Machine Learning international projects. He started working when he was 17 and kept learning new technologies and skills since then. His areas of expertise include Computer Vision, Machine Learning and Deep Learning. He learned skills own his own without a direct mentor - so he knows how troublesome it is for everyone to find to-the-point content that really improves one’s skill-set. He’s designed this course considering the challenges he faced when he learned and in the projects, so you don’t have to spend too much time on finding what’s best for you.

This book Hands-On Penetration Testing with Python by Furqan Khan (January,2019) will help in implementing defensive techniques in your ecosystem successfully with Python.

By the end of this book, you’ll have acquired adequate skills to leverage Python as a helpful tool to pentest and secure infrastructure, while also creating your own custom exploits.

What You Will Learn

1) Get to grips with Custom vulnerability scanner development
2) Familiarize yourself with web application scanning automation and exploit development
3) Walk through day-to-day cybersecurity scenarios that can be automated with Python
4) Discover enterprise-or organization-specific use cases and threat-hunting automation
5) Understand reverse engineering, fuzzing, buffer overflows , key-logger development, and exploit development for buffer overflows.
6) Understand web scraping in Python and use it for processing web responses
7) Explore Security Operations Centre (SOC) use cases
8) Get to understand Data Science, Python, and cybersecurity all under one hood

Author Bio

Furqan Khan
Furqan Khan is a security researcher who loves to innovate in Python, pentesting, ML, AI, and big data ecosystems.

With a gold medal at both M.Tech and B.Tech, he started off as a research scientist at NITK, where he developed a web app scanner for the Ministry of IT (India). He then worked as a security researcher with Paladion Networks and Wipro Dubai exploring pentesting/exploitation space where he developed tools such as vulnerability scanner and a threat intelligence platform.

Currently, he is working with Du-Telecom Dubai as a pentesting manager. He has published and co-authored white papers and journals with Springer and Elsevier, and has also presented his research and development work at international conferences, including CoCon.

This book Mastering Python Scripting for System Administrators by Ganesh Sanjiv Naik (January,2019) will help to Leverage the features and libraries of Python to administrate your environment efficiently.

By the end of this book, you will be able to use the latest features of Python and be able to build powerful tools that will solve challenging, real-world tasks

What You Will Learn

1. Understand how to install Python and debug Python scripts
2. Understand and write scripts for automating testing and routine administrative activities
3. Understand how to write scripts for text processing, encryption, decryption, and archiving
4. Handle files, such as pdf, excel, csv, and txt files, and generate reports
5. Write scripts for remote network administration, including handling emails
6. Build interactive tools using a graphical user interface
7. Handle Apache log files, SOAP and REST APIs communication
8. Automate database administration and perform statistical analysis.

Author Bio

Ganesh Sanjiv Naik
Ganesh Sanjiv Naik is an author, consultant, and corporate trainer in the fields of AI, data science, machine learning, the Internet of Things (IoT), and embedded Linux in relation to product development. He has more than 20 years of professional experience in information technology. Ganesh has a passion and a deep desire for teaching. He has trained 2,000+ engineers in Linux and Android product development.

He has worked as a corporate trainer for the ISRO, Intel, GE, Samsung, Motorola, Penang Skill Development Center (Malaysia), and various companies in Singapore, Malaysia, and India. He has started a company called Levana Technologies, which works in AI, machine learning, and data science, providing training, projects, and consultancy activities.
Oh yes, this is a very cool book! I know, I read it, though not completely.
This is not read, thanks for sharing this!

This book Python Machine Learning Blueprints - Second Edition will help you discover a project-based approach to mastering machine learning concepts by applying them to everyday problems using libraries such as scikit-learn, TensorFlow, and Keras.

By the end of this book, you’ll be able to analyze data seamlessly and make a powerful impact through your projects.

What You Will Learn
- Understand the Python data science stack and commonly used algorithms
- Build a model to forecast the performance of an Initial Public Offering (IPO) over an initial discrete trading window
- Understand NLP concepts by creating a custom news feed
- Create applications that will recommend GitHub repositories based on ones you’ve starred, watched, or forked
- Gain the skills to build a chatbot from scratch using PySpark
- Develop a market-prediction app using stock data
- Delve into advanced concepts such as computer vision, neural networks, and deep learning

Alexander Combs
Alexander Combs is an experienced data scientist, strategist, and developer with a background in financial data extraction, natural language processing and generation, and quantitative and statistical modeling. He currently lives and works in New York City.

Michael Roman
Michael Roman is a data scientist at The Atlantic, where he designs, tests, analyzes, and productionizes machine learning models to address a range of business topics. Prior to this he was an associate instructor at a full-time data science immersive program in New York City. His interests include computer vision, propensity modeling, natural language processing, and entrepreneurship.
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This book Intelligent Projects Using Python will help you implement machine learning and deep learning methodologies to build smart, cognitive AI projects using Python.


By the end of this book, you will be skilled to build your own smart models for tackling any kind of AI problems without any hassle.

What You Will Learn
- Build an intelligent machine translation system using seq-2-seq neural translation machines
- Create AI applications using GAN and deploy smart mobile apps using TensorFlow
- Translate videos into text using CNN and RNN
- Implement smart AI Chatbots, and integrate and extend them in several domains
- Create smart reinforcement, learning-based applications using Q-Learning
- Break and generate CAPTCHA using Deep Learning and Adversarial Learning

Santanu Pattanayak

Santanu Pattanayak works as a Staff Machine Learning Specialist at Qualcomm Corp R&D and is an author of the deep learning book Pro Deep Learning with TensorFlow - A Mathematical Approach to Advanced Artificial Intelligence in Python. He has around 12 years of work experience and has worked at GE, Capgemini, and IBM before joining Qualcomm. He graduated with a degree in electrical engineering from Jadavpur University, Kolkata and is an avid math enthusiast. Santanu is currently pursuing a master's degree in data science from Indian Institute of Technology (IIT), Hyderabad. He also participates in Kaggle competitions in his spare time where he ranks in top 500. Currently, he resides in Bangalore with his wife.

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This book, Learning Python for Forensics - Second Edition, will help in designing, developing, and deploying innovative forensic solutions using Python.

You will build a collection of Python scripts capable of investigating an array of forensic artifacts and master the skills of extracting metadata and parsing complex data structures into actionable reports. Most importantly, you will have developed a foundation upon which to build as you continue to learn Python and enhance your efficacy as an investigator.

What You Will Learn
  • Learn how to develop Python scripts to solve complex forensic problems
  • Build scripts using an iterative design
  • Design code to accommodate present and future hurdles
  • Leverage built-in and community-sourced libraries
  • Understand the best practices in forensic programming
  • Learn how to transform raw data into customized reports and visualizations
  • Create forensic frameworks to automate the analysis of multiple forensic artifacts
  • Conduct effective and efficient investigations through programmatic processing


Preston Miller
Preston Miller is a consultant at an internationally recognized risk management firm. Preston holds an undergraduate degree from Vassar College and a master's degree in digital forensics from Marshall University. While at Marshall, Preston unanimously received the prestigious J. Edgar Hoover Foundation's scientific scholarship. Preston is a published author, recently of Python Digital Forensics Cookbook, which won the Forensic 4:cast Digital Forensics Book of the Year award in 2018. Preston is a member of the GIAC advisory board and holds multiple industry-recognized certifications in his field.

Chapin Bryce
Chapin Bryce is a senior consultant at a global firm that is a leader in digital forensics and incident response investigations. After graduating from Champlain College, with a bachelor's degree in computer and digital forensics, Chapin dove into the field of digital forensics and incident response joining the GIAC advisory board and earning four GIAC certifications: GCIH, GCFE, GCFA, and GNFA. As a member of multiple ongoing research and development projects, he has authored several books and articles in professional and academic publications, including Python Digital Forensics Cookbook (2018 Digital Forensics Book of the Year, Forensic 4Cast), Learning Python for Forensics First Edition, and Digital Forensic Magazine.

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