“…This module also provides a workaround using some of the amazing capabilities of Python libraries such as NLTK, scikit-learn, pandas, and NumPy. The second Python 3 Text Processing with NLTK 3 Cookbook module teaches you the essential techniques of text and language processing with simple, straightforward examples. …”

“…The book will then explain to you the graphic modeler, how to create QGIS plugins with PyQGIS, and how to integrate Python analysis scripts with QGIS. …”

“…What you will learn Understand security breaches, weaknesses, and protection techniques Attack and defend wired as well as wireless networks Discover how to attack and defend LAN-, IP-, and TCP/UDP-based vulnerabilities Focus on encryption, authorization, and authentication principles Gain insights into implementing security protocols the right way Use tools and scripts to perform attacks on network devices Wield Python, PyShark, and other scripting tools for packet analysis Identify attacks on web servers to secure web and email services Who this book is for This book is for red team and blue team pentesters, security professionals, or bug hunters. …”

“…Course Requirements The author’s Deep Learning with TensorFlow, Keras, and PyTorch LiveLessons , or familiarity with the topics covered in Chapters 5 through 9 of his book Deep Learning Illustrated , are a prerequisite. …”

“…You will learn to make efficient and powerful data-driven financial decisions using popular libraries such as TensorFlow, Keras, Numpy, SciPy, and scikit-learn. You will also learn how to build financial applications by mastering concepts such as stocks, options, interest rates and their derivatives, and risk analytics using computational methods. …”

“…To better grasp the concepts explained in this book, you must have a thorough understanding of advanced mathematical concepts, such as Markov chains, Euler's formula, and Runge-Kutta methods as the book only explains how these techniques and concepts can be implemented in Py..…”

“…What you will learn Automate geospatial analysis workflows using Python Code the simplest possible GIS in just 60 lines of Python Create thematic maps with Python tools such as PyShp, OGR, and the Python Imaging Library Understand the different formats that geospatial data comes in Produce elevation contours using Python tools Create flood inundation models Apply geospatial analysis to real-time data tracking and storm chasing Who this book is for This book is for Python developers, researchers, or analysts who want to perform geospatial modeling and GIS analysis with Python. …”

“…You will learn about NumPy and pandas libraries for matrix calculations and data manipulation, study how to use Matplotlib to create highly customizable visualizations, and apply the boosting algorithm XGBoost to make predictions. …”

“…He is a frequent speaker at PyCon. Although Python is his current language of choice, he also has significant experience with systems programming in C, C++, and assembly language. …”

“…An individual who wants to learn PySpark along with its implementation in realistic projects, machine learning or deep learning lovers, and anyone interested in artificial intelligence will be highly benefitted. …”

“…Finally, we will wrap up the two most popular libraries for data science--NumPy and Pandas. The last part delves into essential math for data science. …”

“…Along the way, you'll work with core algorithms, including deep Q-networks and policy gradients, along with industry-standard tools like PyTorch and OpenAI Gym. What's Inside Building and training DRL networks The most popular DRL algorithms for learning and problem solving Evolutionary algorithms for curiosity and multi-agent learning All examples available as Jupyter Notebooks About the Reader For readers with intermediate skills in Python and deep learning. …”

“…Along the way, you'll work with core algorithms, including deep Q-networks and policy gradients, along with industry-standard tools like PyTorch and OpenAI Gym. What's Inside Building and training DRL networks The most popular DRL algorithms for learning and problem solving Evolutionary algorithms for curiosity and multi-agent learning All examples available as Jupyter Notebooks About the Reader For readers with intermediate skills in Python and deep learning. …”

“…Massimo Di Pierro - fellow of the PSF, data scientist and the inventor of web2py. Alex Martelli - fellow of the PSF and co-author of Python in a Nutshell. …”

“…Tutorials by AI's most experienced practitioners, including Gunnar Carlsson (Stanford University) on using topological data analysis to understand, build, and improve neural networks; Bruno Goncalves (JPMorgan Chase) on using recurrent neural networks for time series analysis; and Mo Patel (Independent) on how to build machine learning models in PyTorch. Sessions focused on machine learning, including Alina Matyukhina's (Canadian Institute for Cybersecurity) reveal of the methods dishonest actors use ML to mimic the coding style of software developers in open source projects; Chakri Cherukuri's (Bloomberg LP) discussion of how to apply machine learning and deep learning techniques in quantitative finance; and Cibele Montez Halasz's (Twitter) description of time..…”

“…You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seaborn, Keras, Dash, and so on. …”

“…It assumes that readers have intermediate Python programming knowledge, basic knowledge of Python libraries such as NumPy and Pandas to manipulate and parse data, Matplotlib, and Seaborn for visualizing data, along with a base in genomics and genomic analysis concepts…”

“…Get modern insight into the core of Python data, including the latest versions of Jupyter notebooks, NumPy, pandas and scikit-learn. Look beyond the fundamentals with beautiful data visualizations with Seaborn and ggplot, web development with Bottle, and even the new frontiers of deep learning with Theano and TensorFlow. …”

“…He has run data science workshops at Strata and PyData (among others), built a Data Visualization course with Udacity, and served on the UC Berkeley Extension Data Science Advisory Board. …”

“…Includes Joel Grus (Allen Institute for Artificial Intelligence) live-coding a deep learning library from scratch using NumPy and Eve Porcello (Moon Highway) live-coding a walk-through of GraphQL, and more. …”