Among the languages with the fastest growth nowadays is Python. Its varied applications—from web and gaming programming to data analysis and mining to scientific computing, artificial intelligence, and more—as well as its straightforward syntax make it rather simple to learn!...are boosting its renown. Thus, we are at last addressing the need for a Python Murach book. Whether you're a novice programmer or have years of experience, we think it offers the quickest, easiest, and most professional approach to learn Python available.
The journeyman Pythonista can achieve full mastery with the help of The Hitchhiker's Guide to Python. More than any other language, Python was designed with simplicity and parsimony as its primary goals. Python, which is now 25 years old, is now used by many corporate customers as their primary or secondary language, following SQL. Popularity brings diversity and perhaps diluting effects. Written cooperatively by more than a hundred Python community members, this guide outlines the best practices that package and application developers now employ. The Hitchhiker's Guide, in contrast to other publications written for this demographic, focuses more on design philosophy and less on reusable code, pointing the reader toward outstanding pre-existing sources.
This practical book offers a thorough, in-depth introduction to the fundamentals of Python. This revised fifth edition, which is based on author Mark Lutz's well-liked training course, will assist you in writing effective, high-caliber Python code more rapidly. Whether you're an experienced developer with experience in other languages or new to programming, this is a great way to get started. This self-paced, easy-to-follow lesson introduces you to Python 2.7 and 3.3, the most recent versions in the 3.X and 2.X lines, as well as all other releases that are widely used today. It includes quizzes, exercises, and instructive illustrations. Additionally, certain sophisticated language features that are increasingly prevalent in Python programs will be taught to you.
This handy pocket guide, updated for Python 2.7 and 3.4, is an ideal quick reference for on-the-job use. Concise, essential knowledge on Python types and statements, unique method names, built-in functions and exceptions, frequently used standard library modules, and other well-known Python tools may be found here. Using the helpful index, you can quickly identify what you need. Written by Mark Lutz, who is widely acknowledged as the top Python trainer in the world,The Python Pocket Reference is a perfect addition to Mark O'Reilly's well-known Python tutorials, Learning Python and Programming Python.
Python has been embraced by the financial sector at a breakneck pace recently; major investment banks and hedge funds are utilizing it to construct key trading and risk management systems. adapted for Python 3. This practical guide, now in its second edition, walks developers and quantitative analysts through the Python libraries and tools needed to create interactive financial analytics and financial apps. Author Yves Hilpisch demonstrates how to create a comprehensive framework for derivatives and risk analytics based on Monte Carlo simulation through the use of real-world examples in the book. The framework is based on a sizable, genuine case study. Interactive I Python notebooks are used extensively in the text.
Unsupervised learning, which may hold the key to general artificial intelligence, is seen by many industry professionals as the next frontier in artificial intelligence. Most of the data in the world is unlabeled, thus traditional supervised learning is not applicable. Conversely, meaningful patterns that may be nearly hard for humans to find in unlabeled datasets can be found through the use of unsupervised learning.
This book provides an extremely approachable introduction to natural language processing, the area of study that underpins many language technologies, such as automatic translation and summarization, email and text prediction, and automatic text filtering. It will teach you how to develop Python programs that handle massive amounts of unstructured text. You will be able to use a wide variety of linguistic data structures to access extensively annotated datasets, and you will be familiar with the primary algorithms for examining the structure and content of written communication.
Artificial intelligence (AI) problems can be solved in countless ways thanks to natural language processing (NLP), which enables services like Google Translate and Amazon Alexa. This practical article demonstrates how to use pytorch, a Python-based deep learning library, to use NLP and deep learning techniques if you're a developer or data scientist who's new to these fields. The authors, Delip Rao and Brian McMahon, provide you a strong foundation in natural language processing (NLP) and deep learning methods. They also show you how to utilize Pytorch to create applications that leverage rich text representations that are tailored to your own issues. There are multiple code examples and graphics in every chapter.