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30 Python Best Practices, Tips, And Tricks With the holidays behind us, most of us have returned to our day jobs. For all those hard workers, here are 30 Python best practices, tips, and tricks. I’m sure they’ll help you procrastinate your actual work, and still learn something useful in the process. In case you missed it: Python 2 is officially not supported as of January 1, 2020. This guide has a bunch of examples that only work in Python 3. If you’re still on Python 2.7, upgrade now. Deep Learning Course ⇢ François Fleuret You can find here the materials for the EPFL course EE-559 “Deep Learning”. These documents are under heavy development, in particular due to pytorch updates. Please avoid to distribute the pdf files, and share the URL of this page instead. Info sheet: dlc-info-sheet.pdf

Colin Stebbins Gordon Below is a loosely-categorized collection of links to CS textbooks in a variety of areas that are freely available online, usually because they are one of the following: An open textbook (such as PLAI, SF, or the HoTT book) An older book that is out of print, for which the copyright has returned to the original author(s) (such as TTFP) An author’s own preprint or draft of a textbook. This includes cases where the author has made special arrangements with a publisher to host an electronic copy of a published text on their homepage while it remains in print. Most of these I’ve only used for brief personal reference, and have not read in depth. The exceptions, those books I’ve spent considerable time with and highly recommend, are marked with asterisks.

A Visual Intro to NumPy and Data Representation – Jay Alammar – Visualizing machine learning one concept at a time The NumPy package is the workhorse of data analysis, machine learning, and scientific computing in the python ecosystem. It vastly simplifies manipulating and crunching vectors and matrices. Some of python’s leading package rely on NumPy as a fundamental piece of their infrastructure (examples include scikit-learn, SciPy, pandas, and tensorflow). Beyond the ability to slice and dice numeric data, mastering numpy will give you an edge when dealing and debugging with advanced usecases in these libraries. In this post, we’ll look at some of the main ways to use NumPy and how it can represent different types of data (tables, images, text…etc) before we an serve them to machine learning models. Creating Arrays

CS446: Fall 2017 - RELATE Course Description The goal of Machine Learning is to build computer systems that can adapt and learn from their experience. This course will study the theory and application of learning methods that have proved valuable and successful in practical applications. We review the theory of machine learning in order to get a good understanding of the basic issues in this area, and present the main paradigms and techniques needed to obtain successful performance in application areas such as natural language and text understanding, speech recognition, computer vision, data mining, adaptive computer systems and others. The main body of the course will review several supervised and (semi/un)supervised learning approaches.

edx If you have specific questions about this course, please contact us atsds-mm@mit.edu. Machine learning methods are commonly used across engineering and sciences, from computer systems to physics. Moreover, commercial sites such as search engines, recommender systems (e.g., Netflix, Amazon), advertisers, and financial institutions employ machine learning algorithms for content recommendation, predicting customer behavior, compliance, or risk. As a discipline, machine learning tries to design and understand computer programs that learn from experience for the purpose of prediction or control.

Introduction to Python [Video] Intrigued by Python? Learn how to get started with this popular language, whether you’re new to programming or just new to Python. This engaging video course teaches you Python’s core concepts and data types through hands-on exercises, and delivers fun and useful projects so you can put everything together. Syllabus The Spring 2020 iteration of the course will be taught virtually for the entire duration of the quarter. (more information available here ) Unless otherwise specified the lectures are Tuesday and Thursday 12pm to 1:20pm. Discussion sections will (generally) be Fridays 12:30pm to 1:20pm.

NVIDIA Deep Learning Institute Applications for Natural Language Processing (NLP) have exploded in the past decade. With the proliferation of AI assistants, and organizations infusing their businesses with more interactive human/machine experiences, understanding how NLP techniques can be used to manipulate, analyze, and generate text-based data is essential. Modern techniques can be used to capture the nuance, context, and sophistication of language, just as humans do. And when designed correctly, developers can use these techniques to build powerful NLP applications that provide natural and seamless human-computer interactions within Chat Bots, AI Voice Agents, and many more. Deep learning models have gained widespread popularity for NLP because of their ability to accurately generalize over a range of contexts and languages. In this workshop, you’ll learn how to use Transformer-based natural language processing models for text classification tasks, such as categorizing documents.