#Algorithms

Explore a collection of straightforward implementations of fundamental machine learning algorithms designed for those interested in learning and experimenting with ML models. This project offers insights into key algorithms, including Deep Learning, Random Forests, and SVM, utilizing Python's numpy, scipy, and autograd for clarity and simplicity. The code can be easily executed on local and Docker environments, encouraging contributions to foster collaborative learning and development in the machine learning field.

This repository provides a detailed evaluation of 28 superpixel algorithms utilizing 5 datasets to assess visual quality, performance, and robustness. It acts as a supplemental resource for a comparison published in Computer Vision and Image Understanding, 2018. Key updates include Docker implementations and evaluations of average metrics. The repository allows for fair benchmarking by optimizing parameters on separate training sets, focusing on metrics such as Boundary Recall and Undersegmentation Error.

This project assembles a wide array of Python examples focusing on algorithms, data analysis, and machine learning, utilizing practical, small-scale cases to foster comprehension. It enables Python learners to explore common operations, modules, object-oriented programming, regular expressions, decorators, iterators, generators, and graphing methods objectively. Serving as a valuable resource, it aids both beginners and experienced programmers in expanding their Python capabilities. With additional tutorial links, enthusiasts can gain further insights into various programming techniques and methodologies.

Jon Krohn's open-source project provides substantial learning resources on linear algebra, calculus, probability, statistics, and computer science as foundational elements of machine learning. The series offers practical applications and coding examples, accessible through platforms like YouTube, O'Reilly, and Udemy. It's crafted to support data scientists, software developers, and AI enthusiasts in strengthening their foundational knowledge for effective model innovation and deployment.

Discover an open-source data science repository with resources for learning and applying skills in practical scenarios. Access beginner to advanced tutorials, courses, and essential tools such as Python and R. Explore curated pathways like MOOCs, free courses, and intensive programs, equipped with a comprehensive toolbox including algorithms, machine learning packages, and visualization tools. Expand knowledge with literature, media, and community resources for a thorough understanding of data science.

GenRL is an actively developed PyTorch library facilitating reproducible and accessible reinforcement learning research. It features modular implementations, unified interfaces, and over 20 tutorials, all designed to support reliable algorithm development and benchmarking, seamlessly integrating with OpenAI Gym.

Explore a comprehensive resource collection for coding interviews focusing on data structures like hash tables and trees, and algorithms including dynamic programming and binary search. This guide includes essential patterns, popular problem sets like Blind 75 and Neetcode 150, along with recommended books, courses, and mock interviews. Utilize these resources to refine your technical skills and improve interview performance.

This project compiles video lectures from prestigious institutions such as MIT, Stanford, and UC Berkeley, offering resources across key computer science subjects like data structures, algorithms, and artificial intelligence. Catering to college-level learners and professionals, this resource supports independent study and skill advancement. It also welcomes contributions to expand its course catalog, while maintaining content quality by excluding spam and irrelevant links.

Explore this open-source project for an interactive introduction to data structures and algorithms. With animated illustrations and support for 12 programming languages, it offers an accessible learning path with one-click code execution. Encouraging community interaction, it has received notable endorsements for its practical approach in teaching foundational algorithm knowledge.

Explore reinforcement learning theories with practical Python examples using TensorFlow and PyTorch. This book covers significant algorithms such as PPO, RLHF, IRL, and PbRL within a robust mathematical framework. Each chapter provides cross-platform Python code (Windows, Linux, macOS), guiding through environment setups, including detailed gym frameworks. Suitable for anyone pursuing a comprehensive insight into reinforcement learning, featuring diverse models like average reward and semi-Markov.

Explore an advanced open-source platform for Embodied AI research by Allen Institute for AI. Supports environments like iTHOR and Habitat, offering task and algorithm versatility with PyTorch integration, visualizations, and multi-agent features. Provides tutorials, pre-trained models, and adaptable action spaces for diverse research applications.

Explore a diverse collection of Python algorithm implementations curated for educational exploration. These algorithms may not achieve the same efficiency as those in the standard library but offer vital learning opportunities for both newcomers and experienced coders. Connect with the open-source community on Discord and Gitter, and consider contributing by following the guidelines. Use the directory to effortlessly navigate the project and improve your Python skills through these practical examples.

This guide expands a simple study list into an extensive multi-month preparation plan tailored for aspiring software engineers aiming for technical interviews at major tech firms including Amazon, Google, and Facebook. Focusing on essential computer science concepts, it eliminates unnecessary topics, honing in on practical coding, data structures, algorithms, and computational theory. The plan incorporates resources like books, video courses, and practical coding exercises in languages such as C and Python. It caters to individuals shifting from different fields or enhancing current skills, facilitating entry into software development careers.