Projects sharing features with TorchCode

This is an interactive Python tutorial delivered as a collection of Jupyter notebooks. It is designed as a structured learning path for beginners, teaching fundamental language concepts through a sequence of lessons that combine explanatory text with runnable code cells and embedded practice exercises. Each notebook is a self-contained unit that introduces a topic, demonstrates it with a minimal code example, and then asks the learner to write code themselves, receiving immediate feedback from the browser-based execution environment. The curriculum is built on a progressive concept-stacking mo

InterviewGuide is a comprehensive technical interview preparation platform that covers the full spectrum of software engineering recruitment, from foundational computer science concepts through to offer negotiation. It provides structured learning paths across algorithms, operating systems, databases, networking, and programming languages, with a particular emphasis on C++ and Go. The platform aggregates real interview experiences and company-specific questions from major tech employers, offering candidates a searchable database of past written exam problems and detailed accounts of actual int

CodingInterviews is a technical interview study resource and algorithm implementation guide. It provides a collection of typical programming challenges and reference implementations focused on the data structures and algorithms used in corporate interviews. The project serves as a coding challenge reference, offering a library of proven algorithmic solutions that act as a baseline for comparing candidate implementations. It includes a data structure implementation library and a set of interview problem sets designed for technical interview preparation. The repository organizes its content th

This project is a comprehensive machine learning interview guide and technical study resource designed for individuals preparing for machine learning and AI engineering roles. It provides a collection of materials and practice problems covering core algorithms, theoretical fundamentals, and the implementation of neural network architectures. The resource serves as a technical reference for generative AI development, focusing on the design and optimization of large language models and diffusion systems. It includes frameworks for system design, covering the architecture of production machine l

This project is a curated collection of solved algorithmic challenges and reference implementations. It serves as a study guide for software engineering, providing a library of data structure and algorithm problems used to analyze efficient problem-solving techniques. The repository functions as a solution reference for coding challenges across multiple programming languages. It includes source code examples and conceptual hints designed for technical interview preparation and the study of common coding patterns. The content is organized as a static source code repository with language-speci

This project is a reference collection for computer science fundamentals, providing a study guide and cheat sheets for algorithms and data structures. It serves as a resource for technical interview preparation, combining theoretical knowledge with practical implementation patterns for coding challenges. The content includes a comparative guide for analyzing the efficiency and characteristics of arrays, linked lists, hash tables, and binary search trees. It provides summaries of academic concepts including time and space complexity, sorting methods, and search strategies. The materials cover

This project is a static educational website and comprehensive curriculum focused on computer vision and deep learning. It serves as a public repository of instructional materials, lecture notes, and technical guides specifically detailing convolutional neural networks and visual recognition. The site is developed using static-site generation to host course documentation and student project directories. It provides structured academic resources that guide learners through image classification, generative modeling, and the implementation of various neural network architectures. The curriculum

This project is a technical interview study guide and knowledge base designed for software engineering and AI roles. It provides curated learning paths and a collection of high-frequency questions to help candidates prepare for technical assessments. The resource includes specialized study guides for machine learning, covering supervised and unsupervised learning, computer vision, and natural language processing. It also serves as a system design reference, analyzing architectural patterns, scalability trade-offs, and distributed infrastructure components. Beyond technical theory, the projec

Visualization Curriculum is a curated collection of educational Jupyter notebooks and interactive lessons designed to teach foundational principles and practical design patterns for data visualization. The curriculum covers core concepts such as visual encoding, graphical marks, scales, data transformation, multi-view composition, and interaction techniques. Lessons employ declarative grammars to define data graphics through high-level specifications that map data fields directly to visual marks and channels. The materials support multi-view composition architecture, coordinating distinct da

go-questions is a technical knowledge base and study resource for the Go programming language. It serves as a curated collection of interview questions and detailed explanations focused on the internal principles and advanced patterns of the Go ecosystem. The project is implemented as a static site generated from markdown files, which separates the technical educational content from the presentation logic. The site uses a file-system-based content hierarchy to automate navigation and maps folder structures directly to public URLs. The platform covers areas of technical knowledge synthesis, l

This project is a comprehensive machine learning educational resource and tutorial series delivered as a collection of interactive Jupyter Notebooks. It provides practical Python implementations for the end-to-end machine learning lifecycle, covering supervised and unsupervised learning, deep learning, and reinforcement learning. The resource distinguishes itself by providing detailed implementation guides for complex architectures, including transformers, generative adversarial networks, and convolutional neural networks. It also features specialized courseware for developing reinforcement l

This is a linear algebra course that teaches through Python code and visualization. It is a two-volume textbook of 66 topics, published as PDF chapters, with Jupyter notebooks that pair each mathematical idea with a short script and plot. Topics include vectors, coordinate systems, norms, projection, and matrices. Each topic is a short, self-contained chapter, so readers can study them independently or work through them in order from foundational to more advanced material. The notebooks let learners change inputs and watch the results and diagrams update. Separate slide decks condense each to

This is a comprehensive deep learning course delivered entirely through Jupyter Notebooks, designed to teach neural network construction using TensorFlow 2.x. The curriculum follows a sequential-model-first pedagogy, introducing the Sequential API before moving to functional and subclassing approaches, and covers the full spectrum of model building from regression and classification through convolutional neural networks, natural language processing, and time series forecasting. The course is structured around a checkpoint-based training workflow that saves the best model weights during traini

This project is an educational codebase and reference library that translates theoretical deep learning concepts into executable PyTorch code. It serves as a practical implementation of a deep learning textbook, providing a course-like structure of guided exercises and architectural examples for learning purposes. The repository includes a library of standard neural network architectures, including linear, convolutional, recurrent, and transformer models. It specifically implements a variety of deep learning patterns such as multilayer perceptrons, VGG networks, gated recurrent units, and lon

Tensor-Puzzles is an educational exercise suite and numerical computing tutorial designed for mastering tensor operations and broadcasting rules within PyTorch. It functions as an implementation trainer where users practice transitioning mathematical formulas into code by reimplementing deep learning mathematical primitives. The project utilizes a constraint-based exercise suite that restricts available library calls to force the use of specific tensor primitives. These challenges are structured as sequential puzzles that require users to solve tasks using a modular implementation pattern, wh

This repository is a curated collection of algorithmic challenges and programming exercises designed to support software engineering skill development and technical interview preparation. It provides a structured library of common coding tasks, each organized within a hierarchical directory system that encapsulates specific problem requirements and associated assets. The project distinguishes itself by maintaining multi-language reference implementations for each problem, allowing for the direct comparison of syntax and idiomatic approaches across different programming environments. This reso

This project is a collection of interactive Jupyter notebooks and a structured machine learning tutorial series. It serves as an educational resource for studying predictive modeling and statistical analysis through a curriculum of executable code examples. The notebooks are specifically designed to accompany video tutorials, integrating external video assets with live code to synchronize visual instruction with hands-on experimentation. This approach allows users to follow sequential lessons while executing and modifying machine learning workflows directly in a browser. The content covers t

This project is an automated code assessment tool and educational platform designed for frontend interview preparation. It provides a curated collection of technical challenges that allow developers to practice JavaScript mechanics, algorithmic problem solving, and core software engineering concepts. The platform utilizes a component-driven interface to organize and present educational content, which is managed through markdown-based modeling. It distinguishes itself by integrating automated evaluation systems that analyze user-submitted logic through abstract syntax tree analysis and sandbox

This project is a collection of interactive Jupyter notebooks designed to teach machine learning and deep learning fundamentals through hands-on coding exercises. It provides a structured curriculum that guides users through the end-to-end data science lifecycle, covering everything from initial data preprocessing to final model evaluation. The repository distinguishes itself by bridging theoretical data science concepts with practical implementation using standard industry libraries. It features a series of tutorials that demonstrate how to build and train predictive models and complex neura

This repository is a collection of interactive Jupyter notebooks designed as an educational resource for learning machine learning and data science. It provides a structured curriculum that guides users through the development of predictive models and the analysis of datasets using standard Python libraries. The project utilizes a narrative-driven approach where explanatory text is interleaved with executable code blocks. This format allows learners to execute workflows step-by-step, enabling the visualization of data patterns and the practical implementation of mathematical models within a p

This project is a comprehensive library of reference implementations for fundamental data structures and algorithms, designed to support technical interview preparation and software engineering assessments. It provides a structured collection of computational techniques for solving complex problems involving arrays, strings, graphs, trees, and mathematical analysis. The library distinguishes itself by offering specialized implementations for advanced topics, including concurrent programming patterns and geometric algorithms. It features thread-safe primitives for managing shared state and tas

This project is a collaborative repository and static site generator designed to help software engineers prepare for technical hiring assessments. It functions as a structured knowledge base that organizes algorithmic coding challenges and interview questions into a searchable, web-based interface. The platform distinguishes itself by categorizing practice material based on historical appearance frequency and company-specific interview patterns. Users can filter these coding challenges according to their preparation timeline, allowing for targeted study sessions that prioritize the most relev

This project is a comprehensive curriculum for mastering computer science fundamentals and preparing for technical interviews. It provides over 120 interactive Python coding challenges that focus on algorithmic skill development, data structure implementation, and logical problem solving. The learning experience is delivered through a series of executable notebooks that combine instructional content with hands-on coding exercises. Each challenge is self-contained and relies on automated unit tests to verify the correctness of user-implemented solutions against predefined constraints and edge

This repository is a structured collection of algorithmic coding challenges curated to assist with technical interview preparation. It functions as a comprehensive dataset that organizes programming problems based on the specific companies that have historically included them in their assessment processes. The project distinguishes itself by categorizing these challenges according to both the hiring organization and the frequency of problem appearance. This approach allows users to prioritize high-yield practice material, focusing their study efforts on the topics most relevant to their targe

This project is a comprehensive interview preparation guide and technical study resource for React. It functions as a frontend engineering curriculum and coding challenge bank designed to help developers master the internal mechanics, patterns, and core fundamentals of the React ecosystem. The resource distinguishes itself by providing a curated collection of technical interview questions, conceptual quizzes, and expert solutions. It includes a bank of coding challenges that can be solved in a browser-based environment with automated test cases and real-time rendering, as well as research int

This project is a computational linguistics course and academic repository providing a structured curriculum for learning natural language processing. It consists of educational materials, lecture slides, and a programming curriculum designed to teach the intersection of linguistic fundamentals and statistical data-driven processing. The repository integrates theoretical study with practical application through a combination of university-level lecture modules, curated textbook materials, and a collection of academic research papers. It emphasizes hands-on learning via linguistic data annotat

This project is a collection of comprehensive guides and reference materials designed for technical interviews, machine learning system design, and professional development. It serves as a technical knowledge base and a career coaching manual, providing structured resources to help candidates navigate the machine learning hiring landscape. The resource distinguishes itself by offering detailed frameworks for comparing industry roles, analyzing company types, and planning long-term career progression. It provides specific guidance on evaluating employer organizational health, identifying resea