Search Results for "loops"

...It includes over 100 classes and about 44 hours of video instruction, combined with code samples, projects, and a chat community for support. The material covers the fundamentals—variables, data types, loops, functions—as well as intermediate topics like date handling, list comprehensions, file IO, regular expressions, modules, and packages. The course is designed to be accessible: no prior programming experience required, and the resources are freely available. In addition, it is accompanied by a practical coding approach (projects) and is maintained as an open-source repository under Apache-2.0 license. ...

...The system emphasizes modularity, so teams can swap in new reasoning modules, data retrieval strategies, or domain knowledge bases depending on the research topic. Through self-supervised feedback loops, agents adjust their strategies based on prior outcomes, improving both the quality and relevance of results over time.

LLM101n is an educational repository that walks you through building and understanding large language models from first principles. It emphasizes intuition and hands-on implementation, guiding you from tokenization and embeddings to attention, transformer blocks, and sampling. The materials favor compact, readable code and incremental steps, so learners can verify each concept before moving on. You’ll see how data pipelines, batching, masking, and positional encodings fit together to train a...

...The project was originally inspired by an article by Stephen Curtis and has since grown into a community-driven learning resource. It serves as a roadmap for developers to review and master core principles such as closures, promises, prototypes, event loops, and other critical topics. While not a strict curriculum, it provides a structured path for deeper study and practical learning. The repository also encourages contributions, including personal notes, recaps, and translations, making it accessible to developers worldwide. Recognized as one of GitHub’s top open source projects in 2018, it remains a widely used reference for improving JavaScript skills.

LLM Course is a hands-on, notebook-driven path for learning how large language models work in practice, from data curation to training, fine-tuning, evaluating, and deploying. It emphasizes reproducible experiments: each step is demonstrated with runnable code, clear dependencies, and references to commonly used open-source models and libraries. Learners get exposure to multiple adaptation strategies—LoRA/QLoRA, instruction fine-tuning, and alignment techniques—so they can choose approaches...

...It provides clear code examples for foundational techniques like Q-learning, policy gradients, deep Q-networks, actor-critic methods, and value function approximation within familiar simulation environments. Each algorithm is structured with readable code, explanatory comments, and corresponding environment interaction loops so learners can easily trace how actions, rewards, and model updates connect. The project also includes demo scripts that visualize learning curves and allow students to observe policy improvement over training iterations. By using TensorFlow as the backbone, it highlights practical considerations such as tensor shapes, loss computation, optimization steps, and batching in an RL context.

...It covers the complete pipeline: tokenizing and parsing a simple, expression-oriented language, constructing an AST, and generating LLVM IR with a JIT so you can execute code interactively. Along the way it adds language features like user-defined functions, conditionals, loops, and operator precedence, demonstrating how each addition impacts parsing and codegen. Because it uses Haskell idioms, the code clearly separates pure syntax handling from effectful JIT operations, making the architecture easy to reason about. The examples double as a hands-on introduction to LLVM’s APIs without drowning you in infrastructure. ...

...Parameters can be specified symbolically by expressions containing other parameter values or global variables. Dependencies between parameters are resolved at run time. However, MuParserX expressions are compiled at design time. Zero-delay loops are detected and reported.

An automarking framework for Java programming assignments. This Jython based package simplifies creation of automarkers for assignments. Handles unimplemented methods, private methods, exceptions, detects all infinite loops and more.