josephmisiti/awesome-machine-learning
Stars: 60.3k License: NOASSERTION
This project is a curated list of machine learning frameworks, libraries, and software, organized by language. It provides a variety of powerful tools and libraries to help developers perform machine learning tasks in different programming languages. The project mainly has the following features and advantages:
- Provides machine learning frameworks, libraries and software in a variety of common programming languages
- Machine learning algorithms that support general goals
- Including related tools and libraries in computer vision, natural language processing and other fields
supabase/supabase
Stars: 56.9k License: Apache-2.0
Supabase is an open source Firebase alternative that builds Firebase functionality using enterprise-grade open source tools. Its main functions include:
- Hosted Postgres database
- Authentication and authorization
- Automatically generate API (supports REST and GraphQL)
- Live subscription
- Functions (including database functions and edge functions)
- File storage
The core strength of the project is to provide a developer experience similar to Firebase, with the following key features:
- Use PostgreSQL, a mature, reliable and powerful object-relational database system.
- Supports real-time data updates, monitors insert, update, and delete operations in PostgreSQL through the Elixir server and broadcasts them to the client in JSON format.
- Convert your existing PostgreSQL directly to a RESTful API or GraphQL API without writing additional code.
- Provide file management services and use Postgres for permission management.
- Improve documentation and community support.
kunal-kushwa/DSA-Bootcamp-Java
Stars: 13.3k License: NOASSERTION
This is a training camp material for DSA (data structures and algorithms) and interview preparation. There are corresponding video courses on Youtube.
opentofu/opentofu
Stars: 9.0k License: MPL-2.0
OpenTofu is an open source tool for building, changing, and versioning infrastructure securely and efficiently. It can manage existing popular service providers as well as custom in-house solutions.
- Infrastructure as code: Use high-level configuration syntax to describe infrastructure, so that data center blueprints can be versioned like code and can be shared and reused.
- Execution plan: OpenTofu generates an execution plan during the "planning" phase. The execution plan shows what OpenTofu will do when you call apply. This allows you to avoid any surprises when OpenTofu manipulates the infrastructure.
- Resource graph: OpenTofu builds a graph of relationships between all resources and parallelizes the creation and modification of any non-dependent resources. As a result, OpenTofu builds infrastructure as efficiently as possible and allows operators to gain insight into the dependencies in their infrastructure.
- Change Automation: Apply complex change sets to your infrastructure with less manual interaction. With the execution plan and resource map mentioned earlier, you can know exactly what changes OpenTofo will make to what content in what order, thus avoiding many possible human errors.
krishnaik06/The-Grand-Complete-Data-Science-Materials
Stars: 1.2k License: GPL-2.0
This project is a complete guide to data science, providing a variety of videos and materials. Key features include:
- Provides tutorials on Python, statistics, and SQL
- Provide Git and Github usage tutorials
- Tutorials covering feature engineering, feature selection, and exploratory data analysis (EDA)
- Introduces machine learning algorithms, and provides detailed explanations of deep learning, natural language processing (NLP), and MLOps.
The project has the following core advantages:
- Provides a rich variety of video resources, covering all aspects from basic knowledge to advanced technical applications;
- Covers a wide range of fields, including Python programming, statistics, database operations, machine/deep learning, etc.;
InternLM/lmdeploy
Stars: 1.1k License: Apache-2.0
LMDeploy is a toolkit for compressing, deploying and providing LLM (Language Model), developed by the MMRazor and MMDeploy teams. It has the following core features:
- Efficient inference engine (TurboMind): Based on FasterTransformer, the efficient inference engine TurboMind is implemented, supporting the inference of LLaMA and its variant models on NVIDIA GPU.
- Interactive reasoning mode: By caching the k/v value of the attention mechanism during multiple rounds of dialogue, the dialogue history is remembered and the repeated processing of historical conversations is avoided.
- Multi-GPU model deployment and quantification: Provides comprehensive model deployment and quantization support and validation at different scales.
- Persistent Batch Inference: further optimizes model execution efficiency.