deep-learning
Build and train neural networks with PyTorch - MLPs, CNNs, and training best practices
Discover Tools skills, compare source repositories and read the instructions for each workflow.
Build and train neural networks with PyTorch - MLPs, CNNs, and training best practices
Discover patterns in unlabeled data using clustering, dimensionality reduction, and anomaly detection
Master machine learning foundations - algorithms, preprocessing, feature engineering, and evaluation
Build production-ready classification and regression models with hyperparameter tuning
Process and analyze text using modern NLP techniques - preprocessing, embeddings, and transformers
Build computer vision solutions - image classification, object detection, and transfer learning
Deploy ML models to production - APIs, containerization, monitoring, and MLOps
Linux system management - packages, services, users
Linux networking fundamentals - configuration, troubleshooting, security
Linux programming languages - installation, configuration, development
Linux data science and AI/ML environment setup - TensorFlow, PyTorch, CUDA, Jupyter
Linux shell scripting mastery - Bash, automation, cron jobs
Linux system administration - installation, maintenance, troubleshooting
Linux DevOps tools and practices - CI/CD, automation, infrastructure
Linux specialized topics - kernel, security hardening, performance
Linux development environment - compilers, build tools, IDEs
Linux shell basics - navigation, commands, file operations
Linux system architecture - processes, memory, storage, kernel
Master UIKit - Views, controllers, navigation, Auto Layout, animations
Master iOS development foundations - Architecture, lifecycle, memory, concurrency
Master SwiftUI - Declarative UI, state management, animations, modern iOS development
Master iOS data persistence - SwiftData, Core Data, Keychain, CloudKit
Master iOS testing - XCTest, UI testing, mocking, debugging, performance
Master iOS networking - URLSession, async/await, REST APIs, authentication
The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.