Large Language Models (LLMs): Basics, Architecture, and Applications
Comprehensive guide to Large Language Models. Learn LLM architecture, capabilities, limitations, and practical applications with Python.
Comprehensive guide to Large Language Models. Learn LLM architecture, capabilities, limitations, and practical applications with Python.
Learn how to integrate Large Language Models into production applications. Master API calls, streaming, error handling, cost optimization, and best practices.
Comprehensive introduction to machine learning fundamentals. Learn core concepts, types of ML, key terminology, workflows, and real-world applications.
Master memory management and garbage collection. Learn how modern languages allocate and deallocate memory, different GC strategies, and best practices for efficient code.
Master metaclasses in Python. Learn what metaclasses are, how they work, when to use them, and how to create custom metaclasses with practical examples.
Learn advanced pytest techniques: fixtures for setup/teardown, mocking external dependencies, and best practices for writing isolated, maintainable tests.
Comprehensive guide to model evaluation in machine learning. Learn evaluation metrics, cross-validation techniques, and hyperparameter tuning strategies to build better models.
Comprehensive guide to monitoring and logging in production environments. Learn best practices, tools, metrics, alerting strategies, and observability patterns.
Master Go's multiple return values — the error pattern, named returns with defer-based wrapping, returning optional values with bool, the comma-ok idiom, and when to use structs …
Master multiprocessing in Python. Learn how to use Process, Pool, Queue, and Pipe for parallel processing, inter-process communication, and building high-performance applications.