Deep dive into machine learning with TensorFlow, PyTorch, and scikit-learn. Learn neural networks, deep learning architectures, and practical AI applications. Browse 8 curated articles covering machine learning with practical implementation detail.
PyTorch, TensorFlow, fine-tuning, and practical model building
Model evaluation, transfer learning, and training trade-offs
Deployment-aware tutorials for modern ML teams
Learn how to fine-tune small language models with LoRA and QLoRA in Python using PyTorch, Transformers, PEFT, and TRL. Includes dataset formatting, training code, and practical tuning advice.
In-depth comparison of PyTorch and TensorFlow, analyzing performance, ease of use, deployment options, and helping you choose the right deep learning framework for your projects.
Comparing PyTorch and TensorFlow's real-world performance, analyzing why PyTorch has become the choice for many developers, from dynamic computation graphs and community ecosystem to performance optimization.
Learn how to use pretrained models like ResNet and EfficientNet to build custom image classifiers with PyTorch. Feature extraction, fine-tuning, and training best practices.
Deploy machine learning models to production with confidence. Learn containerization, API design, monitoring, scaling strategies, and best practices for reliable ML systems.
Learn how to build automated ML pipelines with scikit-learn. Covers pipeline design, feature engineering automation, model selection, and production deployment patterns.
Learn how to combine spaCy's pipeline with Hugging Face transformers for text processing, named entity recognition, and sentiment analysis in production.
Learn computer vision from scratch using OpenCV and Python. Master image processing, edge detection, face recognition, and real-time video analysis with practical code examples.