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Explore neural networks' ability to detect fake face images using Grad-CAM algorithm. Learn about datasets, model training, and visualization techniques for understanding AI decision-making in image classification.
Explore AI-powered rap generation using Tacotron 2 and ChatGPT. Learn to create a user-friendly interface with Gradio for generating lyrics and vocals.
Explore the evolution of Convolutional Neural Networks from the 1980s to present day, highlighting key breakthroughs and advancements in this fundamental deep learning architecture.
Explore a graphical solver for mathematical optimization, visualizing function minimization with equality constraints and generating LLM-based reports for enhanced understanding.
Discover how to build a RAG pipeline using LLaMA 3.1 to automate article discovery and summarization based on Google Trends, showcased in a Streamlit web app for efficient content curation.
Master local implementation of Mono-InternVL for advanced computer vision tasks including OCR, object detection, and image parsing using Python, achieving high-precision results.
Learn to build a Python application that enables interactive video conversations using Vision Language Models and Computer Vision techniques for local video Q&A functionality.
Learn how to fine-tune and run SmolVLM2, the smallest video language model designed for edge devices like iPhones, with step-by-step instructions.
Explore Mistral's new OCR model capabilities through rigorous testing on challenging tasks to evaluate its performance and practical applications.
Discover essential Python coding techniques for machine learning across four key areas: vectorization, comprehension, built-in functions, and generators & iterators to optimize your ML code.
Master fine-tuning LLMs for reasoning tasks using GRPO and Unsloth library, requiring only 7GB VRAM. Learn environment setup, data preparation, model training, and result analysis for local implementation.
Explore GRPO training experiments on small Gemma language models with practical implementations and hands-on demonstrations in this focused machine learning tutorial.
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