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Fine-tune YOLO v5 for custom object detection using PyTorch. Learn model installation, training, evaluation, and application on a clothing dataset.
Explore DeepSeek Coder's capabilities in coding tasks, from simple functions to complex problems and app development. Compare its performance to other AI models in various programming scenarios.
Explore AutoGen for creating AI agents with LLMs. Learn to build a stock price analyzer and a multi-agent system for cryptocurrency analysis using Python and GPT models.
Learn to evaluate LayoutLMv3 for document classification, save and load models to HuggingFace Hub, and analyze performance using confusion matrices in this hands-on tutorial.
Explore the breakthrough of Generative Agents, simulating human-like behavior in interactive environments using Large Language Models, with key components and real-world applications.
Learn to use LangChain for advanced document processing, including loading PDFs, creating embeddings, and performing question-answering with ChatGPT. Explore data loaders, indexes, and vector stores for efficient text analysis.
Learn to build a neural network for classification using PyTorch, covering data preparation, network structure, and implementation. Gain hands-on experience in deep learning fundamentals.
Explore Stanford Alpaca's capabilities, learn inference techniques, and compare its performance to ChatGPT in this step-by-step tutorial on setting up and optimizing the model.
Learn to implement a Simple Linear Regression model using PyTorch, covering data exploration, model creation, tensor conversion, training, and prediction analysis in this beginner-friendly tutorial.
Learn to deploy LayoutLMv3 for document classification using Streamlit and HuggingFace Spaces. Covers model loading, input preparation, and classification process, resulting in a functional demo app.
Comprehensive guide to PyTorch tensors: creation, types, operations, indexing, reshaping, and data handling. Essential for beginners in machine learning and deep learning projects.
Simulated machine learning engineer interview using ChatGPT, covering key ML concepts, coding tasks, and system design. Explores AI's ability to handle technical questions and practical scenarios.
Explore PyTorch Lightning for easier Deep Learning projects, using Google's GoEmotions dataset to build an emotion classification model with practical coding examples.
Learn to build and train an LSTM Deep Neural Network for Bitcoin price prediction using multivariate time series data, PyTorch, and PyTorch Lightning. Covers dataset creation, model building, and prediction analysis.
Learn to create custom datasets for YOLOv5 object detection, focusing on clothing items in images using OpenCV, PyTorch, and Python. Includes dataset preparation, format conversion, and file structure explanation.
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