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Learn to analyze cryptocurrency sentiment on Twitter using LangChain and ChatGPT. Create custom prompts, integrate with LLMChain, and build a Streamlit app for real-time sentiment 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 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 fine-tune Llama 7B with Alpaca LoRa on a custom bitcoin sentiment dataset. Covers data preprocessing, model training, and performance analysis for sentiment detection in cryptocurrency tweets.
Explore different memory types for ChatGPT chatbots using LangChain, including buffer, summary, window, and vectorstore memory. Learn implementation techniques for coherent conversations.
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.
Learn to create stunning 3D photos from regular images using machine learning and Python. Explore the process of 3D photo inpainting, from setup to final results, in this hands-on tutorial.
Learn to build reproducible machine learning pipelines using Python and DVC. Track experiments, manage data versions, and compare model metrics for improved ML workflow efficiency.
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