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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.
Learn to deploy a pre-trained BERT model for sentiment analysis as a REST API using FastAPI, PyTorch, and Hugging Face. Covers project setup, API creation, model integration, and real-time predictions.
Learn to build a sentiment classification model using BERT, Hugging Face, and PyTorch. Covers BERT basics, fine-tuning for sentiment analysis, model training, evaluation, and prediction from raw text.
Learn text preprocessing for sentiment analysis using BERT, Hugging Face, and PyTorch. Covers tokenization, padding, attention masks, dataset creation, and data splitting for NLP tasks.
Explore fundamental data visualization techniques using Matplotlib and Seaborn in Python. Learn to create histograms, box plots, violin plots, bar charts, line charts, scatter plots, and more for effective exploratory data analysis.
Learn to classify traffic signs using transfer learning with PyTorch. Explore data, build datasets, train a pre-trained ResNet model, evaluate performance, and make predictions on new images.
Learn to forecast Coronavirus cases using LSTM and PyTorch. Covers data preprocessing, model building, training, evaluation, and future predictions with real-world time series data.
Comprehensive guide to image processing with OpenCV in Python, covering essential techniques from basic operations to advanced manipulations for computer vision applications.
Learn essential Pandas operations for effective data analysis in Python, including handling missing values, manipulating columns, and basic plotting techniques.
Learn to build, train, and evaluate a neural network for weather prediction using PyTorch. Covers data preprocessing, model architecture, GPU acceleration, and practical applications.
Explore neural networks, perceptrons, and sigmoid neurons using TensorFlow.js. Learn about activation functions, network architecture, and weight initialization for machine learning in JavaScript.
Learn to build and train logistic regression models in TensorFlow.js for diabetes prediction. Covers data visualization, dataset creation, model evaluation, and advanced techniques.
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 the breakthrough of Generative Agents, simulating human-like behavior in interactive environments using Large Language Models, with key components and real-world applications.
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