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Fundamentals of Neuroscience, Part 1: The Electrical Properties of the Neuron
Organic Chemistry 1
Mountains 101
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Master Mask R-CNN training for lung segmentation using PyTorch with transfer learning, data preprocessing, model inference, and visualization techniques for medical imaging applications.
Master background removal techniques with U²-Net in TensorFlow, from dataset preparation to model training and inference. Learn to build a complete pipeline for clean, precise image segmentation without green screens or blurry edges.
Master satellite water segmentation with EfficientNetB0 U-Net: build the model from scratch, preprocess data, train on water segmentation datasets, and run inference on test images for exceptional accuracy.
Master dust-storm image segmentation with VGG16 U-Net in TensorFlow 2, from building the model to training and inference on satellite imagery that separates storm plumes from background terrain.
Master YOLOv11 object detection combined with SAM2 segmentation to create precise binary masks from images using Python and Ultralytics API in this hands-on tutorial.
Dive into building a UNETR model for face segmentation with TensorFlow, from dataset loading to training and visualization of segmentation masks for 11 facial classes.
Master U-Net image segmentation from scratch with PyTorch, building a complete car segmentation model from dataset preparation to inference on the Carvana challenge.
Master real-time image and video classification with FasterViT and PyTorch through a complete implementation covering model loading, preprocessing, inference, and visualization for both images and video streams.
Master image classification with Keras Hub and Python through hands-on implementation of pretrained ResNet models, prediction decoding, and result visualization techniques.
Learn to classify images using Vision Transformers (ViT) in Python with Hugging Face and OpenCV. Implement a complete workflow from loading and preprocessing to displaying and saving classified images.
Dive into image recognition with LLaVA and Ollama, learning setup, image captioning, object recognition, and text extraction through practical Python code examples.
Learn to use BLIP-2 Visual Language Model to generate image captions and answer questions about image content through a hands-on coding tutorial with practical implementation steps.
Master building a Vision Transformer (ViT) from scratch with PyTorch for image classification, from dataset processing to patch embeddings, self-attention mechanisms, and model training.
Master CNN fundamentals and build a monkey species classifier using TensorFlow, Keras tuning, VGG16 transfer learning, and neural network visualization techniques.
Master building a butterfly image classifier using Python CNN with data augmentation, visualization, and model optimization techniques for impressive accuracy results.
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