@Joboa
simple, concise and easy to grasp
Nothing here yet.
Nothing here yet.
Sep 11, 2025 · 9 min read · Learning Objectives By the end of Log #6, you will be able to: Create FastAPI endpoints for image prediction and layer visualization Return structured JSON responses including predictions, layer info, and feature maps Expose model metadata through...
Join discussionSep 6, 2025 · 7 min read · Learning Objectives By the end of Log #5, you will be able to: Preprocess uploaded images for CNN models (ResNet18/ResNet50) Convert model tensors back into visualizable images Generate activation feature maps and grids for frontend visualization ...
Join discussionAug 16, 2025 · 4 min read · Learning Objectives By the end of Log #4, you will be able to: Understand the fundamental problem that ResNet architectures solve Explain the concepts of residual and skip connections Analyze the architectural differences between ResNet18 and ...
Join discussionAug 9, 2025 · 9 min read · Learning Objectives By the end of Log #3, you should be able to: Load pretrained CNN models like ResNet18 and ResNet50 using PyTorch’s torchvision.models. Automatically use the official preprocessing transforms from the selected model’s weights. E...
Join discussionAug 3, 2025 · 5 min read · Learning Objectives By the end of Log#2, you should be able to: Set up a FastAPI backend for the CNN Visualizer project. Install and understand the core packages required for a deep learning backend. Create health check endpoints and test them wit...
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