Explore deep learning prediction using Torch-TensorRT in this comprehensive tutorial video. Learn to accelerate inference speed by comparing CPU, CUDA, and TensorRT implementations. Set up the development environment with Docker and Nvidia tools, then dive into using PyTorch to load and utilize the ResNet50 neural network for image classification. Discover techniques for image preprocessing, batch processing, and interpreting model predictions. Implement and analyze benchmarks to compare performance across different hardware configurations. Follow along to trace models, convert to TensorRT, and optimize inference speed. Gain practical insights into deep learning deployment and performance optimization for beginners and intermediate practitioners alike.