This advanced MATLAB project applies deep learning to analyze CERN LHC data for top quark detection. We implement a hybrid approach combining CNNs for calorimeter image analysis with boosted decision trees for jet feature processing. The tutorial covers high-energy physics concepts, distributed computing tools for big data, and techniques to handle class imbalance in particle datasets. Learn to visualize collision events, process detector outputs, and interpret model decisions using gradient-weighted class activation mapping (Grad-CAM). Includes performance benchmarks against traditional cut-based methods.