Compared Deep Learning Neural Networks to Traditional Machine Learning Algorithms to better understand the training process + advantages of both models
Model Definition: Within the notebook, you'll find the architecture definition for CNN and SVM models:
Training and Evaluation:
Training CNN involves backpropagation with gradient descent optimization algorithms like Adam or SGD, where the network adjusts its parameters based on minimizing a predefined loss function.
Training SVM entails maximizing the margin between classes while minimizing classification errors through solving a convex optimization problem using techniques such as Sequential Minimal Optimization (SMO) or gradient descent.
Evaluation Metrics: Both CNN and SVM models can be evaluated using standard metrics including accuracy, precision, recall, and F1-score, offering insights into their performance in correctly classifying MNIST digits.
Deployment and Usage: Post-training, CNN and SVM models find utility in MNIST digit classification tasks, either integrated into production systems or for batch processing of image data.