● Car Price Prediction Using Neural Networks

• Overview: Developed a neural network to predict car prices based on the number of cylinders, incorporating a linear pricing formula.
• Key Achievement: Successfully predicted mid-range car prices, with an 8-cylinder vehicle estimated at approximately $60,000, aligning closely with market expectations.
• Technical Details: Adjusted the neural network to accurately learn the relationship between the number of cylinders and the price.

● High-Accuracy Digit Classifier

• Overview: Designed and implemented a digit classifier using a custom dataset.
• Key Achievement: Achieved 99% accuracy within 10 epochs.
• Technical Details: Implemented an automatic training cancellation feature upon reaching the accuracy threshold.

● Advanced Digit Classifier with CNN

• Overview: Developed a digit classifier utilizing a single convolutional layer and MaxPooling 2D layer.
• Key Achievement: Attained 99.8% accuracy, with training automatically cancelled upon reaching the target.
• Technical Details: Optimized the model for high accuracy with minimal architecture complexity.

● CNN for Image Classification

• Overview: Created a convolutional neural network (CNN) for image dataset classification.
• Key Achievement: Achieved 100% training accuracy using a network with three convolutional layers, with training automatically cancelled upon surpassing 99.9% accuracy.
• Technical Details: Focused on deep learning techniques to maximize training efficiency and accuracy.