Deep-Learning-with-Python

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Contents :

1. Simple Neural Network Models using TensorFlow and PyTorch

In this project, I built and compared neural network architectures using two popular deep learning frameworks: TensorFlow and PyTorch. The goal was to analyze the effect of different configurations (number of neurons and hidden layers) on model performance.

2. Building Simple Artificial Neural Network (ANN) Model

In this jupyter notebook, I walk through the process of building a simple artificial neural network (ANN) to classify handwritten digits using the MNIST dataset.

3. Image Classification using Convolutional Neural Network (CNN's) Model

This project shows comparison between Artificial Neural Networks (ANN's) and Convolutional Neural Networks (CNN's) on the popular CIFAR-10 Image Classification dataset.

4. Building Recurrent Neural Network (RNN) Model

In this jupyter notebook, I have build a Next Word Prediction Model using RNN's.

5. Vehicle Detection using Faster R-CNN

This project demonstrates how to detect vehicles in a video using a pre-trained Faster R-CNN model. The pipeline includes extracting frames from a video, performing object detection on each frame, drawing bounding boxes around detected vehicles, and recompiling the processed frames back into a video.