This module introduces deep learning concepts and reinforcement learning, focusing on neural networks and training algorithms.
Key Topics:
- Neural Networks: Introduction to perceptron, feedforward neural networks, activation functions
- Backpropagation Algorithm: Learning how neural networks learn and adjust weights.
- Deep Learning Frameworks: Introduction to TensorFlow and PyTorch for building and training deep learning models.
- Convolutional Neural Networks (CNNs): Techniques for image recognition, classification, and object detection.
- Recurrent Neural Networks (RNNs) and LSTMs: Handling sequential data, time-series forecasting, and natural language processing.
- Reinforcement Learning – Introduction.
Mini Projects:
- Image Classifier
- Time-Series Forecasting
- Autoencoder for Image Denoising
