Introduction to Neural Networks
In this subheading, students are introduced to the basics of neural networks. They learn about the structure and functionality of artificial neurons and how they are interconnected in layers to form neural networks. Students understand the role of activation functions in neural networks and how they enable non-linear transformations of input data. They also explore the concept of weight initialization and the importance of proper initialization for efficient network training.
Convolutional Neural Networks (CNNs)
This subheading delves into the powerful field of convolutional neural networks (CNNs), which excel in tasks involving image and video data. Students learn about the architecture of CNNs, including convolutional layers, pooling layers, and fully connected layers. They explore advanced techniques like transfer learning, which allows models trained on large datasets to be fine-tuned for specific tasks. Through practical exercises, students gain proficiency in designing and training CNN models for tasks such as image classification and object detection.
Recurrent Neural Networks (RNNs)
Recurrent neural networks (RNNs) are designed to process sequential data, making them ideal for tasks like natural language processing and time series analysis. In this subheading, students delve into the architecture of RNNs, including popular variants like long short-term memory (LSTM) and gated recurrent units (GRU). They explore techniques for handling vanishing and exploding gradients, as well as strategies for model optimization and regularization. Students gain practical experience in training RNN models for tasks such as sentiment analysis and language generation.
