Welcome to the Advanced Deep Learning Tutorial. This guide will delve into the intricacies of deep learning, covering topics from neural networks to convolutional and recurrent neural networks.
Table of Contents
- Introduction
- Neural Networks
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Further Reading
Introduction
Deep learning is a subset of machine learning that structures algorithms in layers to create an "artificial neural network" that can learn from large amounts of data. It's a subset of machine learning that structures algorithms in layers to create an "artificial neural network" that can learn from large amounts of data.
Neural Networks
Neural networks are a series of algorithms that can recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. They are composed of layers of interconnected nodes, or neurons, which process information.
Convolutional Neural Networks (CNNs)
CNNs are particularly good at identifying spatial patterns in images, such as edges, shapes, and textures. They are widely used in image recognition and processing tasks.
Recurrent Neural Networks (RNNs)
RNNs are designed to recognize patterns in sequences of data, such as time series or natural language. They are particularly useful for tasks that involve sequential data.