4 Deep Learning Architectures

Introduction

Deep learning is a subset of machine learning that involves training artificial neural networks to learn from data. It has revolutionized the field of artificial intelligence and has led to breakthroughs in areas such as computer vision, natural language processing, and speech recognition. In this lesson, we will focus on the different architectures commonly used in deep learning.

Feedforward Neural Networks

Feedforward neural networks are the simplest type of neural network and are used for tasks such as classification and regression. They consist of an input layer, one or more hidden layers, and an output layer. The input layer receives the data, and each subsequent layer processes the data and passes it to the next layer until it reaches the output layer. Feedforward neural networks are easy to train and can be used for a wide range of tasks.

Convolutional Neural Networks (CNNs)

Convolutional neural networks are commonly used for image and video recognition tasks. They consist of convolutional layers, pooling layers, and fully connected layers. The convolutional layers apply filters to the input image, which helps to identify features such as edges and corners. The pooling layers downsample the output of the convolutional layers, reducing the dimensionality of the data. The fully connected layers then process the output of the pooling layers and produce the final output.

Recurrent Neural Networks (RNNs)

Recurrent neural networks are used for tasks that involve sequential data, such as speech recognition and natural language processing. They have a feedback loop that allows information to be passed from one step of the sequence to the next. This allows the network to remember previous inputs and use them to make predictions about future inputs. RNNs can be difficult to train, but they are very powerful for tasks that involve sequential data.

Generative Adversarial Networks (GANs)

Generative adversarial networks are used for tasks such as image and video generation. They consist of two neural networks: a generator network and a discriminator network. The generator network generates new data, and the discriminator network tries to distinguish between the generated data and real data. The two networks are trained together, with the generator network trying to fool the discriminator network and the discriminator network trying to correctly identify the generated data.

Conclusion

In conclusion, deep learning architectures have revolutionized the field of artificial intelligence and have led to breakthroughs in a wide range of areas. Feedforward neural networks are the simplest type of neural network and are used for tasks such as classification and regression. Convolutional neural networks are commonly used for image and video recognition tasks. Recurrent neural networks are used for tasks that involve sequential data, such as speech recognition and natural language processing. Generative adversarial networks are used for tasks such as image and video generation.

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⇦ 3 Introduction to Neural Networks 5 Training Deep Neural Networks ⇨