A Survey: Time Travel in Deep Learning Space: An Introduction to Deep Learning Models and How Deep Learning Models Evolved from the Initial Ideas

TL;DR

This survey traces the historical evolution of deep learning models from neural networks to advanced architectures like CNNs, RNNs, and generative models, highlighting key developments and open questions.

Contribution

It provides a comprehensive overview of deep learning's development, connecting early ideas to modern models and discussing practical techniques and open challenges.

Findings

Deep learning evolved from connectionist models to complex architectures.

Convolutional neural networks improved image processing capabilities.

Recurrent neural networks and LSTMs advanced time series modeling.

Abstract

This report will show the history of deep learning evolves. It will trace back as far as the initial belief of connectionism modelling of brain, and come back to look at its early stage realization: neural networks. With the background of neural network, we will gradually introduce how convolutional neural network, as a representative of deep discriminative models, is developed from neural networks, together with many practical techniques that can help in optimization of neural networks. On the other hand, we will also trace back to see the evolution history of deep generative models, to see how researchers balance the representation power and computation complexity to reach Restricted Boltzmann Machine and eventually reach Deep Belief Nets. Further, we will also look into the development history of modelling time series data with neural networks. We start with Time Delay Neural Networks and move further to currently famous model named Recurrent Neural Network and its extension Long Short Term Memory. We will also briefly look into how to construct deep recurrent neural networks. Finally, we will conclude this report with some interesting open-ended questions of deep neural networks.