Top 6 Notable Trends in Deep Learning and Neural Networks

The basic idea of neural networks is to simulate multiple interrelated cells in a computer "brain", allowing it to learn from the environment, recognize patterns, and make decisions similar to humans.

A basic neural network contains about millions of artificial neurons, called units. These units are arranged in layers, each layer connected to the next. The units are divided as follows:

1. Input units – designed to receive information from the external environment.

2. Hidden units – intermediate between input and output; each hidden unit is a compressed linear function of its inputs.

3. Output units – determine how the network should respond to the recently acquired information.

Most neural networks are "fully connected". This means each hidden unit and each output unit is connected to every unit on the opposite side of the layer. The connections between units are called "weights". Weights can be positive or negative, depending on how much they influence another unit.

When a neural network is trained, different input units feed different information patterns into the network. These trigger the hidden layers that eventually reach the output units. This is known as a feedforward network, a common design. After sufficiently training the network with various learning examples, it reaches a stage where it can present a set of completely new inputs that it has not encountered during training and can predict satisfactory outputs.

The following are some important trends in neural networks and deep learning.

Capsule Networks

Capsule networks are an emerging form of deep neural networks. They process information in a way similar to the human brain, essentially meaning capsule networks can preserve hierarchical relationships.

This contrasts with convolutional neural networks. Although CNNs are the most widely used neural networks to date, they fail to consider the crucial spatial hierarchy between simple and complex objects. This leads to misclassifications and higher error rates.

For simple recognition tasks, capsule networks provide higher accuracy while reducing the number of errors. Moreover, they do not require large amounts of training model data.

Deep Reinforcement Learning (DRL)

Deep reinforcement learning is a form of neural network that learns by interacting with the environment through observation, action, and reward. DRL has been successfully used to determine game strategies, such as Atari and Go. The famous AlphaGo project used it to defeat human champions and achieved success.

DRL is essential because it is one of the most versatile learning techniques you can use to develop business applications. It also requires much less data for training. Another advantage is that you can train it via simulation, which completely eliminates the need for labeled data.

Reinforcement Learning

So far, the biggest obstacle for machine learning, especially deep learning, is the availability of large amounts of labeled data for training neural models. Two techniques can help address this issue – synthesizing new data and transferring a training model from task A to task B.

Techniques such as transfer learning (transferring learning from one task to another) or one‑shot learning (learning with only one or no related examples) make them lean‑data learning techniques. Similarly, when interpolating or simulating to synthesize new data, it helps obtain more training data. ML experts usually refer to this as augmenting existing data to improve learning.

Such techniques can be used to solve broader problems, especially when there is limited historical data.

Supervised Models

Supervised models are a form of learning that infer specific functions from previously labeled training data. They use a supervised learning algorithm that includes a set of inputs with corresponding correct outputs.

The labeled inputs are compared with the labeled outputs. Based on the difference, you can compute an error value and then use the algorithm to learn the mapping between inputs and outputs.

The ultimate goal is to approximate the mapping function so that when new input data is received, it can predict precise output data. Similar to a teacher‑supervised learning process, the learning stops when the algorithm reaches satisfactory performance or accuracy.

Networks with Memory Models

An important distinction between humans and machines is the ability to work and think. Undoubtedly, computers can be pre‑programmed to perform a specific task with high precision. However, problems arise when they need to operate in different environments.

To enable machines to adapt to real‑world environments, neural networks must be able to learn sequential tasks without forgetting. Neural networks need various powerful architectures to overcome forgetting. These can include:

1. Long‑term memory networks, which can handle and predict time series.

2. Elastic weight consolidation algorithms, which can slow learning speed based on priority defined by previously completed tasks.

3. Progressive neural networks that are not affected by forgetting and can extract useful features from previously learned networks for new tasks.

Hybrid Learning Models

Various types of deep neural networks, including GANs and DRL, have shown great potential in performance and have been widely applied to different types of data. However, deep learning models cannot model uncertainty in a Bayesian or probabilistic manner.

Hybrid learning models can combine these two approaches and leverage the advantages of both. Some examples of such hybrid models include Bayesian GANs and Bayesian conditional GANs.

Hybrid learning models expand the range of business problems that can be addressed, including deep learning with uncertainty. This enables higher performance and model interpretability, which can encourage broader adoption.

Summary

Artificial intelligence lays the foundation for the new era, and many technological breakthroughs are built upon it. In this article, we introduced several notable trends in deep learning and neural networks. Autonomous driving cars combine multiple models, such as deep reinforcement learning and convolutional neural networks, for visual recognition.

We may have missed many other noteworthy trends. What do you think are the significant trends in deep learning and neural networks? Please share your views in the comments.

Original title: "Top 6 Notable Trends in Deep Learning and Neural Networks"

Author: Manjunath M

Translator: lemon

This article is a translation and does not represent the views of the YunJia community.