Complex problems can be solved with the help of deep learning – but this requires a lot of computing power and huge amounts of data.

What Is Deep Learning?

Deep Learning (DL) is a Machine Learning (ML) subcategory that models data patterns as complex, multi-layered networks. It has the potential to solve complex problems that neither conventional programming work nor other machine learning techniques are suitable for.

In addition, with the help of this technology, significantly more accurate models can be formed than with other methods. The time frame to set up a useful model can be drastically reduced.

However, training deep learning models requires enormous computational power, and interpreting the models is difficult.

The key feature of it is that the trained models have more than one hidden layer between input and output. In most cases, these are artificial neural networks, but a few algorithms implement it with other hidden layers.

Deep Learning vs. Machine Learning

Classic machine learning algorithms generally run much faster than their deep learning counterparts. One or more CPUs are usually sufficient to train a classic ML model.

On the other hand, DL models often require hardware acceleration, for example, GPUs, TPUs, or FPGAs. If these are not used, training deep-learning models can take months.

Deep Learning Use Cases

WHERE IS DEEP LEARNING USED TODAY?

It filters patterns and trends from large amounts of data ( big data ). Typical examples of artificial intelligence are face, speech, and object recognition.

Marketing

It supports marketing departments in implementing personalized measures. To this end, customer needs are discussed and analyzed in detail.

This makes it possible to suggest suitable products. In addition, customer behavior can be predicted, and customer loyalty can be strengthened.

Customer Service

Customer service benefits from deep learning via chatbots. They replace or relieve human employees and answer customer inquiries.

The more modern the algorithm, the more complex the questions can be. With the frequency with which a chatbot is used, its experience grows, and it constantly learns new information.

Sales

Sales departments use it to create precise sales forecasts. For this purpose, existing information is processed and evaluated. The forecasts are created on this basis. It also saves the sales staff time, which helps generate leads.

Medicine

Patient data can be reliably analyzed using this. In this way, artificial intelligence examines findings and recordings and develops proposals for action. Treatment recommendations and diagnoses can be made.

Medication plans can also be drawn up, and help can be requested with prescriptions.

Human Resources

Applications can be analyzed automatically using it. This is used for complete processing or pre-sorting for the HR department. Data sources are viewed more efficiently.

In addition, AI ​​can reliably predict the willingness of applicants to perform.

Self-driving cars

Driving assistants, optimal perception, and maximum safety in autonomous driving are made possible by it. Doing this, the cars record large amounts of data on the roads and process them accordingly.

The knowledge gained serves as a basis for future driving behavior and decisions.

Deep Learning – Algorithms

It is often based on deep neural networks. Convolutional neural networks (CNNs) are often used for computer vision. In contrast, recurrent neural networks (RNNs) are used for things like natural language processing – as are long short-term memory (LSTM) networks and attention-based neural networks.

Random forests (or random decision forests), on the other hand, are not neural networks and are useful when dealing with classification or regression problems.

Convolutional Neural Networks

To simulate a visual cortex, CNNs typically use:

• convolution and pooling layers,
• ReLU layer,
• fully connected layers or
• Loss Layer.

The convolution layer uses the integrals of many small, overlapping regions. The pooling layer relies on nonlinear down sampling. ReLU layers apply the non-saturated activation function f(x) = max (0,x). In a fully connected layer, the neurons connect to all the previous layer’s activations.

A loss layer calculates how network training penalizes the deviation between the predicted and real labels, using a softmax or cross-entropy loss function for classification or a Euclidean loss function for regression.

RNN, LSTM, and attention-based neural networks

In feed-forward neural networks, information flows from the input layer through the hidden layers to the output layer. Such a network can, therefore, only process one state at a time.

In recurrent neural networks, information is looped, allowing the network to “remember” previous outputs. This enables the analysis of sequences and time series. RNNs have two common problems: exploding and vanishing gradients.

In the case of LSTMs, the network can “forget” or “remember” previous information. This effectively gives an LSTM both long-term and short-term memory and solves the problem of vanishing gradients.

LSTMs can handle sequences from hundreds of historical inputs.

Random Forests

Another form of a deep learning algorithm is the random forest or random decision forest. This consists of many layers but is not constructed with neurons but with decision trees.

Its outputs consist of the statistical average values ​​of the individual decision trees. The “random aspect” is created by applying “bootstrap aggregation” to individual decision trees and random feature subsets.