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Top 10 Deep Learning Techniques Data Scientists Should Know About

Machine learning and AI have changed the world in the past couple of years with ground-breaking innovations. Due to the various technological creations, deep learning technology has gained immense popularity in scientific computing, and its algorithms are used in various industries to solve complex problems. Different types of deep learning techniques are used by data scientists and other data professionals to conduct business operations. In this article, we discuss the top deep learning techniques that data scientists and professionals should know about.

• Convolutional Neural Networks (CNN): CNN is also known as ConvNets. It consists of multiple layers and is mainly used for image processing and object detection. These layers focus on processing and extracting different features from the data. The CNN is widely used to identify satellite images, process medical images, and detect anomalies.

• Deep Reinforcement Learning: This is a deep learning algorithm that has an input, output, and multiple other layers that are hidden. This model focuses on predicting future outcomes based on the type of input actions. This technique is extensively used on board games, self-driving cars, robotics, and others.

• Long Short-Term Memory Networks: These are a type of recurrent neural network (RNN) that can learn and memorize long-term dependencies. LSTMs can retain information for a long time. They are useful in time-series prediction because they can remember previous outputs. Besides time-series prediction, it is also capable of speech recognition, music composition, and pharmaceutical development.

• Generative Adversarial Networks: It is a combination of two deep learning techniques of neural networks, i.e., a generator and a discriminator. Both the networks are competitive. The generator keeps producing artificial data that is identical to real data, and the discriminator relentlessly keeps detecting the real and the fake data. Such competition contributes to the overall effectiveness of the system.

 

 

• Boltzmann Machines: This network model does not have a predefined direction, and thus, has its nodes connected to it in a circular arrangement. This technique is used for system monitoring, binary recommendation platform, and specific dataset analysis. Because of its uniqueness, this technique is also used for model parameters production.

• Radial Basis Function Networks: These are special types of feedforward neural networks that use radial basis functions as activation functions. RBFNs have a vector input that feeds into the input layer. They also have a hidden layer and an output layer that is mostly used for classification, regression, and time-series prediction.

• Autoencoders: Autoencoders is one of the most commonly used types of deep learning This model operates automatically based on its inputs before taking an activation function and final output decoding. They are trained neural networks that replicate the data from the input layer to the output layer.

• Batch Normalization: This is one of the most vital parts of prepping data for deep learning techniques. Launched in 2015, it is one of the latest deep learning techniques that is extensively used presently. Batch normalization is used for improving the performance as well as the stability of an artificial neural network.

• Backpropagation: In deep learning, this technique is referred to as the central mechanism for neural networks to learn about errors in data prediction. Here, propagation refers to the transmission of data in a given direction through an allocated channel. The entire system can work according to the signal propagation at the moment of decision and send back data if there are drawbacks in the network.

• Transfer Learning: It is the process of improving a previously trained machine or model to perform new and more specific tasks. It makes use of the previous knowledge gained while solving one problem and applying it to other related problems. This technique is useful as it needs fewer amounts of data than other techniques and helps reduce large computation files.

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