Top 10 Deep Learning Techniques Data Scientist Should Know in 2021.

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

There are different types of deep learning models in which are accurate and effectively tackles the problem that are too complex for the human brain. Here’s how:

Top 10 Deep Learning Techniques Data Scientist Should Know in 2021

Top 10 Deep Learning Techniques

1. Convolutional Neural Networks (CNN): CNN is also known as ConvNets and it is an advanced and high-potential type of the classic artificial neural network model. It used to built and tackles higher complexity, preprocessing, and data compilation. CNN consists of multiple layers and is mainly used for image processing and object detection. These layers are focus on processing and extracting different features from the data. The CNN are widely used technique to identify satellite images, process medical images, and detect anomalies.

The four stages of CNN building:-

Convolution: These process derives the feature maps from input data, followed by a function applied to these maps.
Max-Pooling: It helps CNN to detect the image given modifications.
Flattening: In this stage, the data’s are generated and then flattened for a CNN to analyze.
Full Connection: It described as a hidden layer that compiles the loss function for a model.

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

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

4. Generative Adversarial Networks(GAN): GAN is a combination of two deep learning techniques of neural networks, i.e., a generator and a discriminator. Both networks are very competitive. The generator produces artificial data that is identical to real data, and the discriminator relentlessly keeps detecting the real and the fake data. These competition contributes to the overall effectiveness of the system.

5. Boltzmann Machines: A Network model does not have a predefined direction, and its nodes are connected to in a circular arrangement. These technique are used for system monitoring, binary recommendation platform, and specific dataset analysis. Because of its uniqueness, this technique is also be used in model parameters production.

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

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

The Types of Autoencoders are:

Sparse – Where hidden layers and the outnumber in the input layer for the generalization approach to take place to reduce overfitting. It limits the loss function and also prevents the autoencoder from overusing all its nodes.
Denoising – It is a modified version of inputs gets transformed into 0 at random.
Contractive – Addition of a penalty factor to the loss function that limit overfitting and data copying, incase of hidden layer outnumbering input layer.
Stacked – To an autoencoder, another hidden layer gets added, it leads to two stages of encoding to that of one phase of decoding.

8. Batch Normalization: This technique 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 and extensively used technique presently. Batch normalization is manely used for improving the performance as well as the stability of an artificial neural network.

9 Backpropagation: In deep learning, Backpropagation technique is referred as the central mechanism for neural networks to learn about errors in the data prediction. Here, the propagation refers to as 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 the data if there are drawbacks in the network.

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

These are the multiple deep learning techniques that come with its functionalities and practical approach. Once these models are identified and put in the right scenarios, it can lead high-end solutions based on the framework used by developers.


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