From robotics and self-driving cars to chatbots and virtual assistants, deep learning is everywhere. Applications of deep learning can be seen in almost every industry these days. But are you aware of what techniques go behind the working of these self-driven cars or virtual assistants? Well, here come the deep learning algorithms! This blog sheds light on the top five deep learning algorithms that every data scientist must become familiar with to help build some fantastic data science projects.
ConvNets, often called CNNs, are a specific type of network architecture for deep learning algorithms useful for image recognition and pixel data processing. CNNs process the data by passing it through multiple layers and then extracting features to perform convolutional operations. Rectified Linear Units (ReLUs), present in the first layer, i.e., the convolutional layer, are used to correct the feature map. These feature maps are corrected for the subsequent feed using the next layer, i.e., the Pooling Layer. Pooling is often a down-sampled sampling technique that decreases the dimensionality of the feature map. The third layer, or the Fully Connected Layer, uses the flattened matrix or 2-D array obtained from the pooling layer to classify and identify the image.
Recurrent neural networks, or RNNs, are a subset of traditional feedforward artificial neural networks that can handle sequential data and be trained to retain knowledge from the past. RNNs are modified to work with time series data or data that contains sequences. RNNs implement the working strategy by sending output feeds at (t-1) time if the time is defined as t. The output determined by t is then passed at input time t+1. Similar operations are performed for all inputs of any length. RNNs have the additional feature of storing historical data, thus, even if the model size is increased, the input size does not grow.
Recurrent neural networks (RNN) have a subtype known as the Long Short-Term Memory (LSTM) model. It is used to identify patterns in data sequences, such as those in sensor data, stock prices, or natural language. Recurrent neural networks have the disadvantage of only storing past data in their "short-term memory." The longest retained data is deleted and replaced with new data once its memory runs out. By keeping only specific parts of data in short-term memory, the LSTM model tries to get around this issue.
An unsupervised artificial neural network called an "autoencoder" can learn and comprehend how to effectively compress and encode data. The autoencoder aims to train the network to capture the essential parts of the input image to learn a lower-dimensional representation (encoding) for higher-dimensional data, often for dimensionality reduction. Autoencoders are typically useful for dimensionality reduction, generating image and time series data, image denoising, anomaly detection, etc.
GAN is a generative modeling technique that produces new data based on training data that resembles training data. Generator and Discriminator, the two fundamental models (two neural networks) of GANs, compete with one another and can capture, copy, and analyze the variations in a dataset. By creating data identical to those in the training set, the generator attempts to mislead the discriminator. By distinguishing between fake and genuine data, the discriminator attempts to avoid being misled.
There are a few more deep learning algorithms, such as Multilayer Perceptrons (MLPs), Radial Basis Function Networks (RBFNs), Restricted Boltzmann Machines (RBMs), Self-Organizing Maps (SOMs), Deep Belief Networks, etc.
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