Top 10 Deep Learning Algorithms — Part 2
This is the final leg of the two series blog about Deep learning algorithms. In this blog, we will focus on the rest of the Deep Learning algorithms viz.
6. Multilayer Perceptron.
7. Self-Organizing Maps.
8. Restricted Boltzmann Machines.
9. Deep Belief Network.
10. Auto-Encoders.
6. Multilayer Perceptron (MLPs)
What is it?
In the realm of deep learning, a multi-layered perceptron (MLP) is one of the most prevalent neural network models. An MLP is also known as a “vanilla” neural network, it is a simpler version of today’s complicated models. The techniques it introduced have cleared the way for more complex neural networks.
MLP is used for stock analysis, image identification, spam detection, and election-voting predictions.
How does it work?
The perceptron is made up of two fully connected layers: an input layer and an output layer. MLPs have numerous hidden layers in between.
The MLP has the following algorithm:
● Step 1 — The dot product of the input with the weights that exist between the input layer and the hidden layer (WH) is pushed forward with the help of MLP. At the hidden layer, this dot product returns a value.
● Step 2 — MLPs uses activation functions at each layer. A few of the activation functions to consider are Rectified linear units (ReLU), sigmoid function, and tanh. Any of these activation functions can be used to push the calculated output at the current layer.
● Step 3 — After the inputs cross the activation function, a dot product is taken with the corresponding weights. The calculated output is transferred to the next layer.
● Steps two and three are iterated until we reach the output layer.
● In the case of training data, the calculations will be employed in the output layer for either a backpropagation method that corresponds to the activation function chosen for the MLP. In the case of testing data, the decision will be made based on the output.
MLPs are the foundation of all neural networks. They have substantially increased the computing capacity of computers and are typically used to solve classification and regression problems. Thanks to the multilayer perceptron, computers are no longer bound by XOR situations and can learn rich and complicated models.
7. Self-Organizing Maps (SOMs)
What is it?
A self-organizing map (SOM) is a clustering approach that aids in the discovery of categories in huge datasets, such as identifying client profiles from a list of previous transactions. It’s a type of unsupervised neural network in which neurons (also known as nodes or reference vectors) are placed in a single, 2-dimensional grid that can be either rectangular or hexagonal.
After numerous iterations, the neurons on the grid will gradually coalesce around places with a high density of data points. As a result, the segment of output with several neurons indicates underlying data clusters. As the neurons move, the grid bends and twists inadvertently to better reflect the overall topological nature of our data.
How does it work?
In summary, a SOM is made up of grid neurons that adapt to the intrinsic shape of our data over time. So, how does the SOM grid figure out what shape our data should take? SOM accomplishes this by an iterative approach, which is outlined in the steps below.
● Step 0: Place the grid’s neurons in the data space at random.
● Step 1: Choose one data point by cycling through the dataset in order, either randomly or methodically.
● Step 2: Locate the neuron that is closest to the data point of interest. This neuron is The Best Matching Unit (BMU).
● Step 3: Bring the BMU closer to the data point in question. The learning rate decreases with each iteration. This defines the BMU’s distance.
● Step 4: Bring the BMU’s neighbours closer to the data point. A radius surrounding the BMU is used to identify neighbours, and the value of this radius decreases with each iteration.
● Step 5: Before repeating Steps 1–4, update the learning rate and BMU radius. Repeat these processes until the placements of the neurons are stable.
Validation should be used to fine-tune the learning rate and BMU radius. If the numbers for both are too high, neurons will be constantly pushed around, never settling down. However, if the numbers are too low, the analysis will take too long to complete as the neurons get closer to their ideal places. As a result, it’s best to start with a higher learning rate and a bigger BMU radius before gradually decreasing them.
8. Restricted Boltzmann Machines (RBMs)
What is it?
RBMs are two-layer networks that form the foundation of deep belief networks. It comprises two layers — the visible layer and the hidden layer. The visible layer has input nodes, while the hidden layer is made up of nodes that extracts feature information from the data. The hidden layer’s output is a weighted sum of the input layers. They don’t have any output nodes or normal binary output via which patterns are learnt. The learning process occurs without them. We are simply concerned with the input nodes. Hidden nodes don’t matter. Once the RBM is provided with input, it immediately captures all sequences, attributes/ characteristics, and correlations among the data.
How does it work?
RBM is a Stochastic Neural Network, which means that when triggered, each neuron will exhibit some random behaviour. In RBM, there are two additional layers of bias units (hidden bias and visible bias). On the forward pass, the hidden bias RBM generates activation, whereas the visible bias aids RBM in reconstructing the input on the backward pass. Because there is no connection between the visible units, there is no way of passing information between them. The reconstructed input will always differ from the actual input.
9. Deep Belief Networks (DBNs)
What is it?
A deep belief network (DBN) is a type of generative neural network that produces outcomes using an unsupervised machine learning model. This type of network exemplifies some of the current work in developing unsupervised models utilising relatively unlabeled input.
How does it work?
As DBN is a stack Restricted Boltzmann Machines (RBM), its working is similar to RBM. A deep belief net can be thought of as a collection of basic learning modules, each of which is a form of Boltzmann machine with a visible layer that represents the data and a hidden layer that learns to represent features that capture higher-order correlations in the data. A matrix of equally-weighted connections connects the two layers, and there are no connections between layers. The hidden units are all conditionally independent, making it straightforward to sample a vector given a vector of activities for the visible units. It is simple to obtain a learning signal by starting with an observed data vector on the visible units and alternating between sampling numerous times. The difference between the pairwise correlations of the visible and hidden units at the start and end of the sample is this signal.
10. Auto-encoders
What is it?
An autoencoder is also known as a replicator neural network since it duplicates data from the input to the output unsupervised. The output layer has the same dimensionality as the input layer. In comparison to the input and output layers, the middle layers of the neural network have fewer units. As a result, the reduced representation of the input is stored in the middle layers. The reduced representation of the input is used to recreate the output.
How does it work?
An autoencoder is made up of three parts:
● Encoder: An encoder is a fully connected, FFN network that compresses and encodes the input image into a reduced representation.
● Code: The code then stores the reduced representation of the input. This will be supplied to the decoder.
● Decoder: Like the encoder, the decoder is a feedforward network (FFN). Its structure is identical to that of the encoder. The decoder is in charge of reassembling the input from the code to its original dimensions.
Concluding words
Deep learning is a challenging domain because it makes hard things easy. Deep learning offers a large number of optimization techniques for dealing with complicated problems and has a lot of potential for developing practical applications, all of these features make it lucrative and appealing for many businesses.
