7 Deep Learning Models

Hello! Let us do something today. Open your phone, set up the face unlock feature. Lock it again, and now, show it on your face. How much time does it take to recognize your face? 

 

No! I am not collecting weird datasets like “how much time does it take for a person to unlock their phone using face recognition?” 

 

Nor am I roaming around calculating the average time taken. I am just trying to make you familiar with something deeper that lies in this technology that you use on a daily basis.

 

Deep learning is a part of machine learning that has eased out a lot of complex things for us. In today’s world, it has established itself as one of the most important technological findings in the past few decades, yet it remains on the list of emerging technologies of the past few years. Deep learning works on a few models that are responsible for different kinds of problem-solving.

 

In this blog, we are going to talk about the top deep learning models. Basically, the deep learning algorithms on which deep learning functions. Before we dive in, let us try to know what Deep Learning is.

 

What is Deep Learning?

 

Deep learning is a part of machine learning technique that allows computers to learn by example in the same way that humans do. 

 

Deep learning is a critical component of self-driving automobiles, allowing them to detect a stop sign or discriminate between a pedestrian and a lamppost. It enables voice control in consumer electronics such as phones, tablets, televisions, and hands-free speakers. 

 

Deep learning has gotten a lot of press recently, and with good cause. It's accomplishing accomplishments that were previously unattainable. A computer model learns to execute categorization tasks directly from images, text, or sound in deep learning. 

 

Deep learning models can attain state-of-the-art accuracy, even surpassing human performance in some cases. Models are trained to utilize a huge quantity of labeled data and multilayer neural network topologies.

 

 Now, moving further, let us look at the top-5 deep learning models.

 

Enlisting Deep Learning Models

 

There are two kinds of models in Deep Learning.

 

Supervised Deep Learning Models are Deep learning models that are trained on a particular set of data. It means that they learn from the set outcome of that data. Following are the models that comes under this category;

 

1. Classical Neural Networks

 

Multilayer perceptrons are another name for classic neural networks. Its single character enables it to adapt to fundamental binary patterns via a sequence of inputs, imitating human-brain learning patterns. A multilayer perceptron is a type of neural network that has more than two layers.

 

It is used To format Rows and columns in a tabular dataset (CSV files). In classical Neural networks, the input for classification and regression problems is a set of real values. In order for your model to be more flexible, it needs to be more flexible. Different sorts of data can be used with ANNs.

 

 

2. Convolutional Neural Networks

 

CNNs, also known as ConvNets, are multilayer neural networks that are primarily used for image processing and object detection. In 1988, Yann LeCun created the first CNN, which he called LeNet. It could recognize characters such as ZIP codes and numerals.

 

CNNs were created specifically for picture data and maybe the most efficient and adaptable model for image classification. Despite the fact that CNNs were not designed to deal with non-image input, they can produce remarkable results with it.

 

There are four parts to building the Convolutional Neural Network after you've integrated your input data into the model:

 

• Convolution: the process of creating feature maps from our input data. The maps are then filtered using a function.
• Max-Pooling: This feature allows our CNN to detect a picture that has been modified.
• Flattening: Flatten the data into an array so that CNN can read it.
• Full Connection: The hidden layer that also generates our model's loss function.

 

 

3. Recurrent Neural Networks (RNNs)

 

The outputs from the LSTM can be given as inputs to the current phase since RNNs contain connections that create directed cycles.

 

The LSTM's output becomes an input to the current phase, and its internal memory allows it to remember prior inputs. Image captioning, time-series analysis, natural-language processing, handwriting identification, and machine translations are all common uses for RNNs.

RNNs is used in: 

 

• A single input is mapped to a single output in a one-to-one mapping. Image Classification, for example.

• A single input is mapped to a series of outputs in a one-to-many relationship. Image captioning, for example (multiple words from a single image)

• One to many: A single output is produced by a series of inputs. Sentiment Analysis, for example (binary output from multiple words)

• Many to many: A set of inputs results in a set of outputs. For instance, consider video classification (splitting the video into frames and labeling each frame separately) (Source)

 

 

4. Transformer Networks

 

In 2017, the Transformer networks were introduced as deep learning models. Transformer networks are most commonly employed in natural language processing (NLP).

 

RNNs' computational complexity and slowness prompted the development of transformers. Transformers can handle tasks like machine translation, time series prediction, and text summarization that need sequential data.

 

The fundamental advantage of transformers is that, unlike RNNs, they do not require sequential data to be processed sequentially. The Transformer does not need to handle the earlier dates before the later dates if the input data contains sales numbers in a time-series. 

 

As a result, the Transformers allow for significantly more parallelization than RNNs, resulting in significantly shorter training periods.

 

Unsupervised deep learning models are the ones that are not pre-trained. That means they are applied to a data set from which they can’t learn as explained by towardsdatascience.

 

 

5. Self Organizing Maps(SOMs)

 

Professor Teuvo Kohonen devised SOMs, which enable data visualization by using self-organizing artificial neural networks to minimize the dimensions of data.

 

The problem of humans being unable to visualize high-dimensional data is addressed through data visualization. SOMs are designed to assist people in comprehending this multi-dimensional data.

 

It is used:

 

• When the data provided lacks an output or a Y column.

• Exploration studies to gain a better understanding of the framework that underpins a dataset.

• AI-created creative creations (music, text, and video).

• For feature detection, dimensionality reduction is used.

 

 

6. Boltzmann Machines 

 

There is one feature that all the models have in common. These models work in a specific way. Despite the fact that SOMs are unsupervised, they work in the same direction as supervised models. Input Hidden Layer Output is what I mean by direction.

 

Boltzmann machines do not follow a specific path. In a circular hyperspace, all nodes are connected to one another. Instead of working with fixed input parameters, a Boltzmann machine can create all of the model's parameters.

 

This type of model is known as stochastic, and it differs from the deterministic models mentioned above. Boltzmann Machines with restrictions are more practical.

 

It is used:

 

• When keeping an eye on a system 

• The development of a binary recommendation system

• When working with a small quantity of data, it's important to be as particular as possible.

 

 

7. Autoencoders

 

Autoencoders are neural network designs made up of two sub-networks, encoder and decoder networks, that are linked by a latent space. 

 

In the 1980s, Geoffrey Hinton, one of the most respected scientists in the AI world, and the PDP group produced autoencoders for the first time. 

 

Hinton and the PDP Group set out to solve the problem of "backpropagation without a teacher," often known as unsupervised learning, by treating the input as the teacher. To put it another way, they employed feature data as both a feature and a label.

 

An encoder network, which takes the feature input and encodes it to fit into the latent space, and a decoder network make up an autoencoder. This encoded data (i.e. code) is used by the decoder to turn it back to feature data. 

 

The model in an encoder learns how to efficiently encode the data so that the decoder can convert it back to the original.

 

In the end, deep learning has evolved a lot in the past few years. People have started to notice the technological changes caused by it. AI and ML parameters have developed accordingly and many students nowadays want to pursue a career in the same.