Top 10 Deep Learning Algorithms You Should Know in 2023

Deep learning has become one of the most popular technologies in recent years. With the rise of AI, it has been applied to many fields including computer vision, natural language processing, speech recognition, and robotics. In fact, deep learning is now being used in almost every industry where humans make decisions based on data.

The term deep learning refers to a class of machine learning techniques that involve training computers to learn without explicitly programmed instructions. These methods rely on large amounts of data to train models, and require significant computational power.

In this article, we take a look at some of the most important deep learning algorithms and how they work. We’ll cover what makes each algorithm special, and why you might want to know about them.

What is Deep Learning?

Deep learning is one of the most exciting developments in modern computing. This technology allows computers to learn how to complete tasks without being explicitly programmed. In fact, it does not even require a computer. Instead, it harnesses the power of millions of people’s brains to teach itself what it needs to know.

The technology is part of AI, which stands for Artificial Intelligence. While AI has been around since the 1950s, deep learning is just now becoming widely used. There are many reasons why this is happening. For example, mobile devices like smartphones and tablets have become ubiquitous. They contain sensors that collect information about everything we do. This opens up opportunities to build apps that understand our behavior and adapt accordingly.

How Deep Learning Algorithms Work?

Deep learning algorithms feature self-learned representations. They learn how to represent things without being explicitly told what those things are. This happens during the training phase, where the algorithm learns to recognize patterns in data.

During the training process, algorithms rely on unknown elements in the input distributions to extract features, group similar items together, and discover useful data structures. These steps occur at multiple levels, using different algorithms to build each model.

While no single neural network is considered perfect, certain algorithms are better suited to performing specific tasks. For example, convolutional networks excel at recognizing images, while recurrent neural networks work well with sequential data such as text.

To choose the best algorithm for a given task, it’s important to understand the strengths and weaknesses of each one. Here’s a look at the three most common types of deep learning networks:

• Convolutional Neural Networks (CNN): CNNs are used to identify objects in images. They’re great at finding edges and shapes, because they’re designed to see patterns in 2D space. They’re also great at identifying faces, since facial features tend to follow predictable patterns across photos.

• Recurrent Neural Networks (RNN): RNNs are often employed to analyze sequences of numbers or words. For example, you could train an RNN to predict the next word in a sentence. Because they’re able to examine previous states, they’re able to determine relationships among different parts of speech.

• Long Short Term Memory (LSTM): LSTMs are often used to classify large amounts of data into categories. They’re especially effective at classifying video clips, because they can track changes over long periods of time.

Defining Neural Networks

Neural networks are a type of AI model used to process data. They work similarly to how our brains process information. Each neuron receives data via multiple connections called synapses. This data is processed and stored in memory cells called neurons. Neurons connect to each other via synapses and pass along signals. In turn, the signal is passed on to another neuron. This continues until it reaches the output layer where it is interpreted. To train a neural network, you must provide it with training data. You do this by feeding it examples of what you want the system to learn. Once trained, the neural network can perform tasks such as facial recognition, speech recognition, image classification, object detection, etc.

Source: simplilearn.com

Types of Algorithms used in Deep Learning

Convolutional Neural Networks (CNNs)

Convolutional neural networks (CNNs), also known as convolutional neural nets, are deep learning models inspired by neurons in the human brain. They use artificial neural networks (ANNs) to recognize patterns within data. A CNN consists of multiple layers; each layer performs a different function. The final output of the network is a single number representing how confident it is about the classification.

The first CNN was invented in 1988 by Geoffrey Hinton, Yann LeCun, and Yoshua Bengio. They named it “LeNet”, short for LeNet5. It was used for character recognition, specifically digit recognition. In 1992, Alex Krizhevsky improved upon LeNet5 and introduced the concept of pooling layers. This allowed CNNs to perform better on larger datasets.

In 1998, Andrew Ng published his PhD thesis, titled “Learning Transferable Architectures”. He demonstrated that CNNs could learn features that generalize well across tasks. These features were later used to train very large scale neural networks.

Yann Lecun developed the first CNN, LeNet5, in 1988. It was used for recognizing character strings such as ZIP codes and digits. In 1990, he showed that CNNs could classify MNIST handwritten digit images. In 1994, he trained a CNN to play Atari games. In 1995, he trained a CNN for document categorization. In 1997, he trained a CNN on natural scene images. In 1999, he trained a CNN based on a database of facial expressions.

CNNs are complex algorithms that use neural networks to analyze massive amounts of information. They have multiple layers that process input data, extract features, and make predictions.

The CNN architecture consists of three main parts: convolutional layers, pooling layers, and fully connected layers. Convolutional layers perform feature extraction while pooling layers reduce the size of images. Fully connected layers connect each neuron in one layer to every neuron in another layer to build up a model.

CNN uses a convolutional neural network (CNN) to detect objects in images. A CNN consists of three main layers: input, convolutional, and pooling. In the case of object detection, there are five types of inputs: RGB image, grayscale image, depth map, optical flow, and semantic segmentation labels. Each type of input produces different outputs. For example, the RGB image produces feature maps that contain the pixel intensities of each color channel. Grayscale images produce feature maps containing the intensity values of each gray scale value. Depth maps output feature maps that represent the distance from the camera to the pixels. Optical flow outputs feature maps representing how much movement occurs within the image. Semantic segmentations label the pixels into one of many categories such as cars, people, etc. These labels allow CNNs to learn about specific objects.

The convolutional layer processes the raw data coming in from the input layer. This layer creates filters that look at small regions of the image. Filters are applied to the raw data, and the filter outputs are added together to form a feature map. The number of filters determines the size of the receptive field. If you increase the number of filters, you can see larger areas of the image. However, increasing the number of filters increases the amount of computation required. Therefore, it is important to use enough filters to capture the features of interest while keeping the computational requirements low.

The next step is called max pooling. Max pooling reduces the size of the feature maps without losing information. By reducing the size of the feature map, we reduce the number of parameters needed to train the model. We do this because our goal is to classify the entire image rather than just identify individual objects. After the pooling layer, we apply another set of filters. Then, we add up the filtered outputs again. Finally, we apply dropout to prevent overfitting. Dropout randomly sets some neurons to zero during training. This prevents the model from memorizing the training data. When testing, dropout allows us to generalize better.

CNN’s are one of the most popular neural networks used today because of how well they work with images. They’re particularly good at recognizing objects, like faces, letters, and digits. But there’s a problem: many models require a lot of data to train properly. This requires massive amounts of computing power and storage space. To solve this issue, researchers developed a way to use convolutional layers without requiring large datasets.

The idea behind it is simple: instead of having multiple filters, each with a small receptive field, you can stack several smaller ones together. Each filter performs a different operation on the image, such as detecting edges, corners, or colors. By stacking up several filters, you can make predictions about what’s happening in the image even though you don’t know exactly where something is.

This method works great, but it doesn’t always produce accurate results. For example, suppose you want to recognize whether an object is a face or a flower. If you feed the network a picture of a rose, it might mistake it for a face. So the researchers added another step called a Rectified Linear Unit (ReLu). Instead of just adding the input signal, the ReLu adds some random noise to it. If the input is negative, the ReLu makes the output positive; otherwise, it keeps it unchanged. This forces the model to learn what features are important, rather than memorizing individual examples.

The rectified feature map next goes through a pooling layer. This step downsamples the feature map by reducing the number of channels. In our case, we are downsampling from 64 to 16 channels.

Next, the feature map is flattened into a 2d array. Flattening the feature map makes it easier to feed into a neural network.

Fully connected layers form when the flattened matrix from pooling layer is fed into a neural network. We use a fully connected layer because it allows us to classify and identify the images.

Below is an example of an Image processed via CNN.

Source: simplilearn.com

Long Short Term Memory Networks (LSTMs)

Recurrent neural networks (RNNs) are powerful tools for analyzing sequences of data. In particular, RNNs excel at tasks like language translation, handwriting recognition, and machine translation. However, because traditional RNNs don’t keep track of what happened in the past, it’s difficult to predict future events.

Long short term memory networks (LSTMs) overcome this problem by adding memory cells to the network architecture. These cells allow the model to store important information about the sequence without having to rerun the entire process every time. This makes LSTMs ideal for applications such as text generation, speech processing, and natural language understanding.

LSTM stands for Long Short Term Memory. This type of neural network is used to process sequential data like speech recognition. An LSTM consists of three different types of cells called input, hidden, and output. Each of these cells are responsible for processing information differently. Input cells take in data and pass it along to the next layer. Hidden cells store the processed information and give it back to the output cells. Output cells produce a final value based on the inputs given to the model.

To understand how LSTMs work, let’s look at the following example:

The above diagram illustrates how one cell works. Let’s say we want to recognize whether a word is positive or negative. We feed the words into our model and predict whether the word is positive or negative based on the outputs of the hidden cell. If the prediction is correct, the output cell gives us a score indicating how confident we are about the decision.

Let’s now consider another example where we want to classify text documents into categories. We again feed the documents into the model and predict what category each document belongs to. In this case, however, the input cell does not just receive the raw text; rather, it receives the entire document. As such, it must remember everything in the document while making predictions about the future.

In both examples, the input cell takes in data and passes it forward to the next layer. However, the input cell remembers some of the data it received and uses that data to make predictions about the future. For instance, in the second example, the input cell remembers the words in the document and predicts whether those words belong together.

Next, the hidden cell stores the processed information and gives it back to the output cell. The output cell produces a final value based on inputs given to the model, including the inputs from the input cell and the hidden cell.

Source: simplilearn.com

Recurrent Neural Networks (RNNs)

Recurrent neural networks (RNNs) have connections that form directed cyclic paths, which allows the outputs from the LTM to be fed as inputs into the next phase. This process repeats itself over and over again, creating a directed cycle where information flows in both directions. In addition to being able to remember previous inputs, RNNs also have internal memories that store information about what happened during previous phases.

In practice, it’s common to use a single hidden layer, and each neuron receives inputs from every node in the preceding layer. However, you could also make multiple layers, where some neurons receive input from nodes in one layer while others receive input from nodes in another layer.

Source: simplilearn.com

The output at time t-2 feeds into the input at t-1, and the output at time t-3 feeds into the input at the same time. This continues for each subsequent step. This allows the network to learn from previous steps without having to wait for the entire input to come in.

RNNs are often used in natural language processing applications like speech recognition, where it is necessary to understand what word came before the current one. In fact, you might already be familiar with this type of technology. If you use Gmail, you’ve probably noticed that the suggestions given to you while composing an email change based on the words you typed before. You see “people I know,” “people I’m following,” and even “people I’m writing about.”

This is because Gmail uses an RNN that takes the words that you typed and predicts what word will come next. For instance, if you’re typing “I want to go to the beach,” the system knows that “beach” is likely to follow “go.” So, it suggests “people I know,” since you’re most likely looking for people you know.

Generative Adversarial Networks (GANs)

Generative adversarial networks (GANs) are artificial intelligence (AI) models that learn how to generate new data instances that resemble real data. This type of AI model uses two neural network layers: a generator and a discriminator. A generator learns to produce fake data, while a discriminator learns to distinguish between the generated data and the real data.

The usage of GANs in computer vision applications has increased over a period time. Examples include generating high resolution images, simulating gravitational lensing for dark matter research, improving astronomical images, creating photo-realistic portraits of humans, and rendering 3D objects.

Video game developers use GANsto upscale low-resolution,2D textures in old videogames by recreating them in higher resolutions via image training; GANs can also be used to generate low-quality textures in low-end graphics cards.

GANs are generative adversarial networks. They’re very similar to deep learning neural nets. A neural net takes input and processes it into some output. In GANs, there are two models; one called the “generator,” and another called the “discriminator.” Both models take inputs and produce outputs. However, while the generator tries to make something up, the discriminator looks to see whether what it generates is actually real or fake. If it thinks it’s fake, it tells the generator, and the cycle continues. This process is repeated over and over again until both the generator and the discriminators learn to generate realistic samples.

Source: simplilearn.com

Radial Basis Function Networks (RBFNs)

Radial basis function (RBF) networks are one type of feedforward neural network (FFNN). These models are often used for pattern recognition problems such as image processing, speech recognition, and natural language processing. In addition to being able to learn complex patterns, FFNNs are extremely fast because they don’t require matrix multiplication. This makes them ideal for large data sets. However, most commonly used activation functions like sigmoid or tanh aren’t suitable for training FFNNs. Therefore, researchers developed alternative activation functions like Gaussian, hyperbolic tangent, and exponential linear units. One of those alternatives is RBFs.

Radial basis function neural networks (RBFNs) are a type of artificial neural network (ANN). ANNs are used to classify data based on similarities to known patterns. For example, they could be used to identify whether a person is male or female based on facial features. An RBFN uses radial basis functions (RBFs), which are mathematical functions that model how points are distributed around a central point. In an RBFN, each neuron represents a different feature of the data. Each neuron receives a weighting value from the previous layer, along with the input data. The weights determine the importance of each feature. Then, the neuron applies the RBF to the weighted values to produce a numerical output. This process repeats across multiple layers until the final output is produced.

See this example of an RBFN:

Source: simplilearn.com

Multilayer Perceptrons (MLPs)

Multilayer perceptron (MLP) models are one type of artificial neural network. A multilayer perceptron consists of an input layer, one or more hidden layers, and an output layer. In contrast to recurrent neural networks, where each neuron maintains connections to every neuron in the previous layer, there are no such connections in an MLP. Each neuron receives inputs from neurons in the preceding layer and applies an activation function to produce an output value. These values are passed to the next layer of neurons, which do likewise. This process continues until the final layer produces the desired output.

MLPs are useful because they allow us to build models that can handle complex problems. They work by building a neural network out of multiple layers of artificial neurons. Each neuron receives inputs from the previous layer and produces outputs based on those inputs. In addition, each neuron has some weight associated with it. These weights can change during the course of the learning process.

The way MLPs work is similar to how we think about our brains. We know that neurons communicate with one another via synapses. When a neuron fires, it causes nearby neurons to become activated. This type of communication allows us to solve complicated tasks such as recognizing objects. As you might guess, MLPs do the same thing. By changing the values of the weights, the MLP learns how to make predictions about future events.

Source: simplilearn.com

Self Organizing Maps (SOMs)

Professor Teuvo Kohonen developed the concept of self organizing maps (SOMs). He applied it to machine learning in the 1980s. In his research he used the idea of creating artificial neural networks that could learn and organize themselves. This led him to develop a method called Kohonen’s self-organized map, which is now widely known as SOM.

Data visualization attempts to solve the problems that humans cannot easily visualize large amounts of data. For example, you might want to know what happened during the 2016 election campaign. You can look up each candidate’s name and see how many votes they received, but that doesn’t tell you much about why people voted for one candidate over another. A data visualization tool like Tableau can show you different kinds of metrics, such as the number of voters who supported Donald Trump compared to Hillary Clinton. However, the best way to really understand the data is to use some form of visual representation.

The SOM algorithm can be used to train neural networks. In this case, it is used to train a single layer feedforward network. A single neuron is trained to recognize patterns in the training set. Once the network is trained, it is applied to unseen inputs. This process is repeated many times to produce a model that can classify unseen inputs.

Neural networks use the concept of neurons. Neurons are computational units that perform simple operations such as addition, multiplication, and comparison. They receive inputs from other neurons via connections known as synapses. Each connection represents a relationship between two neurons. When a neuron receives enough inputs, it fires. If the firing pattern matches the pattern stored in memory, the output value increases.

A SOM consists of a collection of nodes arranged into a grid. Every node contains a weight vector, which stores the strength of the relationship between the node and each of the inputs. Nodes are organized into clusters based on similarities among their vectors. The number of nodes determines how similar the clusters are. As the clustering progresses, some nodes begin to cluster together while others drift apart. Eventually, one node emerges as the best match for a given input. The weight vector associated with this node becomes the output of the SOM.

Source: simplilearn.com

Deep Belief Networks (DBNs)

Deep belief networks (DBNs) are generative models that consists of multiple layers of stoichastic, latent variables. The latent variables have binary value and are often called hidden unit. DBNs are a Stack of Boltzman Machine with connections between the layers and each RBM layer communicate with both the Previous and Subsequent layers. DBNs are used for Image Recognition, Video Recognition, Motion Capture Data.

Deep Belief Networks are a type of deep neural network used for unsupervised learning. They consist of three components: an input layer, hidden layers, and an output layer. A Deep Belief Network learns by propagating information up and down the layers. In the case of the DBN shown here, it starts with an input vector x1 and passes it through a stack of hidden layers. Each layer computes a probability distribution over the next layer’s outputs. At each step, the current state of the network is passed along to the next layer, and the process continues until the final layer produces the desired output.

Below is an example of DBN architecture:

Source: simplilearn.com

Restricted Boltzmann Machines( RBMs)

A restricted Boltzmann machine (RBM) is a type of artificial neural network (ANN). These models are stochastic neural nets that can learn from data via unsupervised training. They are composed of visible units and hidden units. A RBM consists of three layers:

1. Input layer

2. Hidden layer

3. Output layer

The input layer receives external information, while the output layer provides feedback to the model. The hidden layer contains neurons that are randomly wired together. Each neuron represents one unit in the visible layer. The weights connecting each neuron in the visible layer to the corresponding neuron in the hidden layer form the connections matrix W.

In contrast to fully connected ANNs, where every neuron in the hidden layer connects to every neuron in the visible layer, the connections among neurons in the hidden layer in an RBM are sparse. In addition, the connection strengths between the visible and hidden layers are constrained to obey certain constraints. For example, the sum of the weights connecting each neuron in a given row of the visible layer to the same column in the hidden layer must equal zero.

Recurrent neural networks (RNNs) are powerful tools for machine learning because they allow you to build models capable of processing sequential data like text or speech. They do this by creating a representation of the entire sequence rather than just looking at it piecemeal. However, RNNs require a lot of memory to store all the information about the whole sequence. This makes them difficult to train and hard to scale up.

The Recurrent Neural Network (RBM) is a type of artificial neural network that solves both problems. Instead of storing all the information in a single vector, it stores it in multiple vectors called visible units and hidden units. These units are arranged in layers where the visible units are closest to the input and the hidden units are furthest away from the input.

In the forward pass, the RBM takes the input and computes the probability distribution over the visible units. Then, in the backward pass, it uses those probabilities to compute the likelihood of every possible combination of values in the hidden units. As such, it creates a compressed representation of the input that requires much less memory than traditional RNNs.

Below is a diagram of how RBMs function:

Source: simplilearn.com

Autoencoders

Autoencoders are a special type of feedforward artificial neural network that was developed in the 1980s by Geoffrey Hinton. He wanted to use them to help train computers to recognize patterns in large datasets. These models consist of an encoder, which takes in the original data and transforms it into a compressed representation, and a decoder, which reconstructs the original data from the encoded version. In the 1990s, researchers found that autoencoders could be used to perform unsupervised machine learning tasks.

An autoencoder is a type of artificial intelligence model used to compress data. It takes information and reduces it down into a smaller form. This process is called encoding, while the reverse process is decoding. In this case, the autoencoding neural networks learn how to compress images into a smaller, more compact version. Then, the encoded image is fed to another neural network, which attempts to recreate the original image. If successful, the decoded image is nearly identical to the original image.

Source: simplilearn.com

Deep learning has evolved over the last five years, and deep neural networks have become one of the most popular tools used by companies across the world. In fact, it is predicted that by 2020, there will be about 30 billion devices connected to the internet, and almost half of those will be smart speakers. This brings us to the question… what does this mean for businesses?

The answer is simple – deep learning algorithms will play a significant role in powering the next generation of intelligent machines. As such, we believe that now is the best time to start preparing yourself for a career in data science.

If you are looking to get started with machine learning and artificial intelligence, the following courses could help you kickstart your journey towards becoming a data scientist.