Introduction to Deep Learning

Artificial neural networks are used in deep learning to process massive amounts of data and carry out complex calculations. This particular kind of machine learning is predicated on the composition and operations of the human brain. 

Machines are trained by deep learning algorithms through example-based learning. Deep learning is frequently used in sectors like healthcare, e-commerce, entertainment, and advertising. Deep learning models are the giants of the artificial intelligence sea, transforming the way robots see, comprehend, and learn from data. These models, which draw inspiration from the intricate neural networks seen in the human brain, have accelerated progress across a range of fields, including natural language processing and picture identification. 

Introduction to Neural Networks

Artificial neurons, sometimes referred to as nodes, make up a neural network, which is organized similarly to the human brain. Three levels of these nodes are stacked next to one another:

• The layer of input 
• The layer or layers hidden
• The layer of output

Each node receives information from data in the form of inputs. The node computes the inputs, multiplies them by random weights, and adds a bias. Lastly, to choose which neuron to fire, nonlinear functions—also referred to as activation functions—are used.

There is only one hidden layer visible in the figure above, which is what is known as an artificial neural network or neural network. However, deep neural networks—as implied by their several hidden layers—are what give them their moniker. These interconnected hidden layers are what teach our model how to get the desired result.

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Working of Deep Learning Model

Deep learning techniques rely on artificial neural networks (ANNs) to mimic the way the brain processes information, even if they include self-learning representations. Algorithms use unknown factors in the input distribution to group objects, extract features, and find meaningful data patterns during training. This happens on several levels, employing the algorithms to create the models, much like when robots are trained for self-learning.

Multiple algorithms are used by deep learning models. Even while no network is flawless, some algorithms function better for particular jobs than others. It’s helpful to have a firm grasp of each major algorithm in order to make the appropriate decisions.

Top Popular Deep Learning Algorithms

1. Multilayer Perceptrons (MLPs)

Introduction

One of the earliest deep learning methods and the most fundamental deep learning algorithm is MLP. We advise you to begin with MLP if you are new to deep learning and have only recently begun to investigate it. One way to think of MLPs is as a type of feedforward neural network. 

Example of MLP:

Credits: Link

Working of MLP

• The input layer, which is the first layer, receives the inputs, while the hidden layers are used by the final layer to produce the output. It is called a feedforward network because every node is connected to every other node on the following layer, allowing information to continuously flow forward across the levels.
• Backpropagation is a popular supervised learning technique used in MLP training.
• We feed weights (randomly assigned values) to each buried layer. The activation function receives the input and weights together, and it uses this information to determine the output before passing it on to the subsequent layer. We compute the loss (error) and go back to change the weights if we don’t get the desired result. Trial and error is used to iteratively process the data until the desired result is achieved. The right weights are crucial for training the deep learning model since they will define the output that you get in the end.
• tanh, the Rectified Linear Unit (ReLU), and sigmoid functions are frequently used as activation functions in MLPs.

Applications of MLP

Social media platforms like Facebook and Instagram utilize it to compress image data. That makes a big difference in how quickly the photos load, especially with weak network connections.

Additional uses include data compression, picture and speech recognition, and problem solving via classification.

Pros of MLP

• Unlike other models based on Probability, they do not make any assumptions about the Probability density functions (PDF).
• Capacity to directly supply the decision function through perceptron training.

Cons of MLP

• The only possible outputs for the perceptrons are 0 and 1, as a result of the hard-limit transfer function. 
•  The MLP network may become caught in a local minimum when updating the layer weights, which can reduce accuracy. 

2. Radial Basis Function Networks (RBFNs)

Introduction

It is based on the Radial Basis Function (RBF) activation function, as the name implies. Using RBFN instead of MLP, this deep learning approach training process takes a little less time. 

Radial basis functions (RBFs) are a unique class of feedforward neural networks that employ them as activation functions. These consist of three layers: input, hidden, and output. Their primary applications are in time-series prediction, regression, and classification.

Working of RBFN

• A three-layer feedforward neural network, comprising an input layer, a hidden layer made up of several RBF nonlinear activation units, and a linear output layer that serves as a summation unit to produce the final output, is a basic example of an RBF type of neural network.
• RBFN determines the network’s structure by trial and error. That’s accomplished in two steps:
  • Using k-means clustering, an unsupervised learning approach, the centers of the hidden layer are found in the first step.
  • The weights using linear regression are established in the following stage. The error is calculated using Mean Squared Error (MSE), and the weights are adjusted to minimize MSE.

Applications of RBFN

Because RBFNs can function with time-series-based data, they are employed in the retail industry to estimate sales prices and assess stock market prices. Additional uses include image recognition, speech recognition, time-series analysis, adaptive equalization, diagnosis in medicine, etc.

Pros of RBFN

• Since backpropagation is not used, the training procedure is quicker than with MLP.
• In contrast to MLP, it is simple to understand the functions of the hidden layer nodes.

Cons of RBFN

• Compared to multilayer perceptrons, categorization in the RBF network is slower, despite the fact that training is completed more quickly. 
• The rationale is that during classification, each node in the hidden layer needs to calculate the RBF function for the input sample vector.
• In contrast to MLP, it is simple to understand the functions of the hidden layer nodes.

Input and output for the RBFN deep learning model is shown above, No matter what surface the objects are on or where they are, we want our model to be able to identify them. 

3. Convolutional Neural Networks (CNN)

Introduction

Rather than supplying our network with full images, CNN breaks down photos into several overlapping tiles as part of its data processing workflow. Next, we apply a method known as a sliding window to the entire original image and save the outcome as a distinct little picture tile. Using a sort of brute force approach, the sliding window finds the object in each potential section by scanning the entire area for the given image. This process is repeated until the desired object is found in each sector.

Working of CNN

There are three basic building blocks of a CNN

• Convolutional layers
• Pooling layers
• Fully-Connected Layers

The most important component of convolutional neural networks is the Convolutional layer. The parameters of each layer use a set of filters, or kernels, which can be thought of as the layers’ neurons. They produce the output with weighted inputs and based on the input size (a fixed square), which is also known as a receptive field.

Feature maps are produced when these filters are applied to the input image. It is one filter’s output that was applied to the layer before it. A particular filter is drawn over the whole prior layer by moving each pixel of the image one at a time. A specific neuron is triggered for every point, and the results are gathered into a feature map. 

We save the outcome of processing each tile into a grid with the same tile layout as the original image in order to preserve the original image’s tile arrangement.

Pooling Layer: The result of the convolution layer is a massive grid array. We utilize the max pooling downsampling approach to minimize the size of this array. Retaining only the most important input tile from the array is the fundamental concept behind utilizing a pooling layer.

Fully connected Layer: Since the array consists entirely of numerical values, we may feed it into a fully connected neural network, in which every neuron is connected. ReLU is the most often used activation function in CNN.

Applications of CNN

CNNs are used by social media platforms including Facebook, Instagram, and others to identify and recognize faces. You are therefore utilizing CNN when attempting to tag a buddy in your post!

Additional uses include image identification, forecasting, natural language processing, video analysis, and more.

Pros of CNN

In comparison to other algorithms, CNN yields more accurate results, especially for use cases involving the recognition of objects or images.

Cons of CNN

CNN training requires a lot of processing power. They are therefore not economical.

4. Recurrent Neural Networks (RNNs)

Introduction

The linked nodes in recurrent neural networks have directed cycles. To achieve the auto-complete feature kind of functionalities, they process the subsequent sequence of inputs using their memory. RNNs are special because they can accept an infinite number of inputs. 

The LSTM outputs can be provided as inputs to the current phase of an RNN thanks to its directed cycle connections. 

Because of its internal memory, the LSTM’s output serves as an input for the current phase and has the ability to remember prior inputs. RNNs are frequently utilized in machine translation, natural language processing, handwriting recognition, time-series analysis, and picture captioning.

Working of RNN

The several steps for every time state of a recurrent neural network are depicted in the above diagram. RNNs use the knowledge they have gathered from their previous inputs in addition to taking into account the weights of the hidden units when determining the output. Because of their internal memory, RNNs are a unique kind of deep neural network that are able to recall such characters. After something is generated, it is duplicated and sent back into the deep neural network in a loop. Because of this, the input may result in a different output depending on earlier inputs in the linked layers. 

Let’s use an example to better grasp this:

Example: Let’s say you have constructed a feedforward network that reads words as input and analyzes each character in the word. When you get to the letter “o” in the term ProjectPro, it will have already forgotten the previous three characters, “P,” “r,” and “o.” 

Applications of RNN

RNN is widely used by Google, Search Engines, and Web Browsers for word and phrase auto-completion. Other uses include video frame analysis and text detection and recognition.

Pros of RNN

A key component of time series prediction is the retention of information by RNN models over the training phase.

Cons of RNN

• Because the computation is recurring, it takes time.
• Long sequences in the training data set are difficult to process, especially when using tanh or ReLU as activation functions.

5. Long Short-Term Memory Networks (LSTMs)

Introduction

Long-term dependencies can be learned very well by LSTMs, a unique type of RNN. Let’s attempt to explain long-term dependency using an illustration.

Assume you have developed a model to anticipate the following word based on the ones that came before. Let’s say you are attempting to guess the final word in the sentence, “the sun rises in the east.” Since there is no more context required, it is evident that the word that comes after will be east. RNNs can learn and anticipate the output with ease in these kinds of situations, when there is little to no gap between the pertinent information and the required location. However, if we were to say something like, “I was born in India. I can speak Hindi rather well. 

Working of LSTM

The several memory blocks, or cells, that make up the LSTM network are the rectangular blocks seen above.  

The cell state, represented by the diagram’s horizontal top line, is crucial to LSTMs. 

First step: LSTM determines which data from the cell state should be retained and which should be discarded. This choice is determined by the sigmoid layer.

Second Step : The tanh and sigmoid functions are crucial in detecting pertinent information. The LSTM determines whether fresh information should be kept and replaces the irrelevant information found in step 1.

Third Step : – The cell state, which is now a filtered version due to the application of the sigmoid and tanh functions, is used to calculate the output.

Applications of LSTM

Text and video analysis, auto-completion, time-series forecasting, anomaly detection in network traffic data or IDSs (intrusion detection systems), and caption creation. 

Pros of LSTM

Compared to traditional RNNs, LSTMs are far more useful for simulating long-range relationships and temporal sequences. 

Cons of LSTM

• The LSTM model must be trained using a lot of compute and resources, and it takes a long time.
• They have a tendency to overfit.

6. Restricted Boltzmann Machines (RBMs)

Introduction

Dimensionality reduction, classification, regression, collaborative filtering, feature learning, and topic modeling are among the applications of this deep learning technique. The fundamental units of DBNs are RBMs.

RBMs are divided into two layers:

• Visible units 
• Hidden units

All hidden units are related to every visible unit. RBMs lack output nodes and have a bias unit that is coupled to every visible and hidden unit.

You’ve probably noticed that YouTube suggests videos based on what you’ve previously viewed. You will also start seeing a ton of recommendations if you have viewed a Netflix web series or movie. They employ an RBM-based method called collaborative filtering.

Working of RBM

The forward pass and backward pass phases comprise RBMs.

• After receiving the inputs, RBMs convert them into a string of numbers that are used to encode the inputs for the forward pass.
• RBMs mix a single overall bias and each input’s unique weight. The output is sent to the hidden layer by the algorithm.
• RBMs translate that set of numbers to create the reconstructed inputs during the backward pass.
• Reconstruction-by-mixing (RBM) combines individual weight and general bias with each activation, then sends the output to the visible layer.
• In order to assess the quality of the output at the visible layer, the RBM compares the reconstruction with the original input.

An illustration of how RBMs work is shown below:

Applications of RBM

The RBM algorithm is used by Netflix, Prime Video, and streaming apps to make suggestions to consumers based on their viewing habits.

Pattern recognition, recommendation engines, classification issues, topic modeling, etc. all use feature extraction.

Pros of RBM

• Because the learning method can effectively handle large amounts of unlabeled data, RBMs can be pre-trained in an entirely unsupervised manner.
• They can encode any distribution and don’t require a lot of processing power.

Cons of RBM

• It can be difficult to calculate the energy gradient function during training.
• The CD-k algorithm makes weight adjustments more difficult than backpropagation.

7. Self Organizing Maps (SOMs)

Introduction

SOMs were developed by Professor Teuvo Kohonen and allow data visualization by using self-organizing artificial neural networks to minimize the dimensions of the data. 

The challenge of high-dimensional data being difficult for people to see is one that data visualization aims to address. SOMs are designed to aid people in comprehending this high-dimensional data.

Working of SOM

• After setting each node’s weights initially, SOMs select a random vector from the training set.
• To determine which weights are the most likely input vector, SOMs look at each node individually. The Best Matching Unit (BMU) is the name given to the winning node.
• SOMs learn about the BMU’s area, and as time goes on, there are less neighbors.
• The sample vector receives a winning weight from SOMs. A node’s weight varies more with proximity to a BMU.
• The neighbor learns less the farther it is from the BMU. For N iterations, SOMs repeat step two.

Applications of SOM

Image analysis, process control, problem detection, and monitoring, etc. Because SOMs can produce strong visuals, they are widely utilized in the healthcare industry to create 3D charts and for 3D modeling of human heads from stereo pictures.

Pros of SOM

• With SOM, interpreting and comprehending the data is simple. 
• Checking for any similarities in our data is made even easier by using dimensionality reduction.

Cons of SOM

• Neuron weights must be both sufficient and necessary for SOM to cluster the input data. 
• We risk not receiving an output that is either very accurate or instructive if we provide SOM much more or less input during training.

8. Generative Adversarial Networks (GANs)

Introduction

It is a method for unsupervised learning that can automatically find and understand patterns in the data. Next, new samples like the original dataset are produced by GANs.

Generative Adversarial Networks (GANs) are deep learning algorithms that generate new instances of data by mimicking the training set. The two halves of a GAN are a discriminator that gains knowledge from the erroneous data and a generator that learns to create fake data.

Over time, there has been a growth in the use of GANs. They can be applied to dark-matter research to replicate gravitational lensing and enhance astronomy photos. Using image training, video game creators can recreate low-resolution, 2D graphics in older games in 4K or higher resolutions by using GANs.

Working of GAN

• The generator’s phony data and the actual sample data are distinguished from one another by the discriminator.
• The generator creates fictitious data during the first training, and the discriminator soon picks up on the fact that it’s not real.
• To update the model, the GAN transmits the findings to the discriminator and generator.

Demonstration of how GAN work:

Applications of GAN

In the game business, GANs are commonly utilized for 3D object generation. They are also employed in the creation of cartoon characters and image editing.

They are also employed as novel and article illustrations.

Pros of GAN

• The internal representation of any data, even complex and convoluted distributions, can be learned by GANs. With unlabeled data, they can be trained efficiently to generate high-quality, realistic outputs fast.
• They are able to measure the separation between items in addition to recognizing them.

Cons of GAN

• Since they create new data from the old data, there is no evaluation metric to determine the output accuracy.
• High processing and training time required for the model.

9. Autoencoders

Introduction

Autoencoders are unsupervised algorithms that bear a striking resemblance to machine learning’s Principal Component Analysis (PCA). Multi-dimensional data is transformed into low-dimensional data using them. We can also rebuild the original data if that is what we desire.

As an illustration, let’s say a friend has asked you to share some software that you have stored on your computer. That software has a folder size of almost one gigabyte. However, if you compress it, the file size will go down and uploading will be simple. This folder can be downloaded immediately by your friend, who can then extract the files and obtain the original folder.

Working of Autoencoders

Autoencoders are composed of three primary parts: 

• Encoder: The encoder takes the input and compresses it into a latent space representation that may be later on rebuilt to obtain the original input.
• Code: After encoding, this is the compressed portion (also known as the latent space representation).
• Decoder: The goal of the decoder is to restore the code to its initial state. The resulting reconstruction output can have some loss and not be as accurate as the original.

Applications ofAutoencoders

Picture coloring, denoising, and compression, among other techniques.

They are employed in the medical field for medical imaging, which is the method and procedure of imaging the inside of the human body in order to do clinical analysis.  like the discovery of breast cancer.

Pros of Autoencoders

Some functions can have their computing cost reduced by using several encoder and decoder layers.

Cons of Autoencoders

• When it comes to reconstructing images, it is not as effective as GANs and typically performs poorly with complicated images.
• After encoding, we could lose important information from our original input.

10.  Deep Belief Networks

Introduction

Generative models called DBNs are made up of several layers of latent, stochastic variables. Latent variables, often known as hidden units, are binary variables.

Each RBM layer communicates with both the layer before it and the one after it, and DBNs are a stack of Boltzmann Machines with connections between the layers. Motion capture, video, and picture identification are all handled by Deep Belief Networks (DBNs). 

Working of Deep Belief Networks

• The Greedy algorithm is used to pre-train DBNs. They learn all of the generating weights and top-down techniques layer by layer. 
• On the top two hidden layers of the network, many stages of Gibbs sampling are performed (to generate an approximation sequence of observations from a specified multivariate probability distribution when direct sampling is not feasible). Taking a sample from the RBM that is defined by the top two hidden layers is the idea.
• Next, we extract a sample from the visible units by doing a single ancestral sampling pass through the remaining parts of the model.
• We can learn the values of the latent variables in each layer in a single, bottom-up pass. 
• In the lowest layer, an observed data vector serves as the basis for the Greedy pre-training. Then, with the aid of fine-tuning, it employs the generating weights in the opposite direction.

Applications of Deep Belief Networks

Anime characters are created with Variational Autoencoders (VAEs), a kind of autoencoder employed in the gaming and entertainment sectors.

 to identify, classify, and produce motion-captured photos, video clips, and photographs. 

Pros of Deep Belief Networks

• Even a small labeled dataset can be used by them.
• DBNs offer strong categorization performance. (angle of view, size, placement, color, etc.)

Cons of Deep Belief Networks

It takes a lot of hardware to process inputs.

Conclusion

The development of deep learning models has opened up previously unimaginable possibilities and expanded the realm of what is possible for robots to achieve. Every model, from the ground-breaking Transformers to the iconic FNNs, brings special advantages to the table that allow for advancements in a range of applications.

Deep learning models are constantly being improved upon, offering us hope for a time when machines will be able to perceive, reason, and create with a level of sophistication never seen before as we traverse the neural seas. The field of artificial intelligence is still evolving as a result of the waves of deep learning advancements.