Machine Learning and AI have changed the world around us for the last few years with its breakthrough innovation. Furthermore, it is the various deep learning techniques that take Machine Learning to a whole new level where machines can learn to discern tasks, inspired by the human brain’s neural network. It is the reason why we have voice control on our smartphones and TV remotes.
There are different types of deep learning models that are both accurate and effectively tackle problems that are too complex for the human brain. Here’s how:
Top 10 Deep Learning Techniques
1. Classic Neural Networks
Also known as Fully Connected Neural Networks, it is often identified by its multilayer perceptrons, where the neurons are connected to the continuous layer. It was designed by Fran Rosenblatt, an American psychologist, in 1958. It involves the adaptation of the model into fundamental binary data inputs. There are three functions included in this model: they are:
- Linear function: Rightly termed, it represents a single line which multiplies its inputs with a constant multiplier.
- Non-Linear function: It is further divided into three subsets:
- Sigmoid Curve: It is a function interpreted as an S-shaped curve with its range from 0 to 1.
- Hyperbolic tangent (tanh) refers to the S-shaped curve having a range of -1 to 1.
- Rectified Linear Unit (ReLU): It is a single-point function that yields 0 when the input value is lesser than the set value and yields the linear multiple if the input is given is higher than the set value.
Works Best in:
- Any table dataset which has rows and columns formatted in CSV
- Classification and Regression issues with the input of real values
- Any model with the highest flexibility, like that of ANNS
2. Convolutional Neural Networks
CNN is an advanced and high-potential type of the classic artificial neural network model. It is built for tackling higher complexity, preprocessing, and data compilation. It takes reference from the order of arrangement of neurons present in the visual cortex of an animal brain.
The CNNs can be considered as one of the most efficiently flexible models for specializing in image as well as non-image data. These have four different organizations:
- It is made up of a single input layer, which generally is a two-dimensional arrangement of neurons for analyzing primary image data, which is similar to that of photo pixels.
- Some CNNs also consist of a single-dimensional output layer of neurons that processes images on their inputs, via the scattered connected convolutional layers.
- The CNNs also have the presence of a third layer known as the sampling layer to limit the number of neurons involved in the corresponding network layers.
- Overall, CNNs have single or multiple connected layers that connect the sampling to output layers.
This network model can help derive relevant image data in the form of smaller units or chunks. The neurons present in the convolution layers are accountable for the cluster of neurons in the previous layer.
Once the input data is imported into the convolutional model, there are four stages involved in building the CNN:
- Convolution: The process derives feature maps from input data, followed by a function applied to these maps.
- Max-Pooling: It helps CNN to detect an image based on given modifications.
- Flattening: In this stage, the data generated is then flattened for a CNN to analyze.
- Full Connection: It is often described as a hidden layer that compiles the loss function for a model.
The CNNs are adequate for tasks, including image recognition, image analyzing, image segmentation, video analysis, and natural language processing. However, there can be other scenarios where CNN networks can prove to be useful like:
- Image datasets containing OCR document analysis
- Any two-dimensional input data which can be further transformed to one-dimensional for quicker analysis
- The model needs to be involved in its architecture to yield output.
Read more: Convulational neural network
3. Recurrent Neural Networks (RNNs)
The RNNs were first designed to help predict sequences, for example, the Long Short-Term Memory (LSTM) algorithm is known for its multiple functionalities. Such networks work entirely on data sequences of the variable input length.
The RNN puts the knowledge gained from its previous state as an input value for the current prediction. Therefore, it can help in achieving short-term memory in a network, leading to the effective management of stock price changes, or other time-based data systems.
As mentioned earlier, there are two overall types of RNN designs that help in analyzing problems. They are:
- LSTMs: Useful in the prediction of data in time sequences, using memory. It has three gates: Input, Output, and Forget.
- Gated RNNs: Also useful in data prediction of time sequences via memory. It has two gates— Update and Reset.
Works Best in:
- One to One: A single input connected to a single output, like Image classification.
- One to many: A single input linked to output sequences, like Image captioning that includes several words from a single image.
- Many to One: Series of inputs generating single output, like Sentiment Analysis.
- Many to many: Series of inputs yielding series of outputs, like video classification.
It is also widely used in language translation, conversation modeling, and more.
Learn more about: Recurrent Neural Network
4. Generative Adversarial Networks
It is a combination of two deep learning techniques of neural networks – a Generator and a Discriminator. While the Generator Network yields artificial data, the Discriminator helps in discerning between a real and a false data.
Both of the networks are competitive, as the Generator keeps producing artificial data identical to real data – and the Discriminator continuously detecting real and unreal data. In a scenario where there’s a requirement to create an image library, the Generator network would produce simulated data to the authentic images. It would then generate a deconvolution neural network.
It would then be followed by an Image Detector network to differentiate between the real and fake images. Starting with a 50% accuracy chance, the detector needs to develop its quality of classification since the generator would grow better in its artificial image generation. Such competition would overall contribute to the network in its effectiveness and speed.
Works Best in:
- Image and Text Generation
- Image Enhancement
- New Drug Discovery processes
5. Self-Organizing Maps
The SOMs or Self-Organizing Maps operate with the help of unsupervised data that reduces the number of random variables in a model. In this type of deep learning technique, the output dimension is fixed as a two-dimensional model, as each synapse connects to its input and output nodes.
As each data point competes for its model representation, the SOM updates the weight of the closest nodes or Best Matching Units (BMUs). Based on the proximity of a BMU, the value of the weights changes. As weights are considered as a node characteristic in itself, the value represents the location of the node in the network.
Works best in:
- When the datasets don’t come with a Y-axis values
- Project explorations for analyzing the dataset framework
- Creative projects in Music, Videos, and Text with the help of AI
6. Boltzmann Machines
This network model doesn’t come with any predefined direction and therefore has its nodes connected in a circular arrangement. Because of such uniqueness, this deep learning technique is used to produce model parameters.
Different from all previous deterministic network models, the Boltzmann Machines model is referred to as stochastic.
Works Best in:
- System monitoring
- Setting up of a binary recommendation platform
- Analyzing specific datasets
7. Deep Reinforcement Learning
Before understanding the Deep Reinforcement Learning technique, reinforcement learning refers to the process where an agent interacts with an environment to modify its state. The agent can observe and take actions accordingly, the agent helps a network to reach its objective by interacting with the situation.
Here, in this network model, there is an input layer, output layer, and several hidden multiple layers – where the state of the environment is the input layer itself. The model works on the continuous attempts to predict the future reward of each action taken in the given state of the situation.
Works Best in:
- Board Games like Chess, Poker
- Self-Drive Cars
- Inventory Management
- Financial tasks like asset pricing
One of the most commonly used types of deep learning techniques, this model operates automatically based on its inputs, before taking an activation function and final output decoding. Such a bottleneck formation leads to yielding lesser categories of data and leveraging most of the inherent data structures.
The Types of Autoencoders are:
- Sparse – Where hidden layers outnumber the input layer for the generalization approach to take place to reduce overfitting. It limits the loss function and prevents the autoencoder from overusing all its nodes.
- Denoising – Here, a modified version of inputs gets transformed into 0 at random.
- Contractive – Addition of a penalty factor to the loss function to limit overfitting and data copying, incase of hidden layer outnumbering input layer.
- Stacked – To an autoencoder, once another hidden layer gets added, it leads to two stages of encoding to that of one phase of decoding.
Works Best in:
- Feature detection
- Setting up a compelling recommendation model
- Add features to large datasets
In deep learning, the backpropagation or back-prop technique is referred to as the central mechanism for neural networks to learn about any errors in data prediction. Propagation, on the other hand, refers to the transmission of data in a given direction via a dedicated channel. The entire system can work according to the signal propagation in the forward direction in the moment of decision, and sends back any data regarding shortcomings in the network, in reverse.
- First, the network analyzes the parameters and decides on the data
- Second, it is weighted out with a loss function
- Third, the identified error gets back-propagated to self-adjust any incorrect parameters
Works Best in:
- Data Debugging
10. Gradient Descent
In the mathematical context, gradient refers to a slop that has a measurable angle and can be represented into a relationship between variables. In this deep learning technique, the relationship between the error produced in the neural network to that of the data parameters can be represented as “x” and “y”. Since the variables are dynamic in a neural network, therefore the error can be increased or decreased with small changes.
Many professionals visualize the technique as that of a river path coming down the mountain slopes. The objective of such a method is — to find the optimum solution. Since there is the presence of several local minimum solutions in a neural network, in which the data can get trapped and lead to slower, incorrect compilations – there are ways to refrain from such events.
As the terrain of the mountain, there are particular functions in the neural network called Convex Functions, which keeps the data flowing into expected rates and reach its most-minimum. There can be differences in methods taken by the data entering the final destination due to variation in initial values of the function.
Works Best in:
- Updating parameters in a given model
There are multiple deep learning techniques that come with its functionalities and practical approach. Once these models are identified and put in the right scenarios, it can lead to achieving high-end solutions based on the framework used by developers. Good luck!
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