Ever wondered how smart assistants such as Siri, Google Assistant, and Alexa translate your voice commands into actions? How do search engines provide relevant search results? How do self-driving cars detect accidents? All these systems can work autonomously because of deep learning, which is set to be a "defining future technology". With the advancements around the globe, be it digital, environmental, or even social, technology is the answer because it applies deep learning to new domains and products and helps develop the necessary tools. If such is the importance of deep learning in our lives, we must understand what it is.
Deep learning is a subfield of machine learning that teaches computers to display human-like capabilities such as planning, reasoning, and creativity. It enables the systems to adapt and improvise in a new environment so that they can generalize their knowledge and apply it to unfamiliar scenarios. As far as the technical systems are concerned, deep learning enables them to:
- Perceive and deal with environment
- Solve problems
- Act to achieve specific goals
Top 7 Deep Learning Algorithms to learn in 2023
Following are the top deep learning algorithms that can help you solve complex real-world problems.
Convolutional Layer Networks (CNNs)
A Convolutional Neural Network (ConvNet or CNN) is a well-known artificial neural network that learns directly from data. It takes in an input image, assigns weights and biases to different image objects, and differentiates one from the other. Unlike other classification algorithms, ConvNet doesn’t require high pre-processing.
Convolutional Neural Network imitates the architecture of neurons in the human brain. It is particularly useful for Object detection and Image recognition. Like other artificial neural networks, CNN also consists of an input layer, hidden layers, and an output layer. The three common layers (building blocks) of CNN are:
- Convolutional Layer - This layer applies filters to an input and generates feature maps.
- Pooling Layer - The output from the above layer is a huge grid array. The pooling layer down samples and simplifies the output.
- Fully Connected Layer - This layer maps the representation between the input and the output and can be computed by matrix multiplication followed by the bias effect.
Using Convolutional Neural Networks for deep learning is popular due to the following three factors:
- They produce accurate recognition results.
- They eliminate the need for feature extraction techniques.
- They can be retrained for new recognition tasks.
Recurrent Neural Networks (RNNs)
RNNs are deep learning neural networks that remember the input sequence, store it in cell states/memory states, and predict the future words. They are used for image captioning, time series prediction, machine translation, and natural language processing.
How does RNN Work?
- The input layer of RNN takes in the input, processes it, and passes it on to the middle layer.
- The middle layer contains multiple hidden layers. Each hidden layer has its own weights, biases, and activation functions. RNN standardizes them so that all hidden layers contain the same parameters.
- RNN creates one hidden layer instead of multiple ones (as each has the same parameters) and loops over it as many times as required.
Long Short-Term Memory Networks (LSTMs)
Suppose, while watching a drama, you know what happened in the previous episode. RNNs work in a similar fashion and remember the previous information to process the current input. But their shortcoming is: they can not remember long term dependencies due to vanishing gradient. Here comes the use of LSTMs.
Long Short-Term Memory Networks are advanced recurrent neural networks that can learn long term dependencies and can handle the vanishing gradient problem faced by RNN.
The LSTM network consists of different memory blocks called cells that have different parts as shown below.
Long Short-Term Memory Networks
These three parts of the cell are known as gates.
- The first part i-e., Forget gate, separates the relevant and the irrelevant information coming from the previous timestamp.
- The second part i-e., Input gate, adds/updates new information.
- The third part i-e., Output gate, forwards the added/updated information from the current timestamp to the next timestamp.
Generative Adversarial Networks (GANs)
Generative Adversarial Network (GAN) is an unsupervised learning algorithm that consists of two neural networks. Both of these networks compete with each other to make accurate predictions.
How does GAN Work?
Consider an example to understand the concept.
What would you do to get good at snooker? You would compete with a person who plays better than you. You would interpret where you were wrong, where he/she was right, and think on what strategy you could use to beat him/her in the next game.
You would continue to play the game until you defeat the opponent. In short, to become a powerful hero (generator), you need a more powerful opponent (discriminator). In deep learning, we use this concept to build better models.
GANs contain the following two neural networks:
- Generator - We train this neural network to generate new examples.
- Discriminator - It classifies examples as either real (belonging to the actual training dataset) or fake (generated).
Multilayer Perceptrons (MLPs)
Multilayer Perceptron is a fully-connected multi-layer neural network that has three layers including one hidden layer. If there are more than one hidden layer, it is called a deep Artificial Neural Network.
MLPs are typical examples of feedforward artificial neural networks. Their hyperparameters need tuning and we can use different cross validation techniques to find ideal values for them.
Working of MLP
- The initial step is forward propagation in which the input layer propagates the data to the output layer.
- In the next step, we calculate the error (difference between the predicted and the known outcome) based on the output.
- We backpropagate the error in the neural network and update the model.
Self-Organizing Maps (SOMs)
Self-Organizing Map (SOM) is a data visualization technique that reduces the dimensions of data to a map. It also groups similar data together and showcases clustering.
- In the first step, we assign weights to the network.
- We randomly choose the data from training datasets and calculate distance between weights (nodes) and sample vector.
- A winner node or Best-Matching Unit (BMU) nearest to the sample vector is selected.
- The neighborhood of BMU or the winner node is found.
- The vector weights or nodes are updated.
- We repeat the process until we train the network for every training data.
Radial Basis Function Networks (RBFNs)
Radial Basis Function Networks (RBFNs) consist of an input layer, hidden layer, and an output layer. They use trial and error method to determine the structure of the neural network.
- The input layer is not the computation layer. It receives the input data and feeds it to the hidden layers.
- The input data can have a pattern that might not be linearly separable. The hidden layer takes that input and converts it into a more linearly separable one.
- The output layer uses a linear activation function for regression and classification tasks.
In a Nutshell
Deep learning algorithms have an increasing number of applications in many industries. For instance, iPhone's Facial Recognition uses deep learning to identify data points from our face and unlock the phone. Virtual assistants such as Amazon Echo, Alexa, Siri, and Google Assistant use deep learning algorithms to develop a customized user experience for us. Nowadays, hotels use robots to clean, greet, and deliver room service. Likewise, there are many other applications of deep learning algorithms. So, this is not the end of deep learning; there is way more to come from it. Who knows what AI and deep learning can do for us in the near future? Maybe it will be a society full of robots.