Artificial intelligence is advancing at a fast speed and is at the top of the hype curve. It involves developing computer systems, and deep learning enables them to perform tasks that require human intelligence. So, we need to understand the basics of deep learning as it has changed the world we live in.
Before discussing the top deep learning algorithms that machines use to mimic the human brain. I will be covering the following topics:
- Introduction to Deep Learning
- What are Neural Networks?
- Working of Deep Learning Algorithms
- Top Deep Learning Algorithms To Learn
Have you ever wondered how Google Translate and Amazon Alexa work? How do self-driving cars perceive their environment and detect objects? How Facebook recommends pages, friends, products, etc. Well, they all work because of deep learning. It is a subset of machine learning that has revolutionized the world with advancements in technology and every business sector. It employs algorithms to process data, imitate thinking processes, understand human speech, and visually recognize objects. But it is not a new concept in the world of technology. The history of deep learning dates back to 1943 when Walter Pitts and Warren McCulloch designed a computer model by imitating the neural nervous system of the human brain. They used threshold logic i-e, a combination of algorithms and mathematics, to mimic the thought process of a human. Since then, deep learning has evolved and played a significant role in automating human life.
Before diving deeper into the details of deep learning algorithms in various fields, we must know what deep learning is. So, deep learning, a buzz in the artificial intelligence world, is a subfield of machine learning that deals with the algorithms inspired by the structure and function of the human brain. It teaches computers to learn from examples so that they can perform tasks intuitive to humans.
In deep learning, neural networks play an essential role. We can define them as a set of algorithms or mathematical processing units that identify relevant relationships in a dataset. A neural network is modeled on the human brain and consists of:
- An input layer,
- Multiple hidden layers,
- An output layer
The data is fed as an input to the neurons. Then, the information is transferred to the next layer using appropriate weights and biases. The output is the final value predicted by the output layer. Neural networks depend on the training data for learning and improving their accuracy over time. They become powerful tools in artificial intelligence and computer science, allowing us to classify and cluster data at high velocity once they are fine-tuned for accuracy.
Deep learning imitates the human brain ability to process data for solving complex problems. Its applications are powering a vast range of industries as it has become more widespread over the years. The deep learning algorithms run through several layers of artificial neural networks and pass a simplified data representation to the next layer. During training, they use unknown elements in the input distribution for extracting features, grouping objects, and discovering useful data patterns. For instance, if we consider an unstructured images dataset, the deep learning algorithms progressively learn about each image as they go through each neural network. The early neural network layers detect low-level features like edges, and subsequent layers provide a more holistic representation of images by combining features from early layers.
Below is the list of the top deep learning algorithms you need to learn to solve complex real-world problems.
- Multilayer Perceptrons (MLPs)
- Recurrent Neural Networks (RNNs)
- Convolutional Layer Networks (CNNs)
- Long Short-Term Memory Networks (LSTMs)
- Restricted Boltzmann Machines (RBMs)
- Radial Basis Function Networks (RBFNs)
- Self Organizing Maps (SOMs)
- Generative Adversarial Networks (GANs)
- Deep Belief Networks (DBNs)
Let’s discuss each of these algorithms briefly.
MLP is the most basic and one of the oldest deep learning algorithms. It is also referred to as a form of Feedforward neural network. Let’s discuss how MLP works?
- The first layer of MLP takes the inputs, and the last layer produces the output based on hidden layers. Each node is connected to the node in the next layer. MLP is called a feedforward network because the information is constantly fed forward between the layers.
- MLP uses sigmoid functions such as
tanhas activation functions to determine the nodes to be fired.
- MLP uses backpropagation for training which is a popular supervised learning technique.
- Each hidden layer is fed with some randomly assigned values known as weights. The input with the combination of weights is supplied to an activation function and passed to the next layer for the output determination. If we don’t achieve the desired ( expected) output, we calculate the loss and back-track to update the weights. The process is continued till the expected output is obtained.
Do you know why Google automatically completes the sentence when you start typing something? It works this way because of RNN. Let’s understand how recurrent neural networks work.
- RNNs are referred to as multiple feedforward neural networks and possess directed cycles among the interconnected nodes.
- They are unique because they take a series of inputs with no size limit.
- They rely not only on weights to determine the output but also on the information learnt from prior inputs.
- They use their memory for processing the next sequence of inputs that implements the auto-complete feature kind of functionalities.
The above figure shows different steps for each time state of an RNN. The output produced is copied and provided back to the network like a loop. Apart from Google Search Engines and Web Browsers, RNNs are also used in text recognition, analyzing video frames, etc.
CNN is a well-known deep learning algorithm with numerous applications in the Object Detection and Image recognition field. It is also referred to as ConvNets. Let’s discuss how it works.
The three basic building blocks of CNN are:
- Convolutional Layer - It is the most important block of CNN that uses a set of filters that can be visualized as a layer of neurons. They have weighted inputs and provide the output based on the input size. These filters generate feature maps when applied to an input image. A particular neuron is activated for each image position, and the output is collected in a feature map.
- Pooling Layer - The pooling layer performs a down-sampling operation that reduces the size of the feature map. The output obtained from the convolutional layer is a grid array huge in size. So, we use a max-pooling algorithm to reduce the size of this array and keep only the most significant input tile from the array.
- Fully Connected Layer - This layer is formed when the flattened matrix from the pooling layer is fed as an input into the neural network that is fully connected i-e., all the neurons are connected. The most common activation function used by CNN is ReLU.
Check out our intro to CNNs using keras to know more about them.
Long Short-Term Memory Network is a kind of recurrent neural network capable of learning long-term dependencies. The network consists of different memory blocks called cells, as shown in the figure below.
The cells remember things, and changes to them are done through mechanisms called gates. Let’s discuss how LSTM works.
sigmoidlayer in LSTM decides what information should be kept intact and what should be thrown away from the cell state.
- LSTM replaces the irrelevant information identified in the above step with the new information. The
tanhplay a significant role in the identification process.
- The cell state helps in determining the final output.
If we want to know more about and see a practical example, check out our article on predicting the price of bitcoin with an LSTM network.
RBM is the simplest deep learning algorithm that consists of a basic structure with two layers:
- A visible input layer
- Hidden layer
Let’s discuss how RBM works.
- The input
xis multiplied by the respective weight w at each hidden node.
- The hidden nodes receive the inputs multiplied by their respective weights and a bias value.
- The activation function passes the result to the output layer for reconstruction.
- RBMs compare the reconstruction with the original input for determining the quality of the result.
RBFN uses the Radial Basis Function (RBF) as an activation function and determines the structure of the neural network by trial and error method. Let’s see how it works.
- In the first step, RBFN determines the centres of the hidden layer using an unsupervised learning algorithm such as k-means clustering.
- In the next step, it determines the weights with linear regression. Mean Squared Error (MSE) determines the error, and the weights are tweaked accordingly to minimize MSE.
SOMs are used to understand the correlation between features and visualize data when the dataset consists of hundreds of features. Let’s see how they work.
- SOMs create a 1D or a 2D map and group similar data items together.
- The weights are initialized randomly for each node, just like other algorithms.
- One sample vector x is randomly taken from the input dataset at each stage, and the distances between x and all other vectors are computed.
- A Best-Matching Unit (BMU) closest to x is selected after voting among all the other vectors.
- The weight vectors are updated once the BMU is identified.
- The BMU and its neighbours are moved closer to the input vector x in the input space. We repeat the process until we get the expected result.
GAN is an unsupervised learning algorithm that automatically discovers the data, learns the patterns, and generates new examples that resemble the original dataset. Let’s see how it works.
Generative Adversarial Network contains two neural networks that are as:
- Generator Network - It is a neural network that generates new examples.
- Discriminator Network - It evaluates the generated examples and decides whether they belong to the actual training dataset.
GANs generate cartoon characters, edit images, and are widely used in the gaming industry for 3D object generations. We can train GANs effectively using unlabeled data so that they can produce realistic and high-quality results.
If you want to learn from a practical example of GANs, check out our generating images with deep learning tutorial.
A DBN is generated when several Restricted Boltzmann Machines (RBM) layers are appended. Let’s see how the network works.
- DBNs are pre-trained by using the Greedy algorithm. They learn all the generative weights and the top-down approaches using a layer-by-layer approach.
- Some steps of Gibbs sampling are run on the top two hidden layers of the network.
- To obtain a sample from the visible units, we use a single pass of ancestral sampling through the rest of the model.
- In the next step, the learning of the values of the latent variables can be concluded by a single, bottom-up pass.
Autoencoders are unsupervised algorithms that convert multi-dimensional data into low-dimensional data. Let’s see how they work.
The three main components in autoencoders are:
- Encoder - It is used to compress the input into a latent space representation that can be reconstructed later to get the original input.
- Code - It is the latent space representation i-e., compressed part obtained after encoding.
- Decoder - It is used for reconstructing the code to its original form.
The massive deployment of deep neural network architecture, computational power, and big data has improved the conventional statistical models for predicting optimized knowledge. Apart from many deep learning applications in our daily lives, many people do not recognize its significance. Currently, many organizations are adopting the breakthroughs of advanced technologies like machine learning, the Internet of Things, artificial intelligence, etc., to remain competitive in the industry. So, deep learning outshines other techniques when it comes to solving complex problems such as natural language processing, speech recognition, and image classification. It’s because we have to worry less about feature engineering.
Thanks for reading!