# Types of Deep Learning

**Types of Deep Learning:** We are all surrounded by technology. Since its inception, technology has enriched our lives with new functionalities brewing continuously. Cutting-edge technologies like AI and ML have conquered the industries, and they find numerous applications across different companies. Among several such technologies, Deep Learning is one of the worth-considering domains.

Deep Learning has penetrated numerous industries and companies. It has become a widely popular tech stack and remains under the knife of research. Various organizations wish to hire Deep Learning professionals at higher salaries and invest in excavating new potentials. So, Deep Learning has become a successful career, and domain knowledge can take you to heights.

Since Deep Learning is such an emerging technology, experts have discovered several types of the stack. You require learning about all variations to excel at Deep Learning. Are you starting your professional journey with Deep Learning training? It’s excellent to discuss the types of Deep Learning before joining the **Deep Learning online course**.

## What is Deep Learning?

Before studying Deep Learning, you are required to understand the meaning of the technology. So, let’s get it cleared before jumping into the advanced concepts.

So, what do you mean by Deep Learning? To be crisp, Deep Learning is one of the many terminologies under the umbrella of Machine Learning. It comprises numerous algorithms to replicate the functionality of the human brain. Also known as Large Neural Networks, Deep Learning is one of the most sought-after technologies out there. Since deep learning is highly significant, it’s great to build a career in the domain nowadays.

**Prerequisites to Learn Deep Learning**

Deep Learning provides immense avenues to build a lucrative career with overwhelming job opportunities. However, it would help if you went through the prerequisites before taking any Deep Learning course and shaping your career. So, let’s quickly look at the requirements to enrich your learning experience.

- Programming Expertise
- Statistical Knowledge
- Hands-on Calculus
- Linear Algebra
- Probability
- Data Science
- Project Portfolio

You must be stumbling on projects, aren’t you? Although it’s not essential, it might help in understanding the concepts closely. Besides, these projects will help your resume stand out among others and increase your chances of employment.

**What are The Types of Deep Learning?**

As you already know, Deep Learning is a subdomain of Machine Learning. Surprisingly, the massive umbrella of Deep Learning encompasses several types. You are required to get familiar with all kinds of Deep Learning before resorting to advanced concepts. So, to widen your knowledge band, here are all the kinds of Deep Learning discussed below.

**Autoencoders**

Autoencoders are one of the most sensational types of deep learning models. These models perceive various coding patterns and are considered to be a particular kind of feedforward neural network. Another striking aspect of Autoencoders is that the input equals output in such a model. A typical Autoencoder comprises three parts: encoder, code, and decoder.

The encoder, as the name suggests, compresses the input to produce a consumable code. In contrast, the decoder feeds on the code produced and creates an output similar to the information supplied as inputs. Many experts consider Autoencoders an unsupervised learning model; however, it generates labels for training purposes and is a self-supervised learning model in reality.

**Deep Belief Network**

Deep Belief Networks are generative graphical representation models. What does it mean? It implies that the model produces all values for a given case at once. Some experts consider Deep Belief Networks to blend machine learning, statistics, probability, and neural networks.

Deep Belief Networks are made up of numerous layers with values that have a relationship between them but not with them. The primary goal is to assist the system in classifying the data into several groups.

**Convolutional Neural Networks**

Another feedforward multilayer perceptron variation is the convolutional neural network (CNN). It’s a form of feedforward neural network in which the individual neurons are arranged to respond to all overlapping visual regions.

Deep CNN works by sequentially modeling small bits of data and integrating them further down in the network. One way to think about Deep CNN is to find edges and create edge detection templates. The ultimate layers will attempt to integrate them into simpler shapes, eventually resulting in templates of various item placements, lights, scales, and so on.

**Recurrent Neural Networks**

The convolutional model takes a fixed number of inputs and produces a fixed-sized vector with a predetermined number of steps. We can use recurrent networks to operate on vector sequences in both input and output. The link between units in a recurrent neural network produces a directed cycle. Unlike standard neural networks, the input and output of a recurrent neural network are linked rather than independent.

Furthermore, every layer of the recurrent neural network uses the same standard parameters. You can use the backpropagation method to train the recurrent network in the same way as a traditional neural network.

**Reinforcement Learning to Neural Networks**

Reinforcement learning is a type of supervised learning that combines dynamic programming and reinforcement learning. Environment, agent, actions, policy, and cost functions are standard components of the strategy. The agent is the system’s controller; policy sets the actions to be executed, and the reward function specifies the reinforcement learning problem’s overall goal. An agent can perform the best action for a given state if they receive the highest potential reward.

An agent is an abstract entity that conducts activities, whether it be an object or a subject. The position and state of being of an agent in its abstract environment are referred to as its state.

The above types are the essence of Deep Learning. Also, more research is going on in Deep Learning, and more varieties are yet to surface in the future. Learning about the different kinds of Deep Learning will prepare you for any course and project. So, what makes you wait? Gear up with these types of Deep Learning models, and become a professional today!