## Deep learning and how it differs from Neural Networks

Machine Learning and Artificial Intelligence have come a long way since their inception in the late 1950s. Recent years have seen an increase in the complexity and advancement of these technologies. Technology advancements in the Data Science domain are commendable, but they have led to a flood of terminology beyond the average person’s comprehension.

The use of these technologies is prevalent across businesses of all sizes. There are many applications of AI and ML in our day-to-day lives, but many don’t know how to distinguish between their vast terminologies.

This confusion results from the fact that, despite having so many names for different concepts, most have deep ties with one another. Despite this, each of these terminologies has distinct advantages and disadvantages.

Understanding a Neural Network is essential before seeing what makes it different from a Deep Learning system.

**What is a Neural Network?**

**What is a Neural Network?**

Human brains are the most complex objects in the universe and inspire neural networks. Any neural network, including the brain, comprises neurons, the basic computational units.

A neuron receives input, processes it, then passes it on to other neurons in the hidden layers until the processed output reaches the output layer.

By interpreting sensory data, neural networks can label or group raw data based on machine perception. Data in the real world (images, sounds, text, time series, etc.) must be transformed into numerical patterns contained in vectors.

The input, output, and hidden layers are the three primary layers of an Artificial Neural Network (ANN).

**What is Deep Learning?**

**What is Deep Learning?**

Now that we have covered the definition and basic meaning of Neural Networks let’s delve further and understand Deep Learning.

In artificial intelligence, deep learning is a subset of machine learning known as hierarchical learning that mimics the brain’s computing capabilities and allows for creating patterns similar to those the brain uses to make decisions. Data representations are used in deep learning systems instead of task-based algorithms. Unstructured or unlabeled data can be analyzed by it.

Known as deep learning systems or deep neural networks, neural networks with multiple hidden layers and nodes are known as deep learning systems or deep neural networks. A deep learning algorithm is an algorithm that trains and predicts output from complex data using deep learning algorithms.

A Deep Learning Model contains more than three layers, i.e., the input layer and output layer, and is defined by the number of hidden layers.

**Differences in the architecture of Neural Networks and Deep Learning**

**Differences in the architecture of Neural Networks and Deep Learning**

**In a neural network architecture:**

- Generally, feedforward neural networks consist of two layers: an input layer and an output layer. The layers in the middle of the middle layers are hidden layers.
- An ANN architecture in which nodes form a directed graph along a temporal sequence of connections is called a recurrent neural network (RNN). As a result, such networks exhibit dynamic behavior over time.
- There is only one difference between symmetrically connected neural networks and recurrent neural networks: the connections between units are symmetric (i.e., same weights both ways).

**In a deep learning system architecture:**

- Unlike supervised networks, unsupervised networks, including Autoencoders and Deep Belief networks, use past experiences to train and do not require formal training.
- With the help of a convolutional neural network, you can assign meaning to various objects in an image (learnable weights and biases) and identify them by their position.
- A recursive neural network generates a structured prediction over scalar predictions generated over variable-size input structures by recursively applying the same weights over time to a structured input.

**Structural differences**

In a neural network structure:

** Neurons: **Neurons are mathematical functions designed to mimic the behavior of biological neurons. The logistic function calculates a weighted average and sends the data through a nonlinear process based on the data supplied.

** A Neural Network has two propagation functions:** forward propagation, which generates the “predicted value,” and backward propagation, which produces the “error value.”

** Weights and connections: **The name implies that the links connect a neuron in one layer with another neuron in another layer. To minimize the chances of losing weight (error), each connection is assigned a weight value that reflects the strength of the relationship between the units.

** As neural networks are trained with Learning Rate:** Gradient Descent, each weight value is subtracted from the derivative of the loss function calculated using back-propagation at every iteration. Model weight values are updated according to the learning rate.

In a deep learning system structure:

** Motherboard: **Deep learning models usually use motherboard chips with PCI-e lanes.

** Processor: T**he GPU requirements for Deep Learning models depend on the number of cores and the cost of the processor.

** Random Access Memory (RAM): **Deep learning models require enormous computing power and storage. To do this, they need bigger RAMs.

** Power Supply Unit (PSU):** Having a big Power Supply Unit (PSU) capable of handling enormous and complex Deep Learning functions becomes increasingly essential as memory requirements increase.

**Conclusion**

Since Deep Learning and Neural Networks are similar in nature and related, it is often difficult to differentiate them on the surface. However, by now, you have probably figured out that these two concepts are not the same and have their distinct differences.

A Neural Network uses neurons to transmit data from input to get output using the various connections in the network. In contrast, Deep Learning focuses on transforming and extracting features involved in establishing a relationship between stimuli and neural responses.