What are neural networks and deep learning?

What are neural networks and deep learning?

In recent years, the idea of neural networks and deep learning has grown in prominence. Deep learning and neural networks are a subset of Artificial Intelligence that can be of great help in data analytics. They both serve a distinct purpose, and, nonetheless, these phrases are usually correlated in the discussion, causing some misunderstanding. Both are advanced technologies used to tackle complicated issues that need analytical computations akin to those performed by the human mind.  So, What distinguishes neural networks and deep learning?

What are neural networks?

Neural networks, also known as artificial neural networks (ANN), mimic human brain function, allowing computer programs to identify patterns and solve common issues in AI, machine learning, and deep learning. A set of algorithms is used to simulate the human brain in neural networks. The neural network has only 3 layers i.e Input layer, Hidden layers & Output layer.

A neural network is a collection of algorithms that attempts to detect underlying correlations in a piece of data. Changes in input can be accommodated by neural networks without requiring a revision of the output criteria. Deep learning techniques are built on neural networks, which are a subset of machine learning. However, you should know that there is a fundamental distinction between neural networks and deep learning.

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What is deep learning?

Deep learning is a branch of machine learning in artificial intelligence that employs networks for unsupervised learning. These neural networks seek to mimic the function of the human brain in terms of data processing and pattern formation for use in decision making. A deep neural network is an ANN that has more than three layers.  

While a single-layer neural network may still produce approximate predictions, more hidden layers can assist optimize and tune for accuracy. Many artificial intelligence (AI) apps and services rely on deep learning to increase automation by completing analytical and physical activities without human interaction. A deep learning system is a self-teaching system that learns by filtering input via several hidden layers.

Neural networks and deep learning

Neural networks are inspired by programming from human biology that allows a computer to learn from observational data. Deep learning is a powerful collection of neural network learning algorithms. Deep learning and neural networks are now the greatest answers to many issues in image identification, audio recognition, and natural language processing.

Applications

Neural networks and deep learning real-world applications are widespread in our lives, but in most cases, they are so effectively integrated into goods and services that consumers are oblivious of the complicated data processing that is taking place in the background. Machinery diagnostics, investment management, creditworthiness, and other applications make use of neural networks. Deep learning is utilized in industries like finance, stock market research, customer service, and healthcare.

While Neural Networks employ neurons to transfer data in the form of input and output values via connections, Deep Learning is linked with feature modification and extraction, attempting to create a relationship between stimuli and related neural responses existing in the brain.

The number of node layers or depth distinguishes neural networks and deep learning. In simple terms, Deep learning is an extended version of the Neural network. So, deep learning can handle a broader range of issues with more ease and efficiency than a Neural network. With enough time and effort, neural networks and deep learning’s autonomous learning approaches might produce simulations that nearly resemble human action. Praxis Business School, a top B-School with campuses in Kolkata and Bangalore, provides industry-driven 9 months Post Graduate Programs in Data Science. Our PGP course in Data Science with ML and AI aims to provide students with the tools, techniques, and abilities necessary for a smooth transition into the area of Analytics and advancement into the jobs of Data Scientists. We focus on providing students with a complete analytics experience.

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