Skip to main content

Neural Network Courses

What Is A Neural Network?

Neural networks are algorithms intended to mimic the human brain. As computers get smarter, their ability to process the way human minds work is the forefront of tech innovation. The fundamental block of deep learning is built on a neural model first introduced by Warren McCulloch and Walter Pitts. Cracking Artificial Intelligence requires that algorithms perform not just similar to the human mind but better. Humans cannot process the amount of data available now, so machine learning is revolutionizing the way we make decisions within just about every field. The neural network isn't an algorithm itself. Instead, it's a framework that informs the way learning algorithms perform. These deep neural networks have real-world applications that are transforming the way we do just about everything.

Learn Neural Networks

Learning Neural Networks goes beyond code. The principles of the framework inform every aspect of how you approach a project. Neural networks and deep learning are principles instead of a specific set of codes, and they allow you to process large amounts of unstructured data using unsupervised learning. Feedforward neural networks are the simplest versions and have a single input layer and a single output layer. However, with multilayer perceptron models, you also have a series of hidden layers that can learn non-linear functions through activation functions like relu. These artificial neural networks build systems of pattern recognition and process large numbers of data sets to produce models of deep learning. We not only have access to our big data, but we can efficiently interpret it through these systems.

Neural Network Courses And Certifications

Courses to help you with the foundations of building a neural network framework include a master's in Computer Science from the University of Texas at Austin. It contains 30 credit hours of study based on the campus learning program from a university consistently rated in the top ten for computer science. If you've already got a foundation in computer science, courses in machine learning and deep learning could help jumpstart your career as a data scientist or developer. IBM's course in deep learning using Tensorflow can help you understand the principles of deep learning and build your skills beyond feedforward networks and single hidden layers. IBM also offers professional certification in deep learning. You'll understand the basics of deep learning (sigmoid functions, training examples, reinforcement learning, for example) and master deep learning libraries such as Tensorflow, Keras, and Pytorch. You'll be able to apply deep learning to real-world use cases through object recognition, text analytics, and recommender systems. MIT's Data Science course teaches you to apply deep learning to your input data and build visualizations from your output.

Explore A Career In Deep Learning

Machine learning algorithms are getting more complex. Whether you've started in Python or are using any number of languages and frameworks to build your model, neural networks are a framework that can offer your business or organization cutting edge data feedback. Decision-making with this type of data is the next wave of tech.