Neural Networks With Deep Learning Training Course in Richmond

Who should attend this Neural Networks with Deep Learning Training Course?

The Neural Networks with Deep Learning Course will provide you a deep understanding of Neural Network Architectures and how they can be leveraged in various applications and industries through Deep Learning techniques. The following are some professionals who can greatly benefit from this course:

• Data Scientists
• Machine Learning Engineers
• Software Developers
• Data Engineers
• DevOps Engineers
• UI/UX Designers
• Regulatory and Compliance Professionals

Prerequisites of the Neural Networks with Deep Learning Training Course

There are no formal prerequisites for this Neural Networks with Deep Learning Training, but a basic understanding of the Python programming language would be helpful.

Neural Networks with Deep Learning Training Course Overview

Neural Networks are a set of algorithms designed to identify patterns. These are developed to imitate the human brain. Neural networks translate sensory data through labelling or clustering raw input and machine perception. These networks identify numerical patterns that are stored in vectors. All the real-world data, including text, images, or sound, must be translated into these numerical patterns. Neural networks can be thought of as a clustering and classification layer on top of the data stored and managed.

The Knowledge Academy’s Neural Networks with Deep Learning Training course will provide delegates with an understanding of deep learning and neural networks. Delegates will be familiarised with basic concepts of neural networks such as binary classification, logistic regression, derivatives, and vectorisation.

During this 1-day training course, delegates will be introduced to Python and Jupyter/IPython notebooks. Delegates will learn about shallow neural networks, including vectorised implementation, activation functions, and backpropagation intuition. In addition, delegates will also gain knowledge on the concepts of deep neural networks involving deep L-layer neural network, deep representations, and forward and backward propagation.