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Deep Learning is used for building and training neural networks – layers of decision-making nodes inspired by the human brain.
It is a subset of machine learning (ML) where artificial neural networks learn from large amounts of data. It allows machines to solve complicated problems even when using diverse, unstructured, and interconnected data sets.
This Deep Learning Training course will provide you with a basic understanding of the linear algebra, probabilities, and algorithms used in deep neural networks. After learning the difference between deep learning and machine learning, delegates will gain in-depth knowledge of the different types of neural networks such as feedforward, convolutional, and recursive. At the end of this course, delegates will be able to build complex models that help machines to solve real-world problems.
Machine Learning Basics
Introduction to Deep Learning
Artificial Neural Networks
Deep Neural Networks
Monte Carlo Methods
Deep Generative Model
Libraries and Frameworks
It is recommended that delegates possess some basic knowledge of Python, linear algebra and probability.
This course is intended for individuals who wish to learn how deep learning works and who may wish to develop their own applications using deep learning techniques.
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Resources are included for a comprehensive learning experience.
"Really good course and well organised. Trainer was great with a sense of humour - his experience allowed a free flowing course, structured to help you gain as much information & relevant experience whilst helping prepare you for the exam"
Joshua Davies, Thames Water
"...the trainer for this course was excellent. I would definitely recommend (and already have) this course to others."
Diane Gray, Shell