Course: Neural Network Mathematics
duration: 5 hours |
Language: English (US) |
access duration: 90 days |
Details
This course teaches you to gain an intuitive understanding of how neural networks work. You’ll explore the mathematical operations performed by a single neuron and learn to recognize the potential of thousands of neurons connected in a well-architected design. In addition, you’ll gain an intuitive and visual understanding of how gradient descent and the gradient vector work. Finally, you’ll examine three neural network activation functions, ReLU, sigmoid, and hyperbolic tangent functions, and two variants of the ReLU function, Leaky ReLU and ELU.
Result
When you're finished, you’ll have a solid grasp of the mechanisms behind neural networks, you'll be able to illustrate the mathematical intuition behind neural networks and you'll be prepared to tackle more complex machine learning problems.
Prerequisites
No formal prerequisites. Some prior knowledge about neural networks is recommended.
Target audience
System Administrator, Network Administrator
Content
Neural Network Mathematics
Neural Network Mathematics: Understanding the Mathematics of a Neuron
First conceived in the 1940s, it wasn't until the early 2010s
- that artificial neurons showed their true potential as layered
- entities in the form of neural networks. When big data processing
- using distributed computing became mainstream, the computational
- capacity was now available to train these neural networks on huge
- datasets. Knowing this is one thing, but understanding how it all
- works is where the true potential lies. Use this course to gain an
- intuitive understanding of how neural networks work. Explore the
- mathematical operations performed by a single neuron. Recognize the
- potential of thousands of neurons connected together in a
- well-architected design. Finally, implement code to mathematically
- perform the operations in a single layer of neurons working on
- batch input. When you're finished, you'll have a solid grasp of the
- mechanisms behind neural networks and the math behind neurons.
Neural Network Mathematics: Exploring the Math behind Gradient Descent
- Because neural networks comprise thousands of neurons and interconnections, one can assume training a neural network involves millions of computations. This is where a general-purpose optimization algorithm called gradient descent comes in.
- Use this course to gain an intuitive and visual understanding of how gradient descent and the gradient vector work. As you advance, examine three neural network activation functions, ReLU, sigmoid, and hyperbolic tangent functions, and two variants of the ReLU function, Leaky ReLU and ELU.
- In examining variants of the ReLU activation function, learn how to use them to deal with deep neural network training issues. Finally, implement a neural network from scratch using TensorFlow and basic Python.
- When you're done, you'll be able to illustrate the mathematical intuition behind neural networks and be prepared to tackle more complex machine learning problems.
Course options
We offer several optional training products to enhance your learning experience. If you are planning to use our training course in preperation for an official exam then whe highly recommend using these optional training products to ensure an optimal learning experience. Sometimes there is only a practice exam or/and practice lab available.
Optional practice exam (trial exam)
To supplement this training course you may add a special practice exam. This practice exam comprises a number of trial exams which are very similar to the real exam, both in terms of form and content. This is the ultimate way to test whether you are ready for the exam.
Optional practice lab
To supplement this training course you may add a special practice lab. You perform the tasks on real hardware and/or software applicable to your Lab. The labs are fully hosted in our cloud. The only thing you need to use our practice labs is a web browser. In the LiveLab environment you will find exercises which you can start immediately. The lab enviromentconsist of complete networks containing for example, clients, servers,etc. This is the ultimate way to gain extensive hands-on experience.
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