Online | May 24, 2021, October 4, 2021,

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Course Details

Course Outline
Learning Outcomes
Materials
Prior Knowledge

Course Outline

Supervised learning: how to frame classification, regression and prediction tasks
Multilayer perceptrons: a simple neural network architecture
Training neural networks: stochastic gradient descent and back propagation
Deep learning: learning complex features from big data
Parameter tuning: regularisation methods to avoid overfitting, choosing activation functions
Convolutional neural networks: learning from image data
Scaling: using GPUs with big data

Learning Outcomes

On successful completion of the course, delegates will…

be able to leverage the popular tensorflow python package to build feed forward neural networks for both regression and classification tasks.
gain an understanding of deep learning terminology and the mechanisms behind how such networks are trained.
gain some experience in integrating with data pre-processing and pipeline creation from the sklearn python package.
experience building convolutional neural networks (CNNs) and monitoring their training via tensorboard.

Materials

Prior Knowledge

Basic knowledge of Python and a familiarity with mathematical concepts such as exponentiation, logarithms and summation notation. Some knowledge of calculus, matrix algebra and probability would be helpful but is not essential. The ideas needed from these subjects will be explained during the course.

Attendee Feedback

“The instructor was great and really helpful and willing to answer loads of wider questions as well as troubleshoot issues with the assignements.”
“The delivery of the training was excellent.”
“Things that were good included the math background and understanding the concept of neural nets, also how willing the instructor was to answer wider questions.”

Training Course Details

Python and Tensorflow

Course Level: Intermediate