Deep Learning is a subset of Machine Learning which is completely based on artificial neural networks. As neural networks are designed to mimic the human brain, deep learning is likewise a human brain mimic. These neural networks are made up of a simple mathematical function that can be stacked on top of each other and arranged in the form of layers, giving them a sense of depth, hence the term Deep Learning.
5
Bachelor
Online
English
04.09.2024
15.08.2024
9.500 NOK + semester fee and literature
Learn more about the power of neural networks and get a better understanding of artificial with our Deep Learning course.
Content
Deep Learning has a plethora of applications in almost every field such as health care, finance, and image recognition. It can be used to solve any pattern recognition problem and without human intervention. There are several popular and widely used deep learning frameworks that help to build neural network models. Some of the common ones are Tensorflow, Keras, PyTorch, and DL4J.
In this course, in addition to getting a solid understanding of deep learning, you will get hands-on experience by solving practical, real-life tasks using state-of-the-art techniques and software frameworks from machine learning, and deep learning. The concepts covered in this course provide relevant theoretical and hands-on programming knowledge.
This course covers the following topics:
- Introduction to Deep Learning (Deep Learning Fundamentals, AI vs. Machine Learning vs. Deep Learning - Relationship Overview)
- Artificial Neural Networks (Perceptrons, Intro to Artificial Neural Networks, Layers in Artificial Neural Networks, Activation Functions in Artificial Neural Networks, Loss Functions in Artificial Neural Networks, Training Artificial Neural Networks, Batch Size & Epochs in Artificial Neural Networks, Optimization Algorithms in Artificial Neural Networks, Learning Rates in Artificial Neural Networks, Backpropagation Intuition - Neural Network Training, Bias in Artificial Neural Networks) .
- Additional Fundamental topics (Datasets for Deep Learning - Training, Validation, & Test Sets, Overfitting- Artificial Neural Networks, Underfitting- Artificial Neural Networks, Tensor flow, and Keras, Multi-Layer Perceptron (MLP), Classification with the Tensor flow and MLP, Regression with the Tensor flow and MLP)
- Convolutional Neural Networks (CNNs) (What are CNNs? Visualizing convolutional filters, Zero padding, Max pooling)
- Recurrent Neural Networks (RNN) (What are RNNs? Architecture, Applications of RNNs)
- LSTM (General structure of LSTM neural network, RNN neural network, and long-term dependence, LSTM neural network and long-term dependence, LSTM network architecture.
The course will be set up as long as there is a sufficient amount of applicants
Lecturer
Department of Electrical Engineering and Computer Science
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