This course is about artificial neural networks. Artificial intelligence and machine learning are getting more and more popular nowadays. In the beginning, other techniques such as Support Vector Machines outperformed neural networks, but in the 21th century neural networks again gain popularity. In spite of the slow training procedure, neural networks can be very powerful. Applications ranges from regression problems to optical character recognition and face detection.
Section 1:
•what are neural networks
•modeling the human brain
•the big picture
Section 2:
•Hopfield neural networks
•how to construct an autoassociative memory with neural networks
Section 3:
•what is back-propagation
•feedforward neural networks
•optimizing the cost function
•error calculation
•backpropagation and gradient descent
Section 4:
•the single perceptron model
•solving linear classification problems
•logical operators (AND and XOR operation)
Section 5:
•applications of neural networks
•clustering
•classification (Iris-dataset)
•optical character recognition (OCR)
•smile-detector application from scratch
In the first part of the course you will learn about the theoretical background of neural networks, later you will learn how to implement them.
If you are keen on learning methods, let’s get started!
Combine the power of Data Science, Machine Learning and Deep Learning to create powerful AI for Real-World applications!
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