Share Description
In this course, you will learn:
- Demonstrate you to Python programming for artificial intelligence, along with a few demonstrated examples. It will begin with understanding the history of neural networks.
- As you progress through the first part of the course, you will learn about the differences between a biological neuron and an artificial neuron, Perceptron and its working mechanism, ANN architecture, and various functions such as activation, softmax, forward propagation, and loss.
- The second section keeps you interested by showcasing tasks using the Keras framework, propagation and gradient descent, and an application study on the MNIST dataset.
- In addition, the course will teach you how Tensorflow 2.0 works.
Syllabus:
- History behind neural networks
- Relationship between biological neuron and artificial neuron
- Perceptron and working mechanism
- Architecture of artificial neural network
- Types of activation functions
- Softmax function
- Forward propagation
- Loss function
- Demo using keras framework
- Back propagation and gradient descent
- Tensorflow 2.0
- Demo on MNIST data set
