4.49 (99 Ratings)

This comprehensive course on ‘Artificial Intelligence’ covers all the basics of neural network-based models. Get a conceptual understanding of learning mechanisms such as Need and history of neural networks, gradient, forward propagation, loss functions and its implementation using python libraries. Learn some essential concepts related to deep neural networks that also work with Google’s powerful library Tensorflow which comes with pre built Keras. We will be covering all theoretical and practical aspects of this subject, in-depth. Dive deeper into the mathematics behind each concept to understand their specifics. We will conclude with a neural network model for classification using MNIST data set. Learn its application on business or real-life problems in other domains.

Skills covered

  • checkPerceptron
  • checkANN
  • checkGradient
  • checkBackpropagation
  • checkActivation Functions
  • checkSoftmax Function

Course Syllabus

Artificial Intelligence with Python

  • playHistory behind neural networks
  • playRelationship between biological neuron and artificial neuron
  • playPerceptron and working mechanism
  • playArchitecture of artificial neural network
  • playTypes of activation functions
  • playSoftmax function
  • playForward propagation
  • playLoss function
  • playDemo using keras framework
  • playBack propagation and gradient descent
  • playTensorflow 2.0
  • playDemo on MNIST data set

Leave a Reply

Your email address will not be published. Required fields are marked *