shadimsaleh / deep-learning-fundamentals-keras

IBM: DL0101EN: Deep Learning Fundamentals with Keras

Deep Learning Fundamentals with Keras

Refer to this project Wiki to access all details.

Index

• Module 1 - Introduction to Deep Learning
  • Introduction to Deep Learning
  • Biological Neural Networks
  • Artificial Neural Networks - Forward Propagation
• Module 2 - Artificial Neural Networks
  • Gradient Descent
  • Backpropagation
  • Vanishing Gradient
  • Activation Functions
• Module 3 - Keras and Deep Learning Libraries
  • Introduction to Deep Learning Libraries
  • Regression Models with Keras
  • Classification Models with Keras
• Module 4 - Deep Learning Models
  • Shallow and Deep Neural Networks
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • Autoencoders

Topics

• Exciting applications of deep learning and why it is really rewarding to learn how to leverage deep learning skills.
• Neural networks and how most of the deep learning algorithms are inspired by the way brain functions and the neurons process data.
• How neural networks feed data forward through the network.
• The gradient descent algorithm and how variables are optimized with respect to a defined function.
• Backpropagation and how neural networks learn and update their weights and biases.
• The vanishing gradient problem.
• Activation Functions.
• Deep learning libraries namely, Keras, PyTorch, and TensorFlow.
• Building a regression model using the Keras library.
• Building a classification model using the Keras library.
• The difference between the shallow and deep neural networks.
• Convolutional networks and how to build them using the Keras library.
• Recurrent Neural Networks.
• Autoencoders and how to build them using the Keras library.