Refer to this project Wiki to access all details.
- 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
- 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.