- Chapter 1 – Introduction to Deep Learning: Understand what is deep learning, explore its history, its relation to machine learning and artificial intelligence; find out why it’s important.
- Chapter 2 – Structure of Neural Networks: Understand the concept of neurons, how they are interconnected and form the basis of neural networks. Learn about network structure, including input, hidden, and output layers.
- Chapter 3 – Deep Feedforward Networks: Learn about one of the simplest kinds of artificial neural networks – deep feedforward networks. Understand the backpropagation algorithm used to train these networks.
- Chapter 4 – Convolutional Neural Networks (CNN): Explore the principles behind CNNs and how they are used in image and video processing. Work on a project implementing a basic CNN.
- Chapter 5 – Recurrent Neural Networks (RNN) and Long Short Term Memory (LSTM): Learn how RNNs and LSTMs harness sequential information by leveraging previous outputs as inputs for the subsequent prediction. Get hands-on by building a basic RNN for time-series analysis.
- Chapter 6 – Deep Learning in Real-world Applications: Learn about the exciting applications of deep learning and neural networks across various industries, including voice recognition, self-driving cars, and healthcare.
- By the end of this course, you’re not just informed about the world of deep learning but equipped to function in it, understand it, and apply this powerful tool in various problem-solving scenarios. Prepare to be submersed in the deep, intriguing waters of Deep Learning!