Deep Learning Introduction

Welcome to the introduction to Deep Learning course. In this course, you will learn the fundamentals of deep learning, including neural networks, convolutional neural networks, and recurrent neural networks. You will also gain hands-on experience with popular deep learning frameworks like TensorFlow and PyTorch.

Course Outline

• Introduction to Neural Networks

  • The history of neural networks
  • Types of neural networks
  • Activation functions

• Convolutional Neural Networks (CNNs)

  • What are CNNs?
  • Applications of CNNs
  • CNN architecture

• Recurrent Neural Networks (RNNs)

  • What are RNNs?
  • Applications of RNNs
  • RNN architecture

• Deep Learning Frameworks

  • TensorFlow
  • PyTorch

• Practical Projects

  • Image classification
  • Natural language processing

Learning Objectives

• Understand the basic concepts of neural networks, CNNs, and RNNs.
• Learn how to implement deep learning models using TensorFlow and PyTorch.
• Gain hands-on experience with practical deep learning projects.

Course Materials

• Textbook: "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
• Online Resources: Deep Learning Specialization by Andrew Ng.

Neural Network

For more information about deep learning, please visit our Deep Learning Resources.