Fundamentals of Neural Networks

Preview this course

Get started with Neural networks and understand the underlying concepts of Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks. This hands-on course will help you understand deep learning in detail with no prior coding or programming experience required.

Unlimited access to 750+ courses.
Enjoy a Free Trial. Cancel Anytime.

- OR -

30-Day Money-Back Guarantee
Full Lifetime Access.
44 on-demand videos & exercises
Level: Beginner
6hrs 37mins
Access on mobile, web and TV

What to know about this course

Learning can be supervised, semi-supervised or unsupervised. Deep-learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks, and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, material inspection, and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance.
This course covers the following three sections: (1) Neural Networks, (2) Convolutional Neural Networks (CNN), and (3) Recurrent Neural Networks (RNN). You will learn about logistic regression and linear regression and know the purpose of neural networks. You will also understand forward and backward propagation as well as the cross-entropy function. Furthermore, you will explore image data, convolutional operation, and residual networks. In the final section of the course, you will understand the use of RNN, Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM). You will also have code blocks and notebooks to help you understand the topics covered in the course.

By the end of this course, you will have a hands-on understanding of Neural Networks in detail. All resources and code files are placed here:

Who's this course for?

This course can be taken by a beginner level audience that intends to obtain an in-depth overview of Artificial Intelligence, Deep Learning, and three major types of neural networks: Artificial Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks.

There is no prior coding or programming experience required. This course assumes you have your own laptop, and the code will be done using Colab.

What you'll learn

  • Learn about linear and logistic regression in ANN
  • Learn about cross-entropy between two probability distributions
  • Understand convolution operation which scans inputs with respect to their dimensions
  • Understand VGG16, a convolutional neural network model
  • Understand why to use recurrent neural network
  • Understand Long short-term memory (LSTM)

Key Features

  • Understand the intuition behind Artificial Neural Networks, Convolution Neural Networks, and Recurrent Neural Networks
  • Understand backward and forward propagation in ANN
  • Understand Bidirectional Recurrent Neural Networks (BRNN)

Course Curriculum

About the Author

Yiqiao Yin

Yiqiao Yin was a PhD student in statistics at Columbia University. He has a BA in mathematics and an MS in finance from the University of Rochester. He also has a wide range of research interests in representation learning: feature learning, deep learning, computer vision, and NLP. Yiqiao Yin is a senior data scientist at an S&P 500 company LabCorp, developing AI-driven solutions for drug diagnostics and development.
He has held professional positions as an enterprise-level data scientist at EURO STOXX 50 company Bayer, a quantitative researcher at AQR working on alternative quantitative strategies to portfolio management and factor-based trading, and equity trader at T3 Trading on Wall Street.

40% OFF! Unlimited Access to 750+ Courses. Redeem Now.