Applied Deep Learning with Keras

Solve complex real-life problems with the simplicity of Keras

Created by: Packt Publishing
Tech Knowledge in Motion

What Will I Learn?

• Understand the difference between single-layer and multi-layer neural network models
• Use Keras to build simple logistic regression models, deep neural networks, recurrent neural networks, and convolutional neural networks
• Apply L1, L2, and dropout regularization to improve the accuracy of your model
• Implement cross-validate using Keras wrappers with scikit-learn
• Understand the limitations of model accuracy

Requirements

• Prior experience of Python programming and experience with statistics and logistic regression will help you get the most out of this course.
• Although not necessary, some familiarity with the scikit-learn library will be an added bonus.

Target audience

• If you have basic knowledge of data science and machine learning and want to develop your skills and learn about artificial neural networks and deep learning, you will find this course useful.