TrainingByPackt / Applied-Deep-Learning-with-Keras

Solve complex real-life problems with the simplicity of Keras

Applied Deep Learning with Keras

Use Python and Keras to build practical deep learning applications that help prepare you for the future of applied artificial intelligence. Applied Deep Learning with Keras takes you from a basic level of knowledge of machine learning and Python to an expert understanding of applying Keras to develop efficient deep learning solutions. To understand the difference between machine and deep learning, you will build a logistic regression model with scikitlearn and then with Keras. By building prediction models for several real-world scenarios, such as disease prediction and customer churn prediction, you will dive deep into Keras and its many models. You will also gain knowledge about how to evaluate, optimize, and improve your models to gain maximum information. You will learn how to use Keras wrappers with scikit-learn and implement cross-validation techniques on your findings, and apply L1, L2, and dropout regularization techniques to improve the accuracy of your model. To improve accuracy, you will learn how to apply the null accuracy, precision, sensitivity, specificity, and AUC-ROC score techniques to fine-tune your model. Then, you will explore convolutional and recurrent neural networks in detail.

What you will learn

By the end of the course, you will be able to:

• 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

Hardware Requirement

For an optimal student experience, we recommend the following hardware configuration:

• Processor: Intel Core i5 or equivalent
• Memory: 4GB RAM
• Storage: 35GB available space

Software Requirement

You’ll also need the following software installed in advance:

• OS: Windows 7 SP1 32/64-bit, Windows 8.1 32/64-bit or Windows 10 32/64-bit, Ubuntu 14.04 or later, or macOS Sierra or later
• Browser: Google Chrome / Mozilla Firefox (Latest Version)
• Conda
• Notepad++/Sublime Text as IDE
• Python 3.4+ (latest is Python 3.7) installed (from https://python.org)
• Python libraries as needed (Jupyter, NumPy, pandas, Matplotlib, and others)