In the previous chapter, we learned how to create a **convolutional neural network** (**CNN**) from scratch with Keras. However, in real-world projects, you almost never code a convolutional neural network from scratch. You always tweak and train them as per the requirement. This book introduces you to the important concepts of **transfer learning** and **pre-trained networks**, also known as pre-trained models, which are used in the industry. This is an advanced level of machine learning, so this chapter assumes that you have adequate knowledge of neural networks and CNNs. We will use images and, rather than building a CNN from scratch, we will match these images on pre-trained models to try to classify them. We will also tweak our models to make them more flexible. The models we will use here are VGG16 and ResNet50, which we will discuss further in the chapter. Before starting to work on pre-trained models, we need to understand about transfer learning.

#### Applied Deep Learning with Keras

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#### Applied Deep Learning with Keras

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#### Overview of this book

Though designing neural networks is a sought-after skill, it is not easy to master. With Keras, you can apply complex machine learning algorithms with minimum code.
Applied Deep Learning with Keras starts by taking you through the basics of machine learning and Python all the way to gaining an in-depth understanding of applying Keras to develop efficient deep learning solutions. To help you grasp the difference between machine and deep learning, the book guides you on how to build a logistic regression model, first with scikit-learn and then with Keras. You will delve into Keras and its many models by creating prediction models for various real-world scenarios, such as disease prediction and customer churning. You’ll gain knowledge on how to evaluate, optimize, and improve your models to achieve maximum information. Next, you’ll learn to evaluate your model by cross-validating it using Keras Wrapper and scikit-learn. Following this, you’ll proceed to understand how to apply L1, L2, and dropout regularization techniques to improve the accuracy of your model. To help maintain accuracy, you’ll get to grips with applying techniques including null accuracy, precision, and AUC-ROC score techniques for fine tuning your model.
By the end of this book, you will have the skills you need to use Keras when building high-level deep neural networks.

Table of Contents (12 chapters)

Applied Deep Learning with Keras

Preface

Preface

Free Chapter

Introduction to Machine Learning with Keras

Machine Learning versus Deep Learning

Deep Learning with Keras

Evaluate Your Model with Cross-Validation using Keras Wrappers

Improving Model Accuracy

Model Evaluation

Computer Vision with Convolutional Neural Networks

Transfer Learning and Pre-Trained Models

Sequential Modeling with Recurrent Neural Networks

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