Book Image

Applied Deep Learning with Keras

By : Ritesh Bhagwat, Mahla Abdolahnejad, Matthew Moocarme
Book Image

Applied Deep Learning with Keras

By: Ritesh Bhagwat, Mahla Abdolahnejad, Matthew Moocarme

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.

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Table of Contents (12 chapters)
Applied Deep Learning with Keras
Applied Deep Learning with Keras
Preface
Preface
Preface
Preface
Free Chapter
1
Introduction to Machine Learning with Keras
Introduction to Machine Learning with Keras
Introduction
Data Representation
Data Preprocessing
Machine Learning Libraries
scikit-learn
Keras
Model Training
Model Tuning
Summary
2
Machine Learning versus Deep Learning
Machine Learning versus Deep Learning
Introduction
Linear Transformations
Introduction to Keras
Summary
3
Deep Learning with Keras
Deep Learning with Keras
Introduction
Building Your First Neural Network
Model Evaluation
Summary
4
Evaluate Your Model with Cross-Validation using Keras Wrappers
Evaluate Your Model with Cross-Validation using Keras Wrappers
Introduction
Cross-Validation
Cross-Validation for Deep Learning Models
Model Selection with Cross-validation
Summary
5
Improving Model Accuracy
Improving Model Accuracy
Introduction
Regularization
L1 and L2 Regularization
Dropout Regularization
Other Regularization Methods
Hyperparameter Tuning with scikit-learn
Summary
6
Model Evaluation
Model Evaluation
Introduction
Accuracy
Imbalanced Datasets
Confusion Matrix
Summary
7
Computer Vision with Convolutional Neural Networks
Computer Vision with Convolutional Neural Networks
Introduction
Computer Vision
Convolutional Neural Networks
Architecture of a CNN
Image Augmentation
Summary
8
Transfer Learning and Pre-Trained Models
Transfer Learning and Pre-Trained Models
Introduction
Pre-Trained Sets and Transfer Learning
Fine-Tuning a Pre-Trained Network
Summary
9
Sequential Modeling with Recurrent Neural Networks
Sequential Modeling with Recurrent Neural Networks
Introduction
Sequential Memory and Sequential Modeling
Recurrent Neural Networks (RNNs)
Long Short-Term Memory (LSTM)
Summary

Accuracy

To understand accuracy properly, first let's explore model evaluation. Model evaluation is an integral part of the model development process. Once you build your model and execute it, the next step is to evaluate your model. A model is built on a training dataset, and evaluating a model's performance on the same training dataset is a bad practice in data science. Once a model is trained on a training dataset, it should be evaluated on a dataset that is completely different from the training dataset. This dataset is known as the test dataset. The objective should always be to build a model that generalizes, which means the model should produce similar (but not the same) results, or relatively similar results, on any dataset. This can only be achieved if we evaluate the model on data that is unknown to it.

The model evaluation process requires a metric that can quantify a model's performance. The simplest metric for model evaluation is accuracy. Accuracy is the fraction of predictions...

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