Applied Deep Learning with Keras Solve complex real life problems with the simplicity of Keras 1st Edition by Ritesh Bhagwat, Mahla Abdolahnejad, Matthew Moocarme ISBN 1838555072 9781838555078 by Bhagwat, Ritesh;abdolahnejad, Mahla;moocarme, Matthew 9781838555078, 1838555072 instant download after payment.

Applied Deep Learning with Keras Solve complex real life problems with the simplicity of Keras 1st Edition by Ritesh Bhagwat, Mahla Abdolahnejad, Matthew Moocarme - Ebook PDF Instant Download/Delivery: 1838555072, 9781838555078
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ISBN 10: 1838555072 
ISBN 13: 9781838555078
Author: Ritesh Bhagwat, Mahla Abdolahnejad, Matthew Moocarme

Take your neural networks to a whole new level with the simplicity and modularity of Keras, the most commonly used high-level neural networks API. Key Features Solve complex machine learning problems with precision Evaluate, tweak, and improve your deep learning models and solutions Use different types of neural networks to solve real-world problems Book Description 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. What you will 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 Who this book is for 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 book useful. Prior experience of Python programming and experience with statistics and logistic regression will help you get the most out of this book. Although not necessary, some familiarity with the scikit-learn library will be an added bonus.

Applied Deep Learning with Keras Solve complex real life problems with the simplicity of Keras 1st Table of contents:

Chapter 1: Introduction to Machine Learning with Keras
Activity 1: Adding Regularization to the Model
Chapter 2: Machine Learning versus Deep Learning
Activity 2: Creating a Logistic Regression Model Using Keras
Chapter 3: Deep Learning with Keras
Activity 3: Building a Single-Layer Neural Network for Performing Binary Classification
Activity 4: Diabetes Diagnosis with Neural Networks
Chapter 4: Evaluate Your Model with Cross-Validation with Keras Wrappers
Activity 5: Model Evaluation Using Cross-Validation for a Diabetes Diagnosis Classifier
Activity 6: Model Selection Using Cross-Validation for the Diabetes Diagnosis Classifier
Activity 7: Model Selection Using Cross-validation on the Boston House Prices Dataset
Chapter 5: Improving Model Accuracy
Activity 8: Weight Regularization on a Diabetes Diagnosis Classifier
Activity 9: Dropout Regularization on Boston House Prices Dataset
Activity 10: Hyperparameter Tuning on the Diabetes Diagnosis Classifier
Chapter 6: Model Evaluation
Activity 11: Computing Accuracy and Null Accuracy of Neural Network When We Change the Train/Test Split
Activity 12: Derive and Compute Metrics Based on the Confusion Matrix
Chapter 7: Computer Vision with Convolutional Neural Networks
Activity 13: Amending our Model with Multiple Layers and Use of SoftMax
Activity 14: Classify a New Image
Chapter 8: Transfer Learning and Pre-trained Models
Activity 15: Use the VGG16 Network to Train a Deep Learning Network to Identify Images
Activity 16: Image Classification with ResNet
Chapter 9: Sequential Modeling with Recurrent Neural Networks
Activity 17: Predict the Trend of Microsoft’s Stock Price Using an LSTM with 50 Units (Neurons)
Activity 18: Predicting Microsoft’s stock price with added regularization
Activity 19: Predicting the Trend of Microsoft’s Stock Price Using LSTM with 100 Units

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