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  • Book Overview & Buying The Deep Learning with Keras Workshop
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The Deep Learning with Keras Workshop

The Deep Learning with Keras Workshop

By : Matthew Moocarme, Mahla Abdolahnejad , Ritesh Bhagwat
4.6 (8)
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The Deep Learning with Keras Workshop

The Deep Learning with Keras Workshop

4.6 (8)
By: Matthew Moocarme, Mahla Abdolahnejad , Ritesh Bhagwat

Overview of this book

New experiences can be intimidating, but not this one! This beginner’s guide to deep learning is here to help you explore deep learning from scratch with Keras, and be on your way to training your first ever neural networks. What sets Keras apart from other deep learning frameworks is its simplicity. With over two hundred thousand users, Keras has a stronger adoption in industry and the research community than any other deep learning framework. The Deep Learning with Keras Workshop starts by introducing you to the fundamental concepts of machine learning using the scikit-learn package. After learning how to perform the linear transformations that are necessary for building neural networks, you'll build your first neural network with the Keras library. As you advance, you'll learn how to build multi-layer neural networks and recognize when your model is underfitting or overfitting to the training data. With the help of practical exercises, you’ll learn to use cross-validation techniques to evaluate your models and then choose the optimal hyperparameters to fine-tune their performance. Finally, you’ll explore recurrent neural networks and learn how to train them to predict values in sequential data. By the end of this book, you'll have developed the skills you need to confidently train your own neural network models.
Table of Contents (11 chapters)
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Preface

Data Preprocessing

To fit models to the data, it must be represented in numerical format since the mathematics used in all machine learning algorithms only works on matrices of numbers (you cannot perform linear algebra on an image). This will be one goal of this section: to learn how to encode all the features into numerical representations. For example, in binary text, values that contain one of two possible values may be represented as zeros or ones. An example of this can be seen in the following diagram. Since there are only two possible values, the value 0 is assumed to be a cat and the value 1 is assumed to be a dog.

We can also rename the column for interpretation:

Figure 1.6: A numerical encoding of binary text values

Another goal will be to appropriately represent the data in numerical format - by appropriately, we mean that we want to encode relevant information numerically through the distribution of numbers. For example, one method to encode...

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The Deep Learning with Keras Workshop
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