To get the most out of this book

1. I assume that you're already experienced with more traditional data science and predictive modeling techniques such as Linear/Logistic Regression and Random Forest. If this is your first experience with machine learning, this may be a little difficult for you.
2. I also assume that you have at least some experience in programming with Python, or at least another programming language such as Java or C++.
3. Deep learning is computationally intensive, and some of the models we build here require an NVIDIA GPU to run in a reasonable amount of time. If you don't own a fast GPU, you may wish to use a GPU-based cloud instance on either Amazon Web Services or Google Cloud Platform.

Download the example code files

You can download the example code files for this book from your account at www.packtpub.com. If you purchased this book elsewhere, you can visit www.packtpub.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

1. Log in or register at www.packtpub.com.
2. Select the SUPPORT tab.
3. Click on Code Downloads & Errata.
4. Enter the name of the book in the Search box and follow the onscreen instructions.

Once the file is downloaded, make sure that you unzip or extract the folder using the latest version of any of these:

• WinRAR/7-Zip for Windows
• Zipeg/iZip/UnRarX for macOS
• 7-Zip/PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Deep-Learning-Quick-Reference. We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "That's exactly what the ModelCheckpoint callback does for us."

A block of code is set as follows:

def binary_accuracy(y_true, y_pred):
    return K.mean(K.equal(y_true, K.round(y_pred)), axis=-1)

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

def build_network(input_features=None):
    inputs = Input(shape=(input_features,), name="input")
    x = Dense(32, activation='relu', name="hidden1")(inputs)
    x = Dense(32, activation='relu', name="hidden2")(x)
    x = Dense(32, activation='relu', name="hidden3")(x)
    x = Dense(32, activation='relu', name="hidden4")(x)
    x = Dense(16, activation='relu', name="hidden5")(x)
    prediction = Dense(1, activation='linear', name="final")(x)
    model = Model(inputs=inputs, outputs=prediction)
    model.compile(optimizer='adam', loss='mean_absolute_error')
    return model

Any command-line input or output is written as follows:

model-weights.00-0.971304.hdf5
model-weights.02-0.977391.hdf5
model-weights.05-0.985217.hdf5

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Select System info from the Administration panel."

Warnings or important notes appear like this.

Tips and tricks appear like this.