Keras Installation

Keras is a high-level Neural Networks API that provides a simplified approach to building, training, and evaluating sophisticated machine learning models. It is built using Python and can sit on top of other lower-level neural network libraries like TensorFlow, Theano, or CNTK. For our movie-themed tutorial, we will focus on using Keras with TensorFlow backend.

Before we start diving into the nuances of Keras, the first step is obvious - install the Keras library on your system.

Keras Installation

Before installing Keras, it is necessary to ensure that we have a supported Python distribution and the necessary dependencies installed. Keras requires the following:

• Python (3.6 or later)
• numpy (1.9.1 or later)
• scipy (0.14 or later)
• TensorFlow (2.0 or later)
• pyyaml
• HDF5 and h5py (optional, required if saving model weights to disk)
• Pillow (optional, required if using model visualization utilities)

Since we are going to use the TensorFlow backend, make sure TensorFlow is installed on your machine. Let's assume the Python environment is already set up on your system.

Install TensorFlow

Now, if you do not have TensorFlow installed, you can do so using the pip Python package manager. Here's a code snippet showing how to install TensorFlow on your machine using pip:

pip install tensorflow

If you're using a Jupyter notebook, add an exclamation mark at the start

!pip install tensorflow

To confirm successful installation, you can import the TensorFlow library and print its version:

import tensorflow as tf
print(tf.__version__)

Now, for the main event - installing Keras.

Install Keras

You can install Keras using pip, as shown in the following snippet:

pip install keras

Again, if you're using a Jupyter notebook, append a exclamation mark at the start:

!pip install keras

As with TensorFlow, you can confirm the successful installation of Keras by importing it and printing its version like so:

import keras
print(keras.__version__)

Well done! You now have Keras installed, atop a TensorFlow backend, ready to help you spin up neural network models to analyze complex movie-related data.

Now let's consider a simple scenario for using Keras, 'The MI6' - a fictional spy agency, is training a model to predict the box office success of future movies based on past movie data. This data includes information such as genre, budget, cast, director, and past movies' box office revenues.

First, you'll load data into a Keras "model". Models in Keras act as containers, into which you stack layers, defining the structure of your neural network.

from keras.models import Sequential

# Initialize the model object
box_office_model = Sequential()

In our box_office_model, we would add layers of nodes - think of these like the neurons firing in a brain, processing and passing on information. The number and structure of these layers define the complexity of your model.

from keras.layers import Dense

# Add an input layer 
box_office_model.add(Dense(12, activation='relu', input_shape=(10,)))

# Add one hidden layer 
box_office_model.add(Dense(8, activation='relu'))

# Add an output layer 
box_office_model.add(Dense(1, activation='sigmoid'))

Here, we are building a model with one input layer (12 nodes/units), one hidden layer (8 nodes/units), and one output layer (1 node/unit). The 'relu' and 'sigmoid' activations dictate how output is computed and passed onto the next layer.

Once the model is defined, we then compile it, which includes specifying a loss function (how wrong our model's predictions are), optimizer (how the model updates based on the data it sees and its loss function) and other optional metrics we can monitor.

box_office_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

Your Keras installation is complete and you've built a simple model. In the next parts of this tutorial, we will explore how to train the model on movie data, evaluate it, and use it to make predictions.

Remember, Keras is a high-level API wired to make your Machine Learning journey easier. So do not stress too much about the complexity – Keras is here to significantly simplify your Machine Learning modeling process. Happy modeling!