Chapter 5,
Wrapping Up, wraps up our look at TensorFlow. We'll revisit our TensorFlow models for font classification, and review
Getting Started with Machine Learning
Building a deep learning model is similar to building any other machine learning model. It involves some high level steps that need to be taken in order to design and redesign the model. The main steps in building a general machine learning model include:
- Splitting the data into train and test data
- Train a machine learning model
- Evaluate the model on the test data
In addition to the above steps, there are some specific steps that need to be taken to build a machine learning model in Python:
- Importing required libraries in Python
- Importing the data
- Cleaning the data
- Splitting the data into train and test data
- Create a machine learning model
- Train/fit the model
- Evaluate the performance of the trained model
Setting up the environment
we use Google colab environment to run our Jupyter notebooks. it also has GPUs, but we start with a simple model, and no need to use GPU. GPUs are blah blah and make running the model much faster. If you are interested, go to this reference to learn about how to use GPUs when running machine learning model.
the notebook is available on Github, a platform to share code with collaborators privately or publicly. THIS IS THE GITHUB LINK
House Price Prediction Using Keras
We will walk you through a hands-on example to apply deep learning on a real-world data set. For this hands-on exercise, we will be using Jupyter notebooks, Keras and TensorFlow.
Unlike most deep learning tutorials, we won’t be using handwritten digits classification from MNIST. We will be using a real estate data to walk you through an applied example in deep learning.