.

💡 Deep Learning Nanodegree Porjects

.

.

Here are the five projects, listed for convenience:

==============================================================

1. Predicting Bike-Sharing Data
2. Dog Breed Classifier
3. Generate TV Scripts
4. Generate Faces
5. Deploy a Sentiment Analysis Model

Prerequisites

We've designed this program such that you only require the following prerequisite knowledge:

Required

• Intermediate Python experience.

Optional

• Multivariable Calculus and Linear Algebra if possible.

That being said, we've included a lot of the detailed mathematics for those of you who do want to go in depth and understand the theory behind these concepts. Such content is optional and shouldn't prevent you from doing the projects. However, it is encouraged for a theoretical understanding.

If you haven't used Numpy, Pandas, or Matplotlib before, please take our Intro to Data Analysis course. These are the main tools for working with and visualizing data in Python, so you'll need some experience with them.

---

Anaconda

Is a distribution of packages built for data science. It comes with conda, a package and environment manager. You'll be using conda to create environments for isolating your projects that use different versions of Python and/or different packages. You'll also use it to install, uninstall, and update packages in your environments. Using Anaconda has made my life working with data much more pleasant.

Deploy a Sentiment Analysis Model

Deploying a Model in SageMaker In this lesson, we're going to take a look at how we can use a model that has been created in SageMaker. We will do this by first deploying our model. For us, this means using SageMaker's functionality to create an endpoint that will be used as a way to send data to our model.

Recall, from the first lesson in this section, that an endpoint is basically a way to allow a model and an application to communicate. An application, such as a web app, will be responsible for accepting user input data, and through an endpoint we can send that data to our model, which will produce predictions that can be sent back to our application!