Developing with S3: AWS with Python and Boto3 Series

This is the code repository for Developing with S3: AWS with Python and Boto3 Series, published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.

About the Video Course

If you want to learn the ins-and-outs of S3 and how to implement solutions with it, this course is for you. S3 is the Simple Storage Service from AWS and offers many great features you can make use of in your applications and even in your daily life! You can use S3 to host your memories, documents, important files, videos and even your own website! This course starts by showing you what you need to install and set up on your computer to work with S3. There are two different sections for Windows and MacOS users. These sections are basically identical and show how you can prepare your computer environment to be ready to work with S3! We show you how to install Python and Boto3 and configure your environments for these tools. We also show you how you can create your own AWS account step-by-step and you'll be ready to work with AWS in no time!. When you've finished preparing your environment to work AWS with Python and Boto3, you'll start implementing your own solutions for AWS. We'll implement our S3 static website hosting from scratch. We'll design a simple website and configure it as a website inside our bucket. Once we have our website up-and-running and accessible via a URL, we'll move on to Route53 to configure our own domain name or DNS to route traffic to our S3-hosted website from our own custom domain! All codes and supporting files for this course are available at: https://github.com/PacktPublishing/Developing-with-S3-AWS-with-Python-and-Boto3-Series

What You Will Learn

Develop a deep learning network from scratch with Keras using Python to solve a practical problem of classifying the traffic signs on the road.
Introduction to Computer Vision & Deep Learning.
Setup and develop an environment with VM or Docker. Ipython and Jupyter notebook.
Activation functions, Forward propagation, backward propagation.
How to use Tensorflow backend. Hands-on coding with me.
Tensorboard and intuitions of filters and hyper-parameters.
Deploy and evaluate for other real-world applications. Future work and readings!
Neural network style transfer - Image style translation and generation
Game AI - Running game agents using Deep Q network

Assumed Knowledge

To fully benefit from the coverage included in this course, you will need:
Beginners on AWS who wants to put their theory in practiceAWS Cloud Architect Associate Exam Preppers who wants to practice their theories with real projectsWho wants to learn how to Host Static Websites with S3!Who wants to learn Multi-Part Upload!Who wants to learn how to work with S3Who wants to learn how to implement Infrastructure-as-a-Code or IaaS on AWSWho wants to learn how to develop infrastructures on AWS using PythonWho wants to learn AWS Python API or namely Boto3

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