This repository contains code for training an image recognition deep learning model to recognize different types of food. It also contains code about how to dockerize your code so that you can run the training on google cloud platform.
This code is part of my online course that you can find HERE.
This course will take you through the steps that a machine learning engineer would take to train and deploy a deep learning model. We will start the course by defining an end goal that we want to achieve. Then, we will download a dataset that will help us achieve that goal. We will build a Convolutional Neural Network using Tensorflow 2 with Keras and then we will train this network on Google AI Platform. After saving the best trained model, we will deploy it as a web app using Flask and Google Cloud Run. Throughout the course, we will be using Docker to containerize our code.
The goal of this course is to make you proficient in training and deploying a deep learning model that was trained using the Tensorflow 2 library.
The course will be a great introduction to Google Cloud Platform if you haven't used it before. I actually made the course in such a way that even if you never used cloud computing services before, you will still be able to follow along.
I try to deconstruct the difficult concepts and make them easily digestible. My goal is to help you learn these skills and become able to apply them to your real life projects. Whether that be for your actual job or for your side projects.
Before creating this course, I actually faced difficulties finding a good guide that shows you how to go from data to building your model to training it on the cloud and finally to deploying it in a form of a web app that I can share with friends, colleagues or clients.
This was the motivation behind creating this course. To simply make your life easier when trying to navigate through the field of deep learning and actually making something that you share with others.
Many introductory courses just show you how to train a deep neural network. But in real life, that's only a small part of an AI project. In this course, I try to show you a global picture of how Artificial Intelligence projects are made.