Train and deploy deep learning models

Introduction to the course and an outline of the course sections and content.

Setting up your local machine with the right software.

Creating a google cloud account with 300$ free cloud credit.

Creating a setup for our code on visual studio code.

Installing python packages that we will use during the course from a requirements.txt file that I will provide you.

Testing our setup by running a few commands and some basic import code.

A description of how machine learning projects work from a high level perspective.

Defining our end goal before we start diving into building our machine learning pipeline.

Where to get the dataset that we will be using throughout the course.

How to organize our dataset by splitting the data into folders that contain images of the same class.

Adding a function that can help us visualize random samples from our dataset.

Getting some insights from our dataset by computing mean and medium values of widths and heights of images.

Things that you should consider when building a neural network to solve a specific problem.

Building our neural network using Keras from Tensorflow. You will also leverage the power of transfer learning to build a power CNN based classifier.

Creating data pipelines for augmenting our dataset. You will create generators that can generate data during the training process. All of this using ImageDataGenerator from Keras.

You will create a training function that makes use of the data pipelines and the deep learning model that you created before.

A clean code is a robust code. In this lecture, we will clean our code for robustness and better readability.

Testing our code on a subset of the data to make sure that our pipeline is working properly.

Adding the evaluation part to our code. We will be using our evaluation generator to evaluate our model.

Some final notes about this section.

Reading data during the training is a crucial part of your machine learning pipeline. In this lecture, I will outline our current setup and what we aim for by the time we start training on google cloud platform.

An introduction to google buckets and how to create them.

In this lecture you will upload your dataset to a google cloud bucket that you previously created.

In order to access your data (that you've put on the cloud) from anywhere, you need to have the right credentials. In this lecture, I will show you how to create credentials in a form of a JSON file.

The previous credentials file that we created did not allow us to access the data on our google bucket. In this lecture, I will show you why and how to fix that.

In this lecture, we will add a function that allows us to download data from google bucket.

In this lecture, we will run our training pipeline to see if everything is working as it should.

A brief description of what Docker is and how to use it from a high level perspective.

Modifying our previous code to prepare it for docker.

How to create a Dockerfile for building docker images.

How to run docker containers using our docker image.

Adding arguments to our training script.

Adding all the necessary steps to be able to use GPUs with Docker.

Building our new image after we set up our machine to use GPUs with Docker.

Summary and final notes about this section.

A brief explanation of what cloud computing is and what AI-Platform is.

Enabling google cloud APIs that we need for our training process on the cloud.

In this lecture, you will learn how to push your docker image to google container registry.

Preparing all the necessary setup to run a training job on AI-Platform.

Launching our training job on AI-Platform and checking the logs coming from the machine that is running the training on the cloud.

An introduction to hyperparameters tuning.

Configuring our code to allow AI-Platform to perform hyperparameters tuning automatically.

Building a new docker image with the hyperparameters tuning setup.

Launching the training process with hyperparameters tuning configured.

Saving our trained deep learning models by adding callbacks from Keras.

In this lecture, you will learning how to upload data to google cloud buckets from your python code.

In this lecture, you will learn how to zip and upload your best trained models to google cloud bucket.

In this video, we will configure our code to finally run the training on the complete dataset.

Summary and final notes about this section.