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Getting started with TensorFlow 2

Approx. 26 hours to complete

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Course Summary

This course is an introduction to TensorFlow 2.0, a popular open-source machine learning framework. In this course, you will learn the basics of TensorFlow and how to build deep learning models using TensorFlow 2.0.

Key Learning Points

Learn the basics of TensorFlow 2.0
Build deep learning models using TensorFlow 2.0
Get hands-on experience with TensorFlow 2.0

Learning Outcomes

Understand the basics of TensorFlow 2.0
Build simple and complex deep learning models using TensorFlow 2.0
Apply TensorFlow 2.0 to real-world problems

Prerequisites or good to have knowledge before taking this course

Familiarity with Python programming language
Basic knowledge of machine learning concepts

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced

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Machine Learning with TensorFlow on Google Cloud Platform
Applied Data Science with Python

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Description

Welcome to this course on Getting started with TensorFlow 2!

In this course you will learn a complete end-to-end workflow for developing deep learning models with Tensorflow, from building, training, evaluating and predicting with models using the Sequential API, validating your models and including regularisation, implementing callbacks, and saving and loading models. You will put concepts that you learn about into practice straight away in practical, hands-on coding tutorials, which you will be guided through by a graduate teaching assistant. In addition there is a series of automatically graded programming assignments for you to consolidate your skills. At the end of the course, you will bring many of the concepts together in a Capstone Project, where you will develop an image classifier deep learning model from scratch. Tensorflow is an open source machine library, and is one of the most widely used frameworks for deep learning. The release of Tensorflow 2 marks a step change in the product development, with a central focus on ease of use for all users, from beginner to advanced level. This course is intended for both users who are completely new to Tensorflow, as well as users with experience in Tensorflow 1.x. The prerequisite knowledge required in order to be successful in this course is proficiency in the python programming language, (this course uses python 3), knowledge of general machine learning concepts (such as overfitting/underfitting, supervised learning tasks, validation, regularisation and model selection), and a working knowledge of the field of deep learning, including typical model architectures (MLP/feedforward and convolutional neural networks), activation functions, output layers, and optimisation.

Outline

Introduction to TensorFlow
Introduction to the course
Welcome to week 1
Hello TensorFlow!
[Coding tutorial] Hello TensorFlow!
What's new in TensorFlow 2
Interview with Laurence Moroney
Introduction to Google Colab
[Coding tutorial] Introduction to Google Colab
TensorFlow documentation
TensorFlow installation
[Coding tutorial] pip installation
[Coding tutorial] Running TensorFlow with Docker
Upgrading from TensorFlow 1
[Coding tutorial] Upgrading from TensorFlow 1
About Imperial College & the team
How to be successful in this course
Grading policy
Additional readings & helpful references
What is TensorFlow?
Google Colab resources
TensorFlow documentation
Upgrade TensorFlow 1.x Notebooks

The Sequential model API
Welcome to week 2 - The Sequential model API
What is Keras?
Building a Sequential model
[Coding tutorial] Building a Sequential model
Convolutional and pooling layers
[Coding tutorial] Convolutional and pooling layers
The compile method
[Coding tutorial] The compile method
The fit method
[Coding tutorial] The fit method
The evaluate and predict methods
[Coding tutorial] The evaluate and predict methods
Wrap up and introduction to the programming assignment
[Knowledge check] Feedforward and convolutional neural networks
[Knowledge check] Optimisers, loss functions and metrics

Validation, regularisation and callbacks
Welcome to week 3 - Validation, regularisation and callbacks
Interview with Andrew Ng
Validation sets
[Coding Tutorial] Validation sets
Model regularisation
[Coding Tutorial] Model regularisation
Introduction to callbacks
[Coding tutorial] Introduction to callbacks
Early stopping and patience
[Coding tutorial] Early stopping and patience
Wrap up and introduction to the programming assignment
[Knowledge check] Validation and regularisation

Saving and loading models
Welcome to week 4 - Saving and loading models
Saving and loading model weights
[Coding tutorial] Saving and loading model weights
Model saving criteria
[Coding tutorial] Model saving criteria
Saving the entire model
[Coding tutorial] Saving the entire model
Loading pre-trained Keras models
[Coding tutorial] Loading pre-trained Keras models
TensorFlow Hub modules
[Coding tutorial] TensorFlow Hub modules
Wrap up and introduction to the programming assignment

Capstone Project
Welcome to the Capstone Project
Goodbye video

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