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Getting started with TensorFlow 2
  • Level Professional
  • المدة 26 ساعات hours
  • الطبع بواسطة Imperial College London
  • Offered by

عن

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.

الوحدات

Welcome and introduction

    • 2 Videos
    • 5 Readings
    • 1 Discussion
    • 1 Plugin
Show info about module content

1 Discussions

  • Introduce yourself

2 Videos

  • Introduction to the course
  • Welcome to week 1

5 Readings

  • About Imperial College & the team
  • How to be successful in this course
  • Grading policy
  • Additional readings & helpful references
  • What is TensorFlow?

Hello TensorFlow!

    • 2 Videos
    • 1 Lab
Show info about module content

1 Labs

  • [Coding tutorial] Hello TensorFlow!

2 Videos

  • Hello TensorFlow!
  • [Coding tutorial] Hello TensorFlow!

What's new in TensorFlow 2

    • 2 Videos
Show info about module content

2 Videos

  • What's new in TensorFlow 2
  • Interview with Laurence Moroney

TensorFlow in Google Colab

    • 2 Videos
    • 1 Readings
Show info about module content

2 Videos

  • Introduction to Google Colab
  • [Coding tutorial] Introduction to Google Colab

1 Readings

  • Google Colab resources

TensorFlow documentation

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • TensorFlow documentation

1 Readings

  • TensorFlow documentation

Installing TensorFlow (OPTIONAL)

    • 3 Videos
Show info about module content

3 Videos

  • TensorFlow installation
  • [Coding tutorial] pip installation
  • [Coding tutorial] Running TensorFlow with Docker

Upgrading from TensorFlow 1 (OPTIONAL)

    • 2 Videos
    • 1 Readings
Show info about module content

2 Videos

  • Upgrading from TensorFlow 1
  • [Coding tutorial] Upgrading from TensorFlow 1

1 Readings

  • Upgrade TensorFlow 1.x Notebooks

Introduction to the week - The Sequential model API

    • 2 Videos
    • 1 Assignment
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1 Assignment

  • [Knowledge check] Feedforward and convolutional neural networks

2 Videos

  • Welcome to week 2 - The Sequential model API
  • What is Keras?

Feedforward neural networks

    • 2 Videos
    • 1 Lab
Show info about module content

1 Labs

  • [Coding tutorial] Building a Sequential model

2 Videos

  • Building a Sequential model
  • [Coding tutorial] Building a Sequential model

Convolutional neural networks

    • 2 Videos
    • 1 Lab
Show info about module content

1 Labs

  • [Coding tutorial] Convolutional and pooling layers

2 Videos

  • Convolutional and pooling layers
  • [Coding tutorial] Convolutional and pooling layers

Weight initialisation

    • 1 Lab
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1 Labs

  • [Reading] Adding weight initialisers

Compiling your model

    • 2 Videos
    • 1 Lab
    • 1 Assignment
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1 Assignment

  • [Knowledge check] Optimisers, loss functions and metrics

1 Labs

  • [Coding tutorial] The compile method

2 Videos

  • The compile method
  • [Coding tutorial] The compile method

Optimisers, loss functions and metrics

    • 1 Lab
Show info about module content

1 Labs

  • [Reading] Metrics in Keras

Training your model

    • 2 Videos
    • 1 Lab
Show info about module content

1 Labs

  • [Coding tutorial] The fit method

2 Videos

  • The fit method
  • [Coding tutorial] The fit method

Evaluation and prediction

    • 2 Videos
    • 1 Lab
Show info about module content

1 Labs

  • [Coding tutorial] The evaluate and predict methods

2 Videos

  • The evaluate and predict methods
  • [Coding tutorial] The evaluate and predict methods

Programming Assignment: CNN classifier for the MNIST dataset

    • 1 Videos
    • 1 Lab
    • 1 Programming
Show info about module content

1 Programming

  • CNN classifier for the MNIST dataset

1 Labs

  • CNN classifier for the MNIST dataset

1 Videos

  • Wrap up and introduction to the programming assignment

Introduction to the week - Validation, regularisation and callbacks

    • 2 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • [Knowledge check] Validation and regularisation

2 Videos

  • Welcome to week 3 - Validation, regularisation and callbacks
  • Interview with Andrew Ng

Model validation

    • 2 Videos
    • 1 Lab
Show info about module content

1 Labs

  • [Coding Tutorial] Validation sets

2 Videos

  • Validation sets
  • [Coding Tutorial] Validation sets

Model regularisation

    • 2 Videos
    • 1 Lab
Show info about module content

1 Labs

  • [Coding Tutorial] Model regularisation

2 Videos

  • Model regularisation
  • [Coding Tutorial] Model regularisation

Batch normalisation

    • 1 Lab
Show info about module content

1 Labs

  • [Reading] Batch normalisation layers

Callbacks

    • 2 Videos
    • 1 Lab
Show info about module content

1 Labs

  • [Coding tutorial] Introduction to callbacks

2 Videos

  • Introduction to callbacks
  • [Coding tutorial] Introduction to callbacks

The logs dictionary

    • 1 Lab
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1 Labs

  • [Reading] The logs dictionary

Early stopping and patience

    • 2 Videos
    • 1 Lab
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1 Labs

  • [Coding tutorial] Early stopping and patience

2 Videos

  • Early stopping and patience
  • [Coding tutorial] Early stopping and patience

Additional callbacks

    • 1 Lab
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1 Labs

  • [Reading] Additional callbacks

Programming Assignment: Model validation on the Iris dataset

    • 1 Videos
    • 1 Lab
    • 1 Programming
Show info about module content

1 Programming

  • Model validation on the Iris dataset

1 Labs

  • Model validation on the Iris dataset

1 Videos

  • Wrap up and introduction to the programming assignment

Saving and loading model weights

    • 3 Videos
    • 1 Lab
Show info about module content

1 Labs

  • [Coding tutorial] Saving and loading model weights

3 Videos

  • Welcome to week 4 - Saving and loading models
  • Saving and loading model weights
  • [Coding tutorial] Saving and loading model weights

Explanation of saved files

    • 1 Lab
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1 Labs

  • [Reading] Explanation of saved files

Model saving criteria

    • 2 Videos
    • 1 Lab
Show info about module content

1 Labs

  • [Coding tutorial] Model saving criteria

2 Videos

  • Model saving criteria
  • [Coding tutorial] Model saving criteria

Saving the entire model

    • 2 Videos
    • 1 Lab
Show info about module content

1 Labs

  • [Coding tutorial] Saving the entire model

2 Videos

  • Saving the entire model
  • [Coding tutorial] Saving the entire model

Saving model architecture only

    • 1 Lab
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1 Labs

  • [Reading] Saving model architecture only

Loading pre-trained Keras models

    • 2 Videos
    • 1 Lab
Show info about module content

1 Labs

  • [Coding tutorial] Loading pre-trained Keras models

2 Videos

  • Loading pre-trained Keras models
  • [Coding tutorial] Loading pre-trained Keras models

TensorFlow Hub modules

    • 2 Videos
    • 1 Lab
Show info about module content

1 Labs

  • [Coding tutorial] TensorFlow Hub modules

2 Videos

  • TensorFlow Hub modules
  • [Coding tutorial] TensorFlow Hub modules

Programming Assignment: Saving and loading models

    • 1 Videos
    • 1 Lab
    • 1 Programming
Show info about module content

1 Programming

  • Saving and loading models

1 Labs

  • Saving and loading models

1 Videos

  • Wrap up and introduction to the programming assignment

Image classifier for the SVHN dataset

    • 2 Videos
    • 1 PeerReview
    • 1 Plugin
    • 1 Lab
Show info about module content

1 Peer Review

  • Capstone Project

1 Labs

  • Capstone Project

2 Videos

  • Welcome to the Capstone Project
  • Goodbye video

Auto Summary

Dive into "Getting started with TensorFlow 2," a comprehensive Data Science & AI course led by Coursera. Perfect for both beginners and experienced users, this course covers building, training, and evaluating deep learning models using TensorFlow's Sequential API. With hands-on tutorials, graded assignments, and a Capstone Project, you'll develop a robust image classifier from scratch. Essential for those proficient in Python and familiar with machine learning concepts, the course spans 1560 minutes and offers Starter, Professional, and Paid subscription options. Join now to master TensorFlow 2!

Dr Kevin Webster
Instructor

Dr Kevin Webster