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Getting started with TensorFlow 2 

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Getting started with TensorFlow 2
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Overview

Duration

26 hours

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Total fee

Free

Mode of learning

Online

Difficulty level

Intermediate

Official Website

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Credential

Certificate

Getting started with TensorFlow 2
 at 
Coursera 
Highlights

  • Shareable Certificate Earn a Certificate upon completion
  • 100% online Start instantly and learn at your own schedule.
  • Course 1 of 3 in the TensorFlow 2 for Deep Learning Specialization
  • Flexible deadlines Reset deadlines in accordance to your schedule.
  • Intermediate Level
  • Approx. 26 hours to complete
  • English Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
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Getting started with TensorFlow 2
 at 
Coursera 
Course details

Skills you will learn
More about this course
  • 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.
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Getting started with TensorFlow 2
 at 
Coursera 
Curriculum

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

Getting started with TensorFlow 2
 at 
Coursera 
Admission Process

    Important Dates

    May 25, 2024
    Course Commencement Date

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