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Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery
English	Self Study	Video and Text Based

Course Overview

The intermediate-level Customizing Your Models with TensorFlow 2 online course by Coursera builds a strong knowledge base for TensorFlow. You will learn to develop customized deep-learning models and workflows for any application. Additionally, the certification course will teach you how to build complex model architecture using lower-level APIs in TensorFlow.

Towards the course end, you will be working on a Capstone Project to apply the concepts you acquired over the course duration. You will custom-build a neutral translation model from scratch. The Customising Your Models with TensorFlow 2 programme, is the second course of the three-part Specialization – TensorFlow 2 for Deep Learning Specialisation. Moreover, the world-renowned institute, Imperial College of London, offers the online course, while Dr Kevin Webster instructs it.

You will learn via pre-recorded lectures and development experience, along with the coding assignments, which offer practical and field-relevant practice. A graduate teaching assistant will guide you through the projects. Most importantly, the Customising Your Models with TensorFlow 2 online course is entirely self-paced with flexible deadlines to suit your comfort.

The Highlights

Shareable electronic certificate
100% online learning
Deep Learning
Flexibles deadlines
Intermediate-level course
Video transcripts
Approx. 27 hours to complete
Lectures in English
Offered by Imperial College London

Programme Offerings

Specialisation Course
Flexible Deadlines
100% online course
Intermediate-level Course
Shareable Certificate
practice quizzes
Capstone Project

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	Coursera

The Customising Your Models with TensorFlow 2 fee is Rs. 4,023 per month
Initially, the programme is available to try free for 7 days.

The Customising Your Models with TensorFlow 2 fee details:

Head	Amount
1 Month	Rs. 4,023
3 Months	Rs. 8,046
6 Months	Rs. 12,069

Eligibility Criteria

Before you apply for the Customising your models with TensorFlow 2 course by Coursera, you need first to complete the Getting started with TensorFlow course. Additionally, candidates need to be proficient in Python programming (Python 3.0) and should have a basic understanding of the Machine Learning fundamentals.

Candidates should also have a working knowledge of Deep Learning concepts, including Transfer Learning, Data Augmentation, Model Architecture, among others.

What you will learn

Knowledge of deep learning

After completing the Coursera Customising Your Models with TensorFlow 2 online training, you will get an idea about the following:

Know how to apple the functional API to create better flexible model architectures with many outputs and inputs
How to apply tools from Keras and TensorFlow to implement data pipelines
Process data sequence and understand sequence modelling tasks
Apply automatic differentiation tools to implement custom training loops
Exploiting model and layer sub-classing API to develop entirely flexible model architectures

Who it is for

Software EngineerData Engineer

Admission Details

Enrol for the Customising your models with TensorFlow 2 programme by Coursera by simply following the steps mentioned below:

Visit the course page.
Type in ‘Customising your models with TensorFlow 2’ in the search bar to locate the course.
Choose the “Join for Free” option on the top right corner.
You can create your account on Coursera or log in through Google, Facebook, or Apple. If already have a Coursera account, log in and enrol for the course.

Application Details

Applying for the Customising of your models with TensorFlow 2 online course is quite simple. You do not have to fill out any form. Just sign up and enrol for the certification course, and you will get the course material.

The Syllabus

Videos

Welcome to Customising your Models with TensorFlow
Interview with Laurence Moroney
The Keras functional API
Multiple inputs and outputs
[Coding tutorial] Multiple inputs and outputs
Variables
Tensors
[Coding tutorial] Variables and Tensors
Accessing layer Variables
Accessing layer Tensors
[Coding tutorial] Accessing model layers
Freezing layers
[Coding tutorial] Freezing layers
Wrap up and introduction to the programming assignment

Readings

About Imperial College & the team
How to be successful in this course
Grading policy
Additional readings & helpful references
Device placement

Practice Exercise

[Knowledge check] Transfer learning

Videos

Welcome to Week 2 - Data Pipeline
Keras datasets
[Coding tutorial] Keras datasets
Dataset generators
[Coding tutorial] Dataset generators
Keras image data augmentation
[Coding tutorial] Keras image data augmentation
The Dataset class
[Coding tutorial] The Dataset class
Training with Datasets
[Coding tutorial] Training with Datasets
Wrap up and introduction to the programming assignment

Readings

TensorFlow Datasets

Practice Exercise

[Knowledge check] Python generators

Videos

Welcome to Week 3 - Sequential Modelling
Interview with Doug Kelly
Preprocessing sequence data
[Coding tutorial] The IMDB dataset
[Coding tutorial] Padding and masking sequence data
The Embedding layer
[Coding tutorial] The Embedding layer
[Coding tutorial] The Embedding Projector
Recurrent neural network layers
[Coding tutorial] Recurrent neural network layers
Stacked RNNs and the Bidirectional wrapper
[Coding tutorial] Stacked RNNs and the Bidirectional wrapper
Wrap up and introduction to the programming assignment

Practice Exercise

[Knowledge check] Recurrent neural networks

Videos

Welcome to week 4 - Model subclassing and custom training loops
Model subclassing
[Coding tutorial] Model subclassing
Custom layers
[Coding tutorial] Custom layers
Automatic differentiation
[Coding tutorial] Automatic differentiation
Custom training loops
[Coding tutorial] Custom training loops
tf.function decorator
[Coding tutorial] tf.function decorator5m
Wrap up and introduction to the programming assignment

Videos

Welcome to Capstone Project
Goodbye video

Instructors

Imperial College, London Frequently Asked Questions (FAQ's)

1: How do I enrol for the entire Specialization?

You can simply use the same steps mentioned above in the ‘admission details’ section and enrol for the TensorFlow 2 for Deep Learning Specialization.

2: Is financial aid available for the TensorFlow 2 for Deep Learning Specialization as well?

Yes, financial assistance is available. All you have got to do is fill out your application form by mentioning the requested details, and you will be notified within 15 days.

3: Is the course 100% online or will I have to attend something in person?

You do not have to attend anything in person. This is an entirely online course where you can access all the required course material via the web. Moreover, you will receive an electronic certificate.

4: Are the prerequisites necessary?

Yes, since the Customising your models with TensorFlow 2 online course is an intermediate-level course, it requires general knowledge and experience in the fields mentioned above. Additionally, this course is the second part of a three-course Specialization, which is why the completion of the first course, Getting Started with TensorFlow 2, is essential before you apply for this one.

5: Should I subscribe to the Specialization?

You can get access to all the three courses in TensorFlow 2 for Deep Learning Specialization. You will also receive a certificate after you complete all the required modules in the courses and it will be added to your accomplishments page. You can print this certificate or add it to your LinkedIn account.