Customising your models with TensorFlow 2
- Offered byCoursera
- Public/Government Institute
Customising your models with TensorFlow 2 at Coursera Overview
Duration | 27 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Customising your models with TensorFlow 2 at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 2 of 3 in the TensorFlow 2 for Deep Learning Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level * Python 3 * Knowledge of general machine learning concepts * Knowledge of the field of deep learning
- Approx. 27 hours to complete
- English Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Customising your models with TensorFlow 2 at Coursera Course details
- Welcome to this course on Customising your models with TensorFlow 2!
- In this course you will deepen your knowledge and skills with TensorFlow, in order to develop fully customised deep learning models and workflows for any application. You will use lower level APIs in TensorFlow to develop complex model architectures, fully customised layers, and a flexible data workflow. You will also expand your knowledge of the TensorFlow APIs to include sequence 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 a custom neural translation 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 follows on directly from the previous course Getting Started with TensorFlow 2. The additional 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, CNN, RNN, ResNet), and concepts such as transfer learning, data augmentation and word embeddings.
Customising your models with TensorFlow 2 at Coursera Curriculum
The Keras functional API
Welcome to Customising your Models with TensorFlow 2
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
About Imperial College & the team
How to be successful in this course
Grading policy
Additional readings & helpful references
Device placement
[Knowledge check] Transfer learning
Data Pipeline
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
TensorFlow Datasets
[Knowledge check] Python generators
Sequence Modelling
Welcome to week 3 - Sequence 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
[Knowledge check] Recurrent neural networks
Model subclassing and custom training loops
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 decorator
Wrap up and introduction to the programming assignment
Capstone Project
Welcome to the Capstone Project
Goodbye video
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