Getting started with TensorFlow 2
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Getting started with TensorFlow 2
This course is part of TensorFlow 2 for Deep Learning Specialization
Instructor: Dr Kevin Webster
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There are 5 modules in 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.
TensorFlow is one of the most popular libraries for deep learning, and it’s widely used today amongst researchers and professionals at all levels. In this week, you will get started with using TensorFlow on the Coursera platform and familiarise yourself with the course structure. You will also learn about some helpful resources when developing deep learning models in TensorFlow, including Google Colab. This week is really about getting everything set up, ready for diving into TensorFlow in the following week of the course.
What's included
14 videos8 readings1 discussion prompt1 ungraded lab1 plugin
14 videos•Total 59 minutes
- Introduction to the course•3 minutes
- Welcome to week 1•1 minute
- Hello TensorFlow!•1 minute
- [Coding tutorial] Hello TensorFlow!•2 minutes
- What's new in TensorFlow 2•4 minutes
- Interview with Laurence Moroney•6 minutes
- Introduction to Google Colab•3 minutes
- [Coding tutorial] Introduction to Google Colab•8 minutes
- TensorFlow documentation•3 minutes
- TensorFlow installation•3 minutes
- [Coding tutorial] pip installation•4 minutes
- [Coding tutorial] Running TensorFlow with Docker•10 minutes
- Upgrading from TensorFlow 1•3 minutes
- [Coding tutorial] Upgrading from TensorFlow 1•6 minutes
8 readings•Total 80 minutes
- About Imperial College & the team•10 minutes
- How to be successful in this course•10 minutes
- Grading policy•10 minutes
- Additional readings & helpful references•10 minutes
- What is TensorFlow?•10 minutes
- Google Colab resources•10 minutes
- TensorFlow documentation•10 minutes
- Upgrade TensorFlow 1.x Notebooks•10 minutes
1 discussion prompt•Total 10 minutes
- Introduce yourself•10 minutes
1 ungraded lab•Total 15 minutes
- [Coding tutorial] Hello TensorFlow!•15 minutes
1 plugin•Total 15 minutes
- Pre-Course Survey•15 minutes
There are multiple ways to build and apply deep learning models in TensorFlow, from high-level, quick and easy-to-use APIs, to low-level operations. In this week you will learn to use the high-level Keras API for quickly building, training, evaluating and predicting from deep learning models. The programming assignment for this week will give you the opportunity to put all this into practice and develop an image classification model from scratch on the MNIST dataset of handwritten images.
What's included
13 videos2 assignments1 programming assignment8 ungraded labs
13 videos•Total 59 minutes
- Welcome to week 2 - The Sequential model API•2 minutes
- What is Keras?•2 minutes
- Building a Sequential model•5 minutes
- [Coding tutorial] Building a Sequential model•5 minutes
- Convolutional and pooling layers•5 minutes
- [Coding tutorial] Convolutional and pooling layers•6 minutes
- The compile method•6 minutes
- [Coding tutorial] The compile method•5 minutes
- The fit method•4 minutes
- [Coding tutorial] The fit method•8 minutes
- The evaluate and predict methods•6 minutes
- [Coding tutorial] The evaluate and predict methods•5 minutes
- Wrap up and introduction to the programming assignment•1 minute
2 assignments•Total 30 minutes
- [Knowledge check] Feedforward and convolutional neural networks•15 minutes
- [Knowledge check] Optimisers, loss functions and metrics•15 minutes
1 programming assignment•Total 60 minutes
- CNN classifier for the MNIST dataset•60 minutes
8 ungraded labs•Total 260 minutes
- [Coding tutorial] Building a Sequential model•20 minutes
- [Coding tutorial] Convolutional and pooling layers•20 minutes
- [Reading] Adding weight initialisers•20 minutes
- [Coding tutorial] The compile method•20 minutes
- [Reading] Metrics in Keras•20 minutes
- [Coding tutorial] The fit method•20 minutes
- [Coding tutorial] The evaluate and predict methods•20 minutes
- CNN classifier for the MNIST dataset•120 minutes
Model validation and selection is an essential part of developing any machine learning model development to help prevent overfitting and improve generalisation. In this week you will learn how to use a validation dataset in a training run and apply regularisation techniques to your model. You will also learn how to use callbacks to monitor performance and perform actions according to specified criteria. In the programming assignment for this week you will put model validation and regularisation into practice on the well-known Iris dataset.
What's included
11 videos1 assignment1 programming assignment8 ungraded labs
11 videos•Total 60 minutes
- Welcome to week 3 - Validation, regularisation and callbacks•2 minutes
- Interview with Andrew Ng•7 minutes
- Validation sets•4 minutes
- [Coding Tutorial] Validation sets•10 minutes
- Model regularisation•7 minutes
- [Coding Tutorial] Model regularisation•5 minutes
- Introduction to callbacks•6 minutes
- [Coding tutorial] Introduction to callbacks•7 minutes
- Early stopping and patience•6 minutes
- [Coding tutorial] Early stopping and patience•6 minutes
- Wrap up and introduction to the programming assignment•1 minute
1 assignment•Total 15 minutes
- [Knowledge check] Validation and regularisation•15 minutes
1 programming assignment•Total 60 minutes
- Model validation on the Iris dataset•60 minutes
8 ungraded labs•Total 260 minutes
- [Coding Tutorial] Validation sets•20 minutes
- [Coding Tutorial] Model regularisation•20 minutes
- [Reading] Batch normalisation layers•20 minutes
- [Coding tutorial] Introduction to callbacks•20 minutes
- [Reading] The logs dictionary•20 minutes
- [Coding tutorial] Early stopping and patience•20 minutes
- [Reading] Additional callbacks•20 minutes
- Model validation on the Iris dataset•120 minutes
As part of your deep learning model development, you will need to be able to save and load TensorFlow models, possibly according to certain criteria you want to specify. In this week you will learn how to use callbacks to save models, manual saving and loading, and options that are available when saving models, including saving weights only. In addition, you will practice loading and using pre-trained deep learning models. In the programming assignment for this week you will write flexible model saving and loading implementations for a model trained on satellite images.
What's included
12 videos1 programming assignment8 ungraded labs
12 videos•Total 74 minutes
- Welcome to week 4 - Saving and loading models•2 minutes
- Saving and loading model weights•7 minutes
- [Coding tutorial] Saving and loading model weights•10 minutes
- Model saving criteria•5 minutes
- [Coding tutorial] Model saving criteria•12 minutes
- Saving the entire model•5 minutes
- [Coding tutorial] Saving the entire model•9 minutes
- Loading pre-trained Keras models•6 minutes
- [Coding tutorial] Loading pre-trained Keras models•8 minutes
- TensorFlow Hub modules•2 minutes
- [Coding tutorial] TensorFlow Hub modules•8 minutes
- Wrap up and introduction to the programming assignment•1 minute
1 programming assignment•Total 60 minutes
- Saving and loading models•60 minutes
8 ungraded labs•Total 260 minutes
- [Coding tutorial] Saving and loading model weights•20 minutes
- [Reading] Explanation of saved files•20 minutes
- [Coding tutorial] Model saving criteria•20 minutes
- [Coding tutorial] Saving the entire model•20 minutes
- [Reading] Saving model architecture only•20 minutes
- [Coding tutorial] Loading pre-trained Keras models•20 minutes
- [Coding tutorial] TensorFlow Hub modules•20 minutes
- Saving and loading models•120 minutes
In this course you have learned an end-to-end workflow for developing deep learning models in Tensorflow. The Capstone Project gives you the opportunity to bring all of your knowledge together to develop a deep learning classifier on a labelled image dataset of street view house numbers.
What's included
2 videos1 peer review1 ungraded lab1 plugin
2 videos•Total 2 minutes
- Welcome to the Capstone Project•1 minute
- Goodbye video•1 minute
1 peer review•Total 60 minutes
- Capstone Project•60 minutes
1 ungraded lab•Total 120 minutes
- Capstone Project•120 minutes
1 plugin•Total 15 minutes
- Post-Course Survey•15 minutes
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Reviewed on May 28, 2023
Amazing course, one of the best so far. The course discusses the Tensorflow 2 framework in a very detailed and practical way. Thanks for the opportunity!
Reviewed on Jul 22, 2023
Awesome course for the students who wanted to start the TensorFlow. Instructors are best, explained the topic in a simple word using appropriate practical examples.
Reviewed on Jul 14, 2021
Really good practical course on image analysis with TF. Make sure you know the basics ahead as the main concepts are not explained, just put into practice.
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