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This course is part of TensorFlow 2 for Deep Learning Specialization
Instructor: Dr Kevin Webster
14,215 already enrolled
Included with
(191 reviews)
Recommended experience
Intermediate level
* Python 3
* Knowledge of general machine learning concepts
* Knowledge of the field of deep learning
(191 reviews)
Recommended experience
Intermediate level
* Python 3
* Knowledge of general machine learning concepts
* Knowledge of the field of deep learning
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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.
TensorFlow offers multiple levels of API for constructing deep learning models, with varying levels of control and flexibility. In this week you will learn to use the functional API for developing more flexible model architectures, including models with multiple inputs and outputs. You will also learn about Tensors and Variables, as well as accessing and using inner layers within a model. The programming assignment for this week will put these techniques this into practice with a transfer learning application on the dogs and cats image dataset.
14 videos5 readings1 assignment1 programming assignment1 discussion prompt6 ungraded labs1 plugin
A flexible and efficient data pipeline is one of the most essential parts of deep learning model development. In this week you will learn a powerful workflow for loading, processing, filtering and even augmenting data on the fly using tools from Keras and the tf.data module. In the programming assignment for this week you will apply both sets of tools to implement a data pipeline for the LSUN and CIFAR-100 datasets.
12 videos1 reading1 assignment1 programming assignment8 ungraded labs
Sequence modelling tasks represent a rich and interesting class of problems, ranging from natural language tasks such as part-of-speech tagging and sentiment analysis, to forecasting of financial time series and speech audio generation. In this week you will learn how to use the recurrent neural network API in TensorFlow, as well as several useful layer types and tools for processing sequence data. In the programming assignment for this week, you will develop a generative language model on the Shakespeare dataset.
13 videos1 assignment1 programming assignment7 ungraded labs
For more advanced use cases of TensorFlow, it is possible to obtain a low level of control over the design and behaviour of your deep learning model, as well as the training loop itself. In this week you will learn how to exploit the Model and Layer subclassing API to develop fully flexible model architectures, as well as using the automatic differentiation tools in TensorFlow to implement custom training loops. In the programming assignment for this week you will implement these custom model building tools to develop a deep residual network.
12 videos1 programming assignment8 ungraded labs
In this course you have learned a powerful set of tools for developing customised deep learning models, including for sequence data, and flexible data pipelines. The Capstone Project brings many of these concepts together with a task to develop a custom neural translation model from English into German.
2 videos1 peer review1 ungraded lab1 plugin
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
Imperial College London is a world top ten university with an international reputation for excellence in science, engineering, medicine and business. located in the heart of London. Imperial is a multidisciplinary space for education, research, translation and commercialisation, harnessing science and innovation to tackle global challenges. Imperial students benefit from a world-leading, inclusive educational experience, rooted in the College’s world-leading research. Our online courses are designed to promote interactivity, learning and the development of core skills, through the use of cutting-edge digital technology.
Imperial College London
Course
DeepLearning.AI
Course
Imperial College London
Specialization
DeepLearning.AI
Specialization
191 reviews
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Showing 3 of 191
Reviewed on Jul 23, 2022
It would be better if related readings can contain some of the background knowledge.
Reviewed on Apr 6, 2021
I recumbent this course.A lot of practice: notebooks, assessments, capstone project and just enough theory about TensorFlow
Reviewed on Jan 8, 2022
Great follow up for the first course by going deeper to Tensorf Flow 2.0
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Jupyter Notebooks are a third-party tool that some Coursera courses use for programming assignments.
You can revert your code or get a fresh copy of your Jupyter Notebook mid-assignment. By default, Coursera persistently stores your work within each notebook.
To keep your old work and also get a fresh copy of the initial Jupyter Notebook, click File, then Make a copy.
We recommend keeping a naming convention such as “Assignment 1 - Initial” or “Copy” to keep your notebook environment organized. You can also download this file locally.
Rename your existing Jupyter Notebook within the individual notebook view
In the notebook view, add “?forceRefresh=true” to the end of your notebook URL
Reload the screen
You will be directed to your home Learner Workspace where you’ll see both old and new Notebook files.
Your Notebook lesson item will now launch to the fresh notebook.
If your Jupyter Notebook files have disappeared, it means the course staff published a new version of a given notebook to fix problems or make improvements. Your work is still saved under the original name of the previous version of the notebook.
To recover your work:
Find your current notebook version by checking the top of the notebook window for the title
In your Notebook view, click the Coursera logo
Find and click the name of your previous file
"Kernels" are the execution engines behind the Jupyter Notebook UI. As kernels time out after 90 minutes of notebook activity, be sure to save your notebooks frequently to prevent losing any work. If the kernel times out before the save, you may lose the work in your current session.
How to tell if your kernel has timed out:
Error messages such as "Method Not Allowed" appear in the toolbar area.
The last save or auto-checkpoint time shown in the title of the notebook window has not updated recently
Your cells are not running or computing when you “Shift + Enter”
To restart your kernel:
Save your notebook locally to store your current progress
In the notebook toolbar, click Kernel, then Restart
Try testing your kernel by running a print statement in one of your notebook cells. If this is successful, you can continue to save and proceed with your work.
If your notebook kernel is still timed out, try closing your browser and relaunching the notebook. When the notebook reopens, you will need to do "Cell -> Run All" or "Cell -> Run All Above" to regenerate the execution state.
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
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