What Is Programming? And How To Get Started
January 28, 2025
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This course is part of TensorFlow 2 for Deep Learning Specialization
Instructor: Dr Kevin Webster
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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.
14 videos8 readings1 discussion prompt1 ungraded lab1 plugin
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.
13 videos2 assignments1 programming assignment8 ungraded labs
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.
11 videos1 assignment1 programming assignment8 ungraded labs
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.
12 videos1 programming assignment8 ungraded labs
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.
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.
DeepLearning.AI
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Imperial College London
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Reviewed on Jul 25, 2020
Really excellent course and quality of lectures and coding tutorials were beyond my expectation. I think this course is literally the best TF course available in Coursera
Reviewed on Nov 12, 2020
Awesome course, the best basic Keras course at Coursera, it should be more promoted, after so much time using TensorFlow, I've just found it now.
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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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.
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