Summary and Schedule
Welcome
This is a hands-on introduction to the first steps in Deep Learning, intended for researchers who are familiar with (non-deep) Machine Learning.
The use of Deep Learning has seen a sharp increase of popularity and applicability over the last decade. While Deep Learning can be a useful tool for researchers from a wide range of domains, taking the first steps in the world of Deep Learning can be somewhat intimidating. This introduction aims to cover the basics of Deep Learning in a practical and hands-on manner, so that upon completion, you will be able to train your first neural network and understand what next steps to take to improve the model.
We start with explaining the basic concepts of neural networks, and then go through the different steps of a Deep Learning workflow. Learners will learn how to prepare data for deep learning, how to implement a basic Deep Learning model in Python with Keras, how to monitor and troubleshoot the training process and how to implement different layer types such as convolutional layers.
Prerequisites
Learners are expected to have the following knowledge:
- Basic Python programming skills and familiarity with the Pandas package.
- Basic knowledge on Machine learning, including the following concepts: Data cleaning, train & test split, type of problems (regression, classification), overfitting & underfitting, metrics (accuracy, recall, etc.).
We are currently looking for volunteers to test this
lesson! If you would like to teach this lesson in a pilot
workshop, please let the lesson developers know by opening
a new issue on the lesson repository or posting to the #machine_learning
Slack channel on The Carpentries
Slack. We would love to help you prepare to teach the lesson and
receive feedback on how it could be further improved, based on your
experience in the workshop.
Setup Instructions | Download files required for the lesson | |
Duration: 00h 00m | 1. Introduction |
What is Deep Learning? When does it make sense to use and not use Deep Learning? When is it successful? What are the tools involved? What is the workflow for Deep Learning? Why did we choose to use Keras in this lesson? How do neural networks learn? |
Duration: 01h 20m | 2. Classification by a neural network using Keras |
What is a neural network? How do I compose a Neural Network using Keras? How do I train this network on a dataset? How do I get insight into learning process? How do I measure the performance of the network? ::: |
Duration: 03h 10m | 3. Monitor the training process |
How do I create a neural network for a regression task? How does optimization work? How do I monitor the training process? How do I detect (and avoid) overfitting? What are common options to improve the model performance? ::: |
Duration: 06h 30m | 4. Advanced layer types |
Why do we need different types of layers? What are good network designs for image data? What is a convolutional layer? How can we use different types of layers to prevent overfitting? ::: |
Duration: 08h 50m | 5. Outlook |
How does what I learned in this course translate to real-world
problems? How do I organise a deep learning project? What are next steps to take after this course? ::: |
Duration: 09h 30m | Finish |
The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.
Software Setup
Installing Python using Anaconda
Python is a popular language for scientific computing, and a frequent choice for machine learning as well. Installing all of its scientific packages individually can be a bit difficult, however, so we recommend the installer Anaconda which includes most (but not all) of the software you will need.
Regardless of how you choose to install it, please make sure you install Python version 3.x (e.g., 3.4 is fine). Also, please set up your python environment at least a day in advance of the workshop. If you encounter problems with the installation procedure, ask your workshop organizers via e-mail for assistance so you are ready to go as soon as the workshop begins.
Checkout the video tutorial or:
- Open https://www.anaconda.com/products/distribution with your web browser.
- Download the Python 3 installer for Windows.
- Double-click the executable and install Python 3 using MOST of the default settings. The only exception is to check the Make Anaconda the default Python option.
Checkout the video tutorial or:
- Open https://www.anaconda.com/products/distribution with your web browser.
- Download the Python 3 installer for OS X.
- Install Python 3 using all of the defaults for installation.
Note that the following installation steps require you to work from the shell. If you run into any difficulties, please request help before the workshop begins.
- Open https://www.anaconda.com/products/distribution with your web browser.
- Download the Python 3 installer for Linux.
- Install Python 3 using all of the defaults for installation.
- Open a terminal window.
- Navigate to the folder where you downloaded the installer
- Type
- Press enter.
- Follow the text-only prompts. When the license agreement appears (a
colon will be present at the bottom of the screen) hold the down arrow
until the bottom of the text. Type
yes
and press enter to approve the license. Press enter again to approve the default location for the files. Typeyes
and press enter to prepend Anaconda to yourPATH
(this makes the Anaconda distribution the default Python).
Installing the required packages
Conda is the package management system associated with Anaconda and runs on Windows, macOS and Linux. Conda should already be available in your system once you installed Anaconda successfully. Conda thus works regardless of the operating system. Make sure you have an up-to-date version of Conda running. See these instructions for updating Conda if required. {: .callout}
To create a conda environment called dl_workshop
with
the required packages, open a terminal (Mac/Linux) or Anaconda prompt
(Windows) and type the command:
Activate the newly created environment:
conda activate dl_workshop
Install tensorflow using pip (python’s package manager):
Note that modern versions of Tensorflow make Keras available as a module.
pip is the package management system for Python software packages. It is integrated into your local Python installation and runs regardless of your operating system too.
It is possible that Windows users will run into version conflicts. If you are on Windows and get errors running the command, you can try installing the packages using pip within a conda environment:
Newer Macs (from 2020 onwards) often have a different kind of chip, manufactured by Apple instead of Intel. This can lead to problems installing Tensorflow . If you get errors running the installation command or conda hangs endlessly, you can try installing Tensorflow for Mac with pip:
Starting Jupyter Lab
We will teach using Python in Jupyter lab, a programming environment that runs in a web browser. Jupyter requires a reasonably up-to-date browser, preferably a current version of Chrome, Safari, or Firefox (note that Internet Explorer version 9 and below are not supported). If you installed Python using Anaconda, Jupyter should already be on your system. If you did not use Anaconda, use the Python package manager pip (see the Jupyter website for details.)
To start jupyter lab, open a terminal (Mac/Linux) or Anaconda prompt (Windows) and type the command:
To start the Python interpreter without jupyter lab, open a terminal (Mac/Linux) or Anaconda prompt (Windows) or git bash and type the command:
Check your setup
To check whether all packages installed correctly, start a jupyter notebook in jupyter lab as explained above. Run the following lines of code:
PYTHON
import sklearn
print('sklearn version: ', sklearn.__version__)
import seaborn
print('seaborn version: ', seaborn.__version__)
import pandas
print('pandas version: ', pandas.__version__)
import tensorflow
print('Tensorflow version: ', tensorflow.__version__)
This should output the versions of all required packages without giving errors. Most versions will work fine with this lesson, but: - For Keras and Tensorflow, the minimum version is 2.12.0 - For sklearn, the minimum version is 1.2.2
Fallback option: cloud environment
If a local installation does not work for you, it is also possible to run this lesson in Binder Hub. This should give you an environment with all the required software and data to run this lesson, nothing which is saved will be stored, please copy any files you want to keep. Note that if you are the first person to launch this in the last few days it can take several minutes to startup. The second person who loads it should find it loads in under a minute. Instructors who intend to use this option should start it themselves shortly before the workshop begins.
Alternatively you can use Google colab. If you open a jupyter notebook here, the required packages are already pre-installed. Note that google colab uses jupyter notebook instead of jupyter lab.
Downloading the required datasets
Download the weather dataset prediction csv and Dollar street dataset (4 files in total)