Commit a10a07aa authored by Oliver Kirsebom's avatar Oliver Kirsebom
Browse files

updated prerequisites

parent 0b02a335
......@@ -11,77 +11,63 @@ This 90-minute hands-on tutorial formed part the workshop [Detection and Classif
## Prerequisites
To participate in the tutorial, you will need to have
the following software installed on your laptop:
To participate in the hands-on session on day two of the workshop, your laptop needs to have a reasonably modern 64-bit operating system,
1. Python
2. The Ketos package, and all its dependencies
3. Jupyter notebook
Whether you are a Linux, Mac, or Windows user, we suggest that you use the Anaconda package manager to install the necessary software, as described in the following,
* Ubuntu 16.04 or later
* macOS 10.12.6 (Sierra) or later
* Windows 7 or later
1. Download Anaconda from [www.anaconda.com/distribution/#download-section](https://www.anaconda.com/distribution/#download-section) (make sure you get the Python 3.7 version)
You will need to have the following software installed on your laptop,
2. Install Anaconda as described at [docs.anaconda.com/anaconda/install/](https://docs.anaconda.com/anaconda/install/)
* Python
* The [Ketos](gitlab.meridian.cs.dal.ca/public_projects/ketos) package, and all its dependencies
* Jupyter notebook
3. Download the file [ketos-2.0.0b0.tar.gz](ketos-2.0.0b0.tar.gz) to your Downloads folder.
Whether you are a Linux, Mac, or Windows user, we suggest that you use the Anaconda package manager to install the necessary software, as described in the following,
4. Finally, install Ketos and Jupyter Notebook as follows,
1. Download Anaconda (full version) from [www.anaconda.com/distribution/#download-section](www.anaconda.com/distribution/#download-section); or, if you are short on disk space or your internet connection is slow, download Miniconda from [https://docs.conda.io/en/latest/miniconda.html](https://docs.conda.io/en/latest/miniconda.html). Whether you opt for the full Anaconda installantion or Miniconda, make sure you get the Python 3.7 version.
### Unix
2. Install Anaconda as described at [docs.anaconda.com/anaconda/install/](docs.anaconda.com/anaconda/install/).
3. Download the file [ketos-2.0.0b0.tar.gz](ketos-2.0.0b0.tar.gz) to your Downloads folder.
4. Now, install Ketos and Jupyter Notebook as follows,
### Unix:
```terminal
conda create --name dl_env
conda activate dl_env
conda install jupyter
pip install ketos-2.0.0b0.tar.gz
pip install gast==0.2.2
conda create --name dl_env
conda activate dl_env
conda install jupyter
conda install pip
pip install ~/Downloads/ketos-2.0.0b0.tar.gz
pip install gast==0.2.2
```
### Windows
### Windows:
* Start the Anaconda Navigator
* Under Environments, select the base environment, then open terminal
* Under *Environments*, select the base environment, then open terminal
* Type the following commands (each line followed by Enter)
```terminal
conda create --name dl_env
conda activate dl_env
conda install jupyter
conda install pip
pip install Downloads\ketos-2.0.0b0.tar.gz
pip install gast==0.2.2
pip install --user ipykernel
python -m ipykernel install --user --name=dl_env
jupyter notebook
```
* In the Jupyter Notebook browser window, select Kernel -> Change kernel -> dl_env.
* In the Jupyter Notebook browser window, select Kernel -> Change kernel -> dl_env.
5. Finally, test that your installation was successful by ...
## Resources
* **Deep Learning.**
If you are new to Machine Learning and Deep Learning in particular, you will find
plenty of free online resources to get you started. Here are a few suggestions:
- [Deep Learning](https://www.deeplearningbook.org/) by Ian Goodfellow and Yoshua Bengio and Aaron Courville
- ...
If you are new to Machine Learning and Deep Learning in particular, you will find plenty of free online resources to get you started. A good place to start, is the introductory chapter of the Deep Learning Book, available online at [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/).
* **Ketos.**
Ketos is an open-source Python package for acoustic data analysis with neural networks,
which has been developed by MERIDIAN. We will be using Ketos for the hands-on session
on the second day of the workshop. To learn more about Ketos, check out the GitLab
repository at [gitlab.meridian.cs.dal.ca/public_projects/ketos](https://gitlab.meridian.cs.dal.ca/public_projects/ketos) or explore the documentation pages at [docs.meridian.cs.dal.ca/ketos](https://docs.meridian.cs.dal.ca/ketos/).
* **Examples of applications of Deep Learning to marine bioacoustics.**
In the folder [papers](papers) you will find recent examples of
studies in which Deep Learning has been used detect and classify
marine sounds from whales and fish.
Ketos is an open-source Python package for acoustic data analysis with neural networks, which has been developed by MERIDIAN. We will be using Ketos for the hands-on session. To learn more about Ketos, check out the GitLab repository at [gitlab.meridian.cs.dal.ca/public_projects/ketos](https://gitlab.meridian.cs.dal.ca/public_projects/ketos) or explore the documentation pages at [docs.meridian.cs.dal.ca/ketos](https://docs.meridian.cs.dal.ca/ketos/).
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