Monthly Archives: June 2019
So, you have embarked into your Deep Learning journey and perhaps you are navigating through the concepts of Gradient Descent, Back-propagation and so forth. After all the theory you are eager to get your environment ready to do some actual ‘deep learning hard work’ and you have no idea where to start. You are in the right place then. This short tutorial has been put together for Mac user (sorry Windows aficionados) and will provide you with what you need to get started.
Yes, you need Python!
Sure you know that Python is the key programming language when it comes to Machine and Deep Learning. Make sure you have our beloved HomeBrew:
/usr/bin/ruby -e “$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
Install Python 3 (with this version, pip3 will be automatically installed)
brew install python3
In order to keep things clean and contain all your deep learning related dependencies in one space, it is useful to use virtual environments.
pip3 install virtualenv virtualenvwrapper
You will also need to modify your bash profile file:
by adding the following:
# virtualenv and virtualenvwrapper
Next step is to create a virtual environment for your deep learning project:
mkvirtualenv cv -p python3
This will create a virtual environment named
cv and in order to come out of such instance, you will need to type the command
Some Additional Dependencies
brew install cmake
Let’s install the real stuff
Situate yourself inside your virtual environment by typing the following:
Some additional dependencies should be taken care of:
pip install numpy h5py pillow scikit-image
Finally, we can install OpenCV:
pip install opencv-python
pip install dlib
pip install tensorflow
pip install keras
Keras, in particular, is a user friendly, beginner library for Machine Learning and Deep Learning models that runs on top of Tensorflow. Happy Machine Learning modelling 🙂