Introduction to Deep Learning
Content
This course will introduce the basic concepts of Deep Learning. We will discuss the methods behind neural networks and how to use them to set up non-linear models for solving, e.g., classification tasks. The basic ideas involved in training neural nets (backpropagation and minimizing loss functions using optimization methods) are covered. We will cover simple deep learning models (with very few fully connected layers) as well as more advanced models such as convolutional neural networks. We shall further focus on model evaluation and how to effectively train the model. In the hands-on session, participants will train their own neural network using TensorFlow's Keras API.
Target group
The course is targeted at beginners with no or little knowledge of Deep Learning.
Basic knowledge of Python is required.
The course is free of charge and will be held in English.
Requirements
Participants are expected to have a computer available to set up a working Python environment for the course.
Below you can find the instructions for setting up isolated Python environments that contain all packages needed to work on the course material.
If you have your own environment, please note that we assume you to use Python >= 3.9 <= 3.12 to be compatible with TensorFlow 2.
Python Anaconda distribution
- For a UNIX-like operating system, open a command line; for Windows, open an Anaconda Prompt.
- Create the environment and install packages (this may take a while):
$ conda create --name hkhlr_dl python=3.12 pip
- Activate the environment and install the packages with pip:
$ conda activate hkhlr_dl $ pip install --no-cache-dir tensorflow pandas matplotlib opencv-python pydot progress jupyterlab graphviz
For more details regarding the installation of TensorFlow, e.g., with/without GPU support, consult the documentation: https://www.tensorflow.org/install/pip.
Python virtual environments
Follow the instructions for setting up a virtual environment for your operating system and activate your environment: https://docs.python.org/3/library/venv.html.
Activate the environment:
$ source /path/to/your/environment/bin/activate
and install the packages with pip as shown above.
