Abstract: Deep learning has succeeded at such difficult tasks as driving cars or winning a game of Go and Dota2. It all sounds spectacular... but how do you create a state-of-the-art neural network for an even simpler task - image classification? In this workshop we will try to make every set approachable, from setting up the environment through building first models to tinkering with experiments.
During the hands-on session, you will experiment with an artificial neural network for image classification and learn practical hacks for how to tune the network for your needs, using techniques such as transfer learning and data augmentation. By the end of the workshop you will be able to create and optimize a deep learning project from scratch.
In this workshop we’ll be using PyTorch, a deep learning framework in Python. You will have a chance to understand why it is a tool of choice for machine learning researchers and data scientists. And why Andrej Karpathy’s skin has improved since he started using it!
Don’t worry about setting up the environment or renting a GPU - all of this will be taken care of by Neptune, the Machine Learning Lab. This platform allows us to send code to a preconfigured cloud environment and keep track of our deep learning experiments. It comes with pre-installed libraries such as tools Jupyter Notebook, PyTorch, Keras, TensorFlow and others.
Bio: Błażej Osiński is a data scientist at deepsense.ai. His professional experience include working at Google, Microsoft and Facebook. He was also the first software engineer at Berlin-based startup Segment of 1, operating in the FMCG industry.
Błażej holds Masters Degree in Computer Science and Bachelors in Mathematics, both from the University of Warsaw.