Building a Sign-to-Speech prototype with TensorFlow, Pytorch and DeepStack: How it happened & What I learned
Steven Kolawole
Computer Vision, Neural Networks / Deep Learning

Building an E2E working prototype that detects sign language meanings in images/videos and generate equivalent voice of words communicated by the sign language, in real-time, won't be completed in a day's work. Here I'd explain how it happened and what I learned in the process.

Can you Read This? (Or: how I Improved Text Readability on the Web for the Visually Impaired)
Asya Frumkin
Algorithms, Computer Vision, Neural Networks / Deep Learning

I will explain my approach of detecting texts on top of an image background that are unreadable to people with visual impairment. I will explain the challenges I. encountered when using different OCR architectures for this task and talk about the solution I came up with.

Financial Portfolio Management with Deep Reinforcement Learning
Neural Networks / Deep Learning, Simulation, Time Series


Grokking LIME: How can we explain why an image classifier "knows" what’s in a photo without looking inside the model?
Kilian Kluge
Computer Vision, Neural Networks / Deep Learning, Transparency / Interpretability

How can LIME explain machine-learning models without peeking inside? Let's find out!

How to Trust Your Deep Learning Code
Tilman Krokotsch
Best Practice, Neural Networks / Deep Learning

Write unit tests and learn to trust your Deep Learning code again.

Machine Learning Testing Ecosystem of Python
Yunus Emrah Bulut
Computer Vision, Ethics (Privacy, Fairness,… ), Governance, Natural Language Processing, Neural Networks / Deep Learning, Security

Machine learning testing becomes an indispensable part of the MLOps and Python offers great ecosystem for this purpose.

Optimize your network inference time with OpenVINO
Adrian Boguszewski
Jupyter, Neural Networks / Deep Learning, Performance

Learn how to automatically convert the model using Model Optimizer and how to run the inference with OpenVINO Runtime to infer your model with low latency on the CPU and iGPU you already have. The magic with only a few lines of code.

Performing Content: Can NLP and Deep Learning algorithms predict reader preferences?
Sebastian Cattes
Natural Language Processing, Neural Networks / Deep Learning, Statistics

Can AI understand what drives user engagement? Join our talk "Performing Content: Can NLP and Deep Learning algorithms predict reader preferences?" to find out what NLP can bring to the editorial table.

PPML: Machine Learning on Data you cannot see
Valerio Maggio
Neural Networks / Deep Learning, Security

Have you ever wondered how to train your @PyTorch model on private data you cannot see? If you want to know how, this is the workshop for you! #PPML cc/ @openminedorg

Practical graph neural networks in Python with TensorFlow and Spektral
Aleksander Molak
Graphs, Neural Networks / Deep Learning

Practical Graph Neural Networks (GNNs) with Spektral & TensorFlow 🤩

Squirrel - Efficient Data Loading for Large-Scale Deep Learning
Dr. Thomas Wollmann
Distributed Computing, Neural Networks / Deep Learning, Parallel Programming / Async

Learn why we built and open sourced a data infrastructure library for deep learning.

The Myth of Neutrality: How AI is widening social divides
Stefanie Stoppel
Ethics (Privacy, Fairness,… ), Neural Networks / Deep Learning

AI is not neutral and its creation often perpetuates harmful biases. My talk highlights how difficult it is to build "fair and responsible" AI, but also why it's worth to try & prevent these algorithms from cementing existing injustices.

Transformer based clustering: Identifying product clusters for E-commerce
Sebastian Wanner, Christopher Lennan
Natural Language Processing, Neural Networks / Deep Learning, Use Case

Transformer based clustering with Sentence-Transformers and Facebook Faiss for an E-commerce use case where we clustered offers to automatically generate new products.