ML conf EU

ML conf EU 2020

An Introduction to Transfer Learning in NLP and HuggingFace

Thomas Wolf

As an AI scientist and a developer, I have been engaged with AI-applications for many years especially focusing on object detection and recognition purposes. I love thinking that we can get creative in designing neural networks. We can train them supervised, unsupervised, semi or self-supervised, and this gives possibilities to mimic the human brain in a narrow domain. However, in vision applications, there are still things where AI is lacking and will be lacking without computer vision knowledge. Computer vision has been solving detection and recognition problems for many years. However, in the last decade, it seems like AI is seen as a replacement of computer vision. AI can find the optimal model for a specific type of data set and it might achieve generalization better. AI can be designed in a way that it can learn life-long which also brings possibilities of creating models which serve better when they are used longer. However, an AI vision system will be lacking capabilities without computer vision knowledge. First of all, it will require a very big data set to train the model what can be expensive or even not possible. On the other hand, computer vision systems can be modeled only using a hand-drawn template image. Training AI models also requires GPUs. Nevertheless, I do not want to encourage everyone to train AI models for solving any simple problem which could be solved easily computer vision. Last but not least, knowing computer vision, machine learning and especially feature engineering methods helps to design hybrid models that might be more robust to adversarial attacks or changing conditions. In this lecture, I will briefly introduce how computer vision (especially using the OpenCV library) and machine learning can be used for creating detection and recognition models. Some experience with python, jupyter notebook and some machine learning background would be useful to get more benefits from this lecture. <a href="https://www.froala.com/wysiwyg-editor?pb=1"> </a>

Computer Vision Using OpenCV

Beril Sirmacek

Elegant and graceful mathematics make a cool textbook cover, but the inside of those same books are usually dry cold engineering. It's important to mix the theory of innovation with the excitement of practicality, and through the composition of these elements we find innovation. In this talk, I'll show you from an engineering perspective how to explore, balance, and ultimately bottle machined success. <a href="https://www.froala.com/wysiwyg-editor?pb=1"> </a>

The Evolution Revolution

Robert Plummer

This talk will be a walkthrough of utilizing machine learning to replace a rule based system for consumers. We will discuss when is it okay to use ML, how to build these models with intelligent data, evaluate these offline and finally how to validate this evaluation to land these models in production systems. Furthermore, we will illustrate various self-learning/interactive-learning strategies that can be used for production systems to automate how models teach themselves to become better. <a href="https://www.froala.com/wysiwyg-editor?pb=1"> </a>

How to Machine Learn-ify any Product

Shivani Poddar

Teaching ML and AI to Coders

Laurence Moroney

Chip and equipment manufacturing and tracking is a tough task given the strict adherence to quality standards and processes like six-sigma control checks. In this session, we will be looking at key real-world problems from Semiconductor &amp; Manufacturing, and possible methodologies where we leveraged a combination of traditional computer vision techniques and coupled it with the power of deep transfer learning and machine \ deep learning. We will be covering two main use-cases from the Industry: Automatic Defect detection at NanoscaleDefect Clustering at Nanoscale <a href="https://www.froala.com/wysiwyg-editor?pb=1"> </a>

Deep Transfer Learning for Computer Vision

Dipanjan Sarkar

Sachin Dangayach

Most people would agree that R is a popular language for data analysis. Perhaps less well known is that R has good support for parallel execution on a single CPU through packages like future. In this presentation we will talk about our experience scaling up R processes even further running R in parallel in docker containers using Kubernetes. Robots generate massive amounts of sensor and other data; extracting the right information and insights from this requires significant more processing than can be tackled on a single execution environment. Faced with a preprocessing job of several hundred GB of data of compressed json line files, we used Pachyderm to write data pipelines to run the data prep in parallel, using multicore containers on a kubernetes cluster. By the end of the talk we will have dispelled the myth that R cannot be used in production at scale. Even if you do not use R, you will have seen a use case to scale up analysis regardless of your language of choice. <a href="https://www.froala.com/wysiwyg-editor?pb=1"> </a>

Processing Robot Data at Scale with R and Kubernetes

Frans van Dunné

Adoption of AI has been slowed the challenges involved in obtaining performant models, which require significant expertise and effort, and the limited number of practitioners with machine learning expertise. Automated machine learning (AutoML) eliminates the routine steps in the machine learning workflow, thus empowering domain experts without machine learning background to build good initial models, and allowing experienced practitioners to focus additional manual model optimization. This talk describes the extent of automation available for the various steps and demonstrates AutoML with a classifier for human activities based on accelerometer sensor data. <a href="https://www.froala.com/wysiwyg-editor?pb=1"> </a>

Broadening AI Adoption with AutoML

Bernhard Suhm

TensorFlow.js 101: ML in the Browser and Beyond

Jason Mayes

TensorFlow has built a solid foundation for various machine learning applications, on top of which the Keras ecosystem can really boost the productivity of the developers in building machine learning solutions. Keras has a simple and arbitrarily flexible API for building and training models. However, we still need a lot of manual work to tune the hyperparameters. Fortunately, with Keras Tuner, we can automate the hyperparameter tuning process with minor modifications to the code for building and training the models. To further boost the productivity, we introduce AutoKeras, which fully automates the model building, training, and hyperparameter tuning process. It dramatically reduces the amount of prior knowledge needed of using machine learning for some common tasks. All you need is to define the task and to provide the training data. <a href="https://www.froala.com/wysiwyg-editor?pb=1"> </a>

Boost Productivity with Keras Ecosystem

Haifeng Jin

DeepPavlov Agent is a framework designed to facilitate the development of scalable and production-ready multi-skill virtual assistants, complex dialogue systems, and chatbots. Key features of DeepPavlov Agent include (1) scalability and reliability in the high load environment due to micro-service architecture; (2) ease of adding and orchestrating conversational skills; (3) shared dialogue state memory and NLP annotations accessible to all skills. DeepPavlov DREAM is a socialbot platform with a modular design with the main components such as annotators, skills and selectors run as independent services. These components are conﬁgured and deployed using Docker containers. It allows developers to focus on application development instead of focusing on the intrinsic details of the manual low-level infrastructure conﬁguration. <a href="https://www.froala.com/wysiwyg-editor?pb=1"> </a>

DeepPavlov Agent: Open-source Framework for Multiskill Conversational AI

Mikhail Burtsev

What if you could perform machine learning on the edge, i.e on your mobile device? This would mean that you no longer would need the roundtrip to the server, no data will leave the device and you don't even need an internet connection. In this session you will get an introduction to TensorFlow Lite so that you can use it in your own projects. <a href="https://www.froala.com/wysiwyg-editor?pb=1"> </a>

Machine Learning on the Edge Using TensorFlow Lite

Håkan Silfvernagel

This talk will show how a fraud detection model has been developed based on the data from the browsing sessions of the different users. Tools such as PySpark and Spark ML have been used in this initiative due to a large amount of data. The model created was able to identify a grouping of characteristics that covered 10% of the total sessions in which 88% were deemed fraudulent. This allows analysts to spend more of their time on higher-risk cases. <a href="https://www.froala.com/wysiwyg-editor?pb=1"> </a>

Browser Session Analytics: The Key to Fraud Detection

Javier Alcaide Pérez

After this talk, you will know how to write the perfect song for your favourite singer! This is not a songwriting retreat but a talk about some of the lyrical, structural, harmonic, and melodic features song analysis includes. We will discuss the extraction of song structures using NLP tools and repetition analysis, musical features, and how to use all of those features to predict which songs fit which artist the best. Attend this talk to discover what is the future that the music industry can achieve with machine learning. <a href="https://www.froala.com/wysiwyg-editor?pb=1"> </a>

Can You Sing with All the Voices of the Features?

Yama Anin Aminof

The domain of Natural Language Processing have seen a tremendous amount of research and innovation in the past couple of years to tackle the problem of implementing high quality machine learning and AI solutions using natural text. Text Classification is one such area that is extremely important in all sectors like finance, media, product development, etc. Building up a text classification system from scratch for every use case can be challenging in terms of cost as well as resources, considering there is a good amount of dataset to begin training with. Here comes the concept of transfer learning. Using some of the models that has been pre-trained on terates of data and fine-tuning it based on the problem at hand is the new way to efficiently implement machine learning solutions without spending months on data cleaning pipeline. This talk with highlight ways of implementing the newly launched BERT and fine tuning the base model to build an efficient text classifying model. Basic understanding of python is desirable. <a href="https://www.froala.com/wysiwyg-editor?pb=1"> </a>

Power of Transfer Learning in NLP: Build a Text Classification Model Using BERT

Jayeeta Putatunda

Data visualisation is a fundamental part of Data Science. The talk will start with a practical demonstration (using pandas, scikit-learn, and matplotlib) of how relying on summary statistics and predictions alone can leave you blind to the true nature of your datasets. I will make the point that visualisations are crucial in every step of the Data Science process and therefore that Jupyter Notebooks definitely do belong in Data Science. We will then look at how maintainability is a real challenge for Jupyter Notebooks, especially when trying to keep them under version control with git. Although there exists a plethora of code quality tools for Python scripts (flake8, black, mypy, etc.), most of them don't work on Jupyter Notebooks. To this end I will present nbQA, which allows any standard Python code quality tool to be run on a Jupyter Notebook. Finally, I will demonstrate how to use it within a workflow which lets practitioners keep the interactivity of their Jupyter Notebooks without having to sacrifice their maintainability. <a href="https://www.froala.com/wysiwyg-editor?pb=1"> </a>

Never Have an Unmaintainable Jupyter Notebook Again!

Marco Gorelli

In many real-world applications, data quality and curation and domain knowledge play a much larger role in building successful models than coming up with complex processing techniques and tweaking hyper-parameters. Therefore, a machine learning toolbox should enable users to understand both data and model, and not burden the practitioner with picking preprocessing steps and hyperparameters. The dabl library is a first step in this direction. It provides automatic visualization routines and model inspection capabilities while automating away model selection. dabl contains plot types not available in standard python libraries so far, as well as novel algorithms for picking interesting visualizations. Heuristics are used to select appropriate preprocessing for machine learning, while state-of-the-art portfolio selection algorithms are used for efficient model and hyperparameter search. dabl also provides easy access to model evaluation and model inspection tools provided scikit-learn. <a href="https://www.froala.com/wysiwyg-editor?pb=1"> </a>

Dabl: Automatic Machine Learning with a Human in the Loop

Andreas Müller

Are you a Software Engineer who got tasked to deploy a machine learning or deep learning model for the first time in your life? Are you wondering what steps to take and how AI-powered software is different from traditional software? Then it is the right workshop to attend. The internet offers thousands of articles and free of charge courses, showing how it is easy to train and deploy a simple AI model. At the same time in reality it is difficult to integrate a real model into the current infrastructure, debug, test, deploy, and monitor it properly. In this workshop, I will guide you through this process sharing tips, tricks, and favorite open source tools that will make your life much easier. So, at the end of the workshop, you will know where to start your deployment journey, what tools to use, and what questions to ask. <a href="https://www.froala.com/wysiwyg-editor?pb=1"> </a>

The Hitchhiker's Guide to the Machine Learning Engineering Galaxy

Alyona Galyeva

Come check out our workshop which will walk you through 3 common journeys when using TensorFlow.js. We will start with demonstrating how to use one of our pre-made models - super easy to use JS classes to get you working with ML fast. We will then look into how to retrain one of these models in minutes using in browser transfer learning via Teachable Machine and how that can be then used on your own custom website, and finally end with a hello world of writing your own model code from scratch to make a simple linear regression to predict fictional house prices based on their square footage. <a href="https://www.froala.com/wysiwyg-editor?pb=1"> </a>

Hands on with TensorFlow.js

This workshop will be both a gentle introduction to Machine Learning, and a practical exercise of using the cloud to train simple and not-so-simple machine learning models. We will start with using Automatic ML to train the model to predict survival on Titanic, and then move to more complex machine learning tasks such as hyperparameter optimization and scheduling series of experiments on the compute cluster. Finally, I will show how Azure Machine Learning can be used to generate artificial paintings using Generative Adversarial Networks, and how to train language question-answering model on COVID papers to answer COVID-related questions. <a href="https://www.froala.com/wysiwyg-editor?pb=1"> </a>

The Machine Learning conference for software developers