November 4 - 6, 2020

Online

ML conf EU 2020

Name: ML conf EU 2020
Start: 2020-11-04T21:00:00.000Z
End: 2020-11-06T21:00:00.000Z

The Machine Learning conference for software developers

Esta edición del evento ha finalizado, las últimas actualizaciones de este Tech Conference están disponibles en el sitio web de la marca.

Sponsorship opportunitites

Versión en EspañolES

TensorFlow.js 101: Aprendizaje automático en el navegador y más allá

41 min

TensorFlow.js 101: Aprendizaje automático en el navegador y más allá

Jason Mayes

Google

TensorFlow.js enables machine learning in the browser and beyond, with features like face mesh, body segmentation, and pose estimation. It offers JavaScript prototyping and transfer learning capabilities, as well as the ability to recognize custom objects using the Image Project feature. TensorFlow.js can be used with Cloud AutoML for training custom vision models and provides performance benefits in both JavaScript and Python development. It offers interactivity, reach, scale, and performance, and encourages community engagement and collaboration between the JavaScript and machine learning communities.

tensorflow machine learning innovation tensorflow react

Una introducción al aprendizaje por transferencia en NLP y HuggingFace

32 min

Una introducción al aprendizaje por transferencia en NLP y HuggingFace

Thomas Wolf

HuggingFace

Transfer learning in NLP allows for better performance with minimal data. BERT is commonly used for sequential transfer learning. Models like BERT can be adapted for downstream tasks such as text classification. Handling different types of inputs in NLP involves concatenating or duplicating the model. Hugging Face aims to tackle challenges in NLP through knowledge sharing and open sourcing code and libraries.

machine learning

Enseñando ML y AI a los Programadores

34 min

Enseñando ML y AI a los Programadores

Laurence Moroney

Google

The Talk discusses the current state of AI and the challenges faced in educating developers. Google's mission is to train 10 percent of the world's developers in machine learning and AI. They have developed specializations and training initiatives to make AI easy and accessible. The impact of AI education includes rigorous certification exams and partnerships with universities. The Talk also highlights the growth trends in the tech industry and the importance of AI skills. TensorFlow is recommended for its deployment capabilities, and practice is emphasized for building a career in machine learning.

machine learning artificial intelligence

El poder del aprendizaje transferido en NLP: Construye un modelo de clasificación de texto usando BERT

35 min

El poder del aprendizaje transferido en NLP: Construye un modelo de clasificación de texto usando BERT

Jayeeta Putatunda

Indellient US Inc.

Transfer learning is a technique used when there is a scarcity of labeled data, where a pre-trained model is repurposed for a new task. BERT is a bidirectional model trained on plain text that considers the context of tokens during training. Understanding the baseline NLP modeling and addressing challenges like context-based words and spelling errors are crucial. BERT has applications in multiple problem-solving scenarios, but may not perform well in strict classification labels or conversational AI. Training BERT involves next sentence prediction and mass language modeling to handle contextual understanding and coherent mapping.

machine learning

DeepPavlov Agent: Marco de código abierto para IA conversacional multihabilidad

27 min

DeepPavlov Agent: Marco de código abierto para IA conversacional multihabilidad

Mikhail Burtsev

DeepPavlov.ai

The Pavlov Agent is an open source framework for multi-skill conversational AI, addressing the need for specific skills in different domains. The microservice architecture allows for scalability and skill reuse. The Deep Pavlov Library enables the creation of NLP pipelines for different skills. The Deep Pavlov Dream serves as a repository for skills and templates, while the Deployment Agent orchestrates all components for a seamless conversational experience. DeepLove.AI offers more flexibility and customization compared to Microsoft's LUIS service.

machine learning

Prácticas con TensorFlow.js

160 min

Prácticas con TensorFlow.js

Workshop

Jason Mayes

Ven y descubre nuestro masterclass que te guiará a través de 3 recorridos comunes al usar TensorFlow.js. Comenzaremos demostrando cómo usar uno de nuestros modelos predefinidos, clases de JS muy fáciles de usar para trabajar rápidamente con ML. Luego veremos cómo volver a entrenar uno de estos modelos en minutos utilizando el aprendizaje por transferencia en el navegador a través de Teachable Machine y cómo se puede usar en tu propio sitio web personalizado. Finalmente, terminaremos con un hola mundo escribiendo tu propio código de modelo desde cero para hacer una regresión lineal simple y predecir los precios ficticios de las casas en función de sus metros cuadrados.

tensorflow machine learning

¡Nunca vuelvas a tener un Jupyter Notebook inmantenible!

26 min

¡Nunca vuelvas a tener un Jupyter Notebook inmantenible!

Marco Gorelli

Samsung R&D Institute UK

Jupyter Notebooks are important for data science, but maintaining them can be challenging. Visualizing data sets and using code quality tools like NBQA can help address these challenges. Tools like nbdime and Precommit can assist with version control and future code quality. Configuring NBQA and other code quality tools can be done in the PyProject.toml file. NBQA has been integrated into various projects' continuous integration workflows. Moving code from notebooks to Python packages should be considered based on the need for reproducibility and self-contained solutions.

dataviz machine learning

Aprendizaje profundo de transferencia para la visión por computadora

8 min

Aprendizaje profundo de transferencia para la visión por computadora

2 authors

Today's Talk focuses on deep transfer learning for Computer Vision in the semiconductor manufacturing industry, specifically defect classification. The speakers discuss using a hybrid classification system with pre-trained models and image augmentation for accurate defect detection. They also explore the use of unsupervised learning, leveraging clustering algorithms and pre-trained models like ResNet-50, for defect analysis without prior knowledge. The process is reproducible, user-friendly, and provides accurate cluster results, with potential for future supervised learning applications.

machine learning

Análisis de Sesiones del Navegador: La Clave para la Detección de Fraudes

7 min

Análisis de Sesiones del Navegador: La Clave para la Detección de Fraudes

Javier Alcaide Pérez

Bluetab Solutions

Blue Tab Solutions specializes in advanced analytics and big data, and recently improved financial fraud detection using Spark and the CRISPM methodology. They discovered insights like the correlation between fraudulent sessions and the mobile cast page accessed from the web application. The models created using decision trees, random forest classifiers, and gradient boosting classifiers were validated using the area under the ROC curve. The GVT classifier yielded the best result with a score of 0.94. Regular training is necessary for accurate models, and the next steps involve real-time action when fraud is detected.

machine learning

Ampliando la adopción de IA con AutoML

9 min

Ampliando la adopción de IA con AutoML

Bernhard Suhm

MathWorks

AutomL simplifies the complexity of building machine learning models, allowing engineers to focus on the hard problems and applications. It enables the solving of problems that wouldn't be feasible otherwise. The three-step AutomL approach by MathWorks includes wavelet scattering for feature extraction. AutoML also enables feature selection and model optimization for memory and power-limited embedded systems. MATLAB can translate to low-level code for deployment.

machine learning artificial intelligence

Procesamiento de Datos de Robots a Escala con R y Kubernetes

8 min

Procesamiento de Datos de Robots a Escala con R y Kubernetes

Frans van Dunné

ixpantia

The Talk discusses the challenges of managing and analyzing the increasing volume of data gathered from robots. It highlights the importance of data extraction and feature engineering in analyzing what happens before a failure. The use of Kubernetes and Packyderm for data management and automatic updates in the pipeline is mentioned. The parallelization of R scripts and the scalability of large clusters for data collection and processing are emphasized. The Talk also mentions the use of AI at the robot fleet level for unlocking new opportunities.

machine learning

Dabl: Aprendizaje Automático Automático con la Participación Humana

35 min

Dabl: Aprendizaje Automático Automático con la Participación Humana

Andreas Müller

Microsoft

This talk introduces Dabble, a library that allows data scientists to iterate quickly and incorporate human input into the machine learning process. Dabble provides tools for each step of the machine learning workflow, including problem statement, data cleaning, visualization, model building, and model interpretation. It uses mosaic plots and pair plots to analyze categorical and continuous features. Dabble also implements a portfolio-based automatic machine learning approach using successive halving to find the best model. The future goals of Dabble include supporting more feature types, improving the portfolio, and building explainable models.

machine learning

¿Puedes Cantar con Todas las Voces de las Características?

8 min

¿Puedes Cantar con Todas las Voces de las Características?

Yama Anin Aminof

MyPart

This Talk discusses the role of repetition in songwriting and how it has become more prevalent over the years. The use of string metrics, such as the Levenstein distance, allows for the analysis of similarity between segments of songs. A similarity threshold of 70% is used to determine if segments are considered similar. Overall, the Talk explores the importance of repetition in creating successful songs and the use of analytical tools to measure similarity.

machine learning

Aprendizaje automático en el borde utilizando TensorFlow Lite

8 min

Aprendizaje automático en el borde utilizando TensorFlow Lite

Håkan Silfvernagel

Miles AS

Håkan Silvernagel introduces TensorFlow Lite, an open-source deep learning framework for deploying machine learning models on mobile and IoT devices. He highlights the benefits of using TensorFlow Lite, such as reduced latency, increased privacy, and improved connectivity. The Talk includes a demonstration of object recognition capabilities and a real-world example of using TensorFlow Lite to detect a disease affecting farmers in Tanzania. References to official TensorFlow documentation, Google IO conference, and TensorFlow courses on Coursera are provided.

machine learning

Aumenta la productividad con el ecosistema de Keras

30 min

Aumenta la productividad con el ecosistema de Keras

Haifeng Jin

Keras Team at Google

This Talk introduces the TensorFlow in Keras ecosystem and highlights its features, including tensor manipulations, automatic differentiation, and deployment. It also discusses the workflow and automation of hyperparameter tuning with Keras Tuner and AutoKeras. The Talk emphasizes the simplicity and productivity of using AutoKeras, which supports various tasks and advanced scenarios. It also mentions the challenges beginners face and provides resources for learning. Lastly, it touches on the use of TensorFlow and Keras in the research domain and the customization options in AutoKeras, including time series forecasting.

tensorflow machine learning keras

Visión por Computadora utilizando OpenCV

32 min

Visión por Computadora utilizando OpenCV

Beril Sirmacek

Jonkoping University & the Owner of Create4D

Today's Talk explores image processing, computer vision, and their combination with machine learning. Image processing involves manipulating images, while computer vision extracts valuable information from images. Histograms are crucial in image processing as they represent the distribution of brightness values. Various image processing techniques can be used, such as thresholding and convolution. Computer vision techniques focus on extracting important features for object recognition and can be hand-tailored. Audio processing is not the focus of OpenCV, but TensorFlow libraries may be more suitable. Understanding the algorithms behind the code is important for robustness and effective debugging. Computer vision has applications in healthcare for cancer recognition and in agriculture for plant health monitoring.

machine learning

La Guía del Autoestopista de la Galaxia de Ingeniería de Aprendizaje Automático

112 min

La Guía del Autoestopista de la Galaxia de Ingeniería de Aprendizaje Automático

Workshop

Alyona Galyeva

¿Eres un Ingeniero de Software al que se le asignó la tarea de implementar un modelo de aprendizaje automático o aprendizaje profundo por primera vez en tu vida? ¿Te preguntas qué pasos seguir y cómo se diferencia el software impulsado por IA del software tradicional? Entonces este es el masterclass adecuado al que asistir.
Internet ofrece miles de artículos y cursos gratuitos que muestran lo fácil que es entrenar e implementar un modelo de IA simple. Al mismo tiempo, en la realidad es difícil integrar un modelo real en la infraestructura actual, depurarlo, probarlo, implementarlo y monitorearlo correctamente. En este masterclass, te guiaré a través de este proceso compartiendo consejos, trucos y herramientas de código abierto favoritas que te facilitarán mucho la vida. Así que al final del masterclass, sabrás por dónde empezar tu viaje de implementación, qué herramientas utilizar y qué preguntas hacer.

machine learning

La Revolución de la Evolución

31 min

La Revolución de la Evolución

Robert Plummer

iFit

The Talk discusses the challenges of implementing software solutions and the need for abstractions. It emphasizes the importance of innovation and implementing once to avoid complexity. The use of Brain.js in machine learning research and its practical applications are highlighted. The talk also mentions the benefits of using JavaScript and GPU.js for graphics processing. Overall, the Talk encourages simplicity, efficiency, and collaboration in software development.

machine learning

Introducción al Aprendizaje Automático en la Nube

146 min

Introducción al Aprendizaje Automático en la Nube

Workshop

Dmitry Soshnikov

Este masterclass será tanto una introducción suave al Aprendizaje Automático, como un ejercicio práctico de uso de la nube para entrenar modelos de aprendizaje automático simples y no tan simples. Comenzaremos utilizando ML Automático para entrenar el modelo para predecir la supervivencia en el Titanic, y luego pasaremos a tareas de aprendizaje automático más complejas como la optimización de hiperparámetros y la programación de series de experimentos en el clúster de cómputo. Finalmente, mostraré cómo Azure Machine Learning se puede utilizar para generar pinturas artificiales utilizando Redes Generativas Adversarias, y cómo entrenar un modelo de preguntas y respuestas de lenguaje en documentos de COVID para responder preguntas relacionadas con COVID.

azure machine learning

Cómo convertir cualquier producto en un producto de aprendizaje automático

33 min

Cómo convertir cualquier producto en un producto de aprendizaje automático

Shivani Poddar

Facebook

In this Talk, an ML engineer from Facebook shares insights on when to use ML and a successful use case from Facebook. The speaker discusses the process of using ML for Facebook Portal calls, including data collection and model selection. The importance of precision and recall in ML models is emphasized, as well as the need for online evaluation and active learning. The Talk also touches on the challenges of data protection and label delay in ML model development.

machine learning

ML conf EU 2020

The Machine Learning conference for software developers

Available in other languages: