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IT Conferences Archive
November 4 - 6, 2020
ML conf EU
Online

ML conf EU 2020

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.
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Una introducción al aprendizaje por transferencia en NLP y HuggingFace
32 min
Una introducción al aprendizaje por transferencia en NLP y HuggingFace
Thomas Wolf
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
Visión por Computadora utilizando OpenCV
32 min
Visión por Computadora utilizando OpenCV
Beril Sirmacek
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 Revolución de la Evolución
31 min
La Revolución de la Evolución
Robert Plummer
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
Cómo convertir cualquier producto en un producto de aprendizaje automático
33 min
Cómo convertir cualquier producto en un producto de aprendizaje automático
Shivani Poddar
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
Enseñando ML y AI a los Programadores
34 min
Enseñando ML y AI a los Programadores
Laurence Moroney
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 learningartificial intelligence
Aprendizaje profundo de transferencia para la visión por computadora
8 min
Aprendizaje profundo de transferencia para la visión por computadora
Dipanjan Sarkar
Sachin Dangayach
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
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
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
Ampliando la adopción de IA con AutoML
9 min
Ampliando la adopción de IA con AutoML
Bernhard Suhm
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 learningartificial intelligence
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
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.
tensorflowmachine learninginnovationtensorflow react
Aumenta la productividad con el ecosistema de Keras
30 min
Aumenta la productividad con el ecosistema de Keras
Haifeng Jin
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.
tensorflowmachine learningkeras
DeepPavlov Agent: Marco de código abierto para IA conversacional multihabilidad
27 min
DeepPavlov Agent: Marco de código abierto para IA conversacional multihabilidad
Mikhail Burtsev
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
Aprendizaje automático en el borde utilizando TensorFlow Lite
8 min
Aprendizaje automático en el borde utilizando TensorFlow Lite
Håkan Silfvernagel
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
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
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
¿Puedes Cantar con Todas las Voces de las Características?
8 min
¿Puedes Cantar con Todas las Voces de las Características?
Yama Anin Aminof
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
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
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
¡Nunca vuelvas a tener un Jupyter Notebook inmantenible!
26 min
¡Nunca vuelvas a tener un Jupyter Notebook inmantenible!
Marco Gorelli
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.
datavizmachine learning
Dabl: Aprendizaje Automático Automático con la Participación Humana
35 min
Dabl: Aprendizaje Automático Automático con la Participación Humana
Andreas Müller
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
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
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
Prácticas con TensorFlow.js
160 min
Prácticas con TensorFlow.js
Workshop
Jason Mayes
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.
tensorflowmachine learning
Introducción al Aprendizaje Automático en la Nube
146 min
Introducción al Aprendizaje Automático en la Nube
Workshop
Dmitry Soshnikov
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.
azuremachine learning
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