PyData Córdoba Argentina 2019
2019-09-27..2019-09-29
Córdoba, Argentina
| title | author | length |
|---|---|---|
| The evolution of basketball with data science | Ivana Seric | 41m |
Chronicle of claims and systems not buzzwords the conference lists the shift from box scores to camera tracking and the concrete effects: SportVU player… | ||
| Empowering Business to make model-based marketing decisions | Erica Mason | 27m |
A marketing media‑mix model becomes an interactive tool: fitted time‑series parameters feed a convex optimization that maximizes orders under per‑channel and… | ||
| FKLearn, A functional machine learning library | Henrique Lopes | 48m |
A practical demo of FKLearn that claims to make models pure functions so they are models as functions and easily serialized for production; it actually wraps… | ||
| Writing effective bayesian programs using TensorFlow and TFP | Junpeng Lao | 01h 22m |
Walks through concrete code patterns for Bayesian modeling with TensorFlow and Tensorflow Probability: uses JointDistributionSequential for generative and… | ||
| Introducción a pandas (español) | Marc Garcia | 01h 36m |
This tutorial is aimed at people new to pandas. Knowledge of Python will be useful but is not essential. JupyterLab will be used, and attendees are expected to… | ||
| Data Products, From 0 to data science pipeline | Daniel Imberman, Gonzalo Diaz | 34m |
Starts with a working prototype in Jupyter notebook then parameterizes it with papermill to produce many variants, uses Apache Airflow DAGs to grid‑search… | ||
| Clustering Data | Georgina Flesia | 01h 20m |
Covers several clustering algorithms and how to analyze their results: K-means and Mean Shift (centroid-based), DBSCAN and OPTICS… | ||
| Machine Learning over Graphs | Federico Albanese | 01h 06m |
Introduces machine learning methods applied to graph-structured data, demonstrating algorithms and techniques with open-source code and public datasets. Covers… | ||
| Pyplan, a Graphical Programming Python IDE for Creating Data Analytics Apps | Jorge Douglas | 01h 07m |
Pyplan is a graphical Python IDE for building data analytics applications that combines flow-based visual programming with notebook-like fast execution and… | ||
| Computer Vision in Practice | Paula Martinez, Rodrigo Beceiro | 44m |
Introduces practical computer vision problems and the state of the art, covering image classification, object detection, image segmentation, and instance… | ||
| A (Very Gentle) Introduction to Quantum Computing | Rodolfo Bonnin | 38m |
Introduces core concepts of quantum computing for data scientists, explaining qubits, superposition, measurement, entanglement and quantum algorithms by… | ||
| Section Extraction from Texts | Gianmarco Cafferata, Francisco Lopez | 40m |
Presents document sectioning as a sequence labeling task and compares different labeling methods. Evaluates Hidden Markov Models and Conditional Random Fields… | ||
| Offer Optimization, a Dual Approach | Gonzalo Barrera Borla | 44m |
A practical near-optimal bidding strategy for demand-side platforms (DSPs) in real-time bidding (RTB) auctions that scales to thousands of campaigns and… | ||
| Breaking the 80/20 Dilemma in Data Science Projects | Leandro Ferrado | 41m |
The talk analyzes the ‘80/20 dilemma’ in data science where most project time is spent on data discovery, collection, cleaning, and encoding business rules… | ||
| From POC to Production - Infrastructure for Machine Learning | Carlos de la Torre | 47m |
Describes how MercadoLibre extended its Fury microservices platform to support machine-learning workflows by building Fury Data Apps. Covers the gap between… | ||
| Data Science Best Practices with Kedro | Carlos Gimenez | 40m |
Kedro is presented as a framework to enforce data science best practices and standardize workflows so teams avoid inconsistencies and rework. The talk explains… | ||
| Python ❤️ Open Data! | Patricio Del Boca | 43m |
An overview of Python’s central role in the Open Data ecosystem, explaining what open data is, why it’s valuable, and how to obtain it. A detailed look at CKAN… | ||
| A better PyData ecosystem, one commit at a time | Marc Garcia | 53m |
We are in 2019 and the PyData ecosystem seems extremely healthy. Python is growing in popularity and the PyData ecosystem is increasingly becoming the de-facto… | ||
| Know What You Don't Know: Tools to Understand Uncertainty in DL | Julian Eisenschlos | 46m |
Bridges practical deep learning with Bayesian inference by introducing Bayesian Neural Networks and the fundamentals of Bayesian inference. Explains how… | ||
| Decentralized Machine Learning | Thierry Silbermann | 34m |
Claims that models train on-device without centralizing raw data and that privacy holds up when updates are Secure Aggregation. Demonstrations use Federated… | ||
| Automatic Suggestions in Conversational Systems | Andres Pipicello | 30m |
Automatic suggestion features for conversational systems are presented, focusing on techniques to generate and rank reply or action suggestions within chatbots… | ||
| Reinforcement Learning with OpenAI Gym | Ana Laura Diedrichs | 35m |
An introduction to reinforcement learning covering its definition, how it differs from supervised and unsupervised learning, and motivating applications… | ||
| Embeddings... Embeddings Everywhere | Milagro Teruel | 31m |
The talk surveys the use of embeddings across data science, explaining how vector embeddings represent items (words, images, users) and enable tasks like… | ||
| A Hitchhiker's Guide to designing a Bayesian library in Python | Junpeng Lao | 01h 00m |
Details on building a Bayesian library with PyMC3 and the PyMC4 redesign, comparing backends like TensorFlow Jax autograd PyTorch Theano, and why choosing a… | ||
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