Haris Pozidis- Snap ML: Accelerated, Accurate,Efficient,Machine Learning| PyData Global 2020

Gael Varoquaux- Dirty Data Science Machine Learning On Non Curated Data| PyData Global 2020

Jesper Dramsch - How to guarantee your machine learning model| PyData Global 2020

Arnaud Van Looveren - Monitoring machine learning models in production | PyData Global 2020

Stuart Lynn - Using EOLearn to build a machine learning pipeline | PyData Global 2020

Liucija Latanauskaite - Why I didn't use deep learning for my image recognition| PyData Global 2020

Hussain Sultan- Growing Machine Learning Platforms in the Enterprise|PyData Global 2020

Rebecca Bilbro - Thrifty Machine Learning| PyData Global 2020

Serg Masis - Interpretable Machine Learning with Python

Using Serverless, Python, R and Machine learning to save a country | PyData Athens

Markus Loning - Introduction to Machine Learning with Time Series | PyData Fest Amsterdam 2020

Jim Dowling - Hopsworks.AI - A feature Store for Machine Learning | PyData Fest Amsterdam 2020

Romeo Kienzler - Privacy by Design Machine Learning | PyData Fest Amsterdam 2020

MLflow - An open platform for the machine learning lifecycle - Abdulrahman Alfozan | PyData Riyadh

Robert Ness - Causal Modeling in Machine Learning | PyData Boston June Meetup

Alyssa Batula: What is Machine Learning, and How Do I Get Started? | PyData Indy 2019

Ben Fulton: Deep Learning Support at Indiana University | PyData Indy 2019

Mateusz Opala: Reproducible Machine Learning | PyData Warsaw 2019

Cyrus Vahid: Anyone can Build Great Deep Learning Applications - Deep Numpy | PyData Warsaw 2019

Jacek Komorowski: Football video analysis using Deep Learning | PyData Warsaw 2019

Pawel Cyrta: Sound Modelling - parametric methods and deep learning... | PyData Warsaw 2019

Keynote - Inga Strumke: Machine Learning can't do the thinking | PyData Warsaw 2019

Kasimov & Petrova: Machine Learning on big data in security applications | PyData Warsaw 2019

Robert Kostrzewski: Modern Machine Learning flow with Quilt and Polyaxon | PyData Warsaw 2019

Marina Volkova: Machine Learning Spacecraft Designing for Cybersecurity | PyData Warsaw 2019

Vladimir Osin, Milan Mulji: Managing Machine Learning Lifecycle with MLflow | PyData Eindhoven 2019

Axel Goblet: Scheduling machine learning pipelines using Apache Airflow | PyData Eindhoven 2019

Hayley Song: Experimental Machine Learning with Holoviz and PyTorch in Jupeyterlab | PyData LA 2019

Avik Das: Dynamics Programming for Machine Learning- Hidden Markov Models | PyData LA 2019

Maria Khalusova: Machine Learning Model Evaluation Metrics | PyData LA 2019

Dmitry Petrov: Machine Learning Models Versioning Using Open Source Tools | PyData LA 2019

Hao Jin: Accelerate NumPy Data Science Workloads and Deep Learning Applications | PyData LA 2019

Thierry Silbermann: Decentralized Machine Learning | PyData Córdoba

LT: Alexander Engelhardt - Adversarial Machine Learning

Dr. Benjamin Werthmann: Law, ethics and machine learning - a curious ménage... | PyData Berlin 2019

Tutorial: Get to grips with pandas and scikit-learn

Peter Wang: Rethinking Open Source in the Era of Cloud & Machine Learning | PyData Berlin 2019

Tutorial: Managing the end-to-end machine learning lifecycle with MLFlow

Benjamin Bossan: skorch: A scikit-learn compatible neural network library... | PyData Berlin 2019

Sarah Diot-Girard: Privacy-preserving Machine Learning for text processing | PyData Berlin 2019

Adrin Jalali: Current affairs, updates, and the roadmap of scikit-learn and... | PyData Berlin 2019

Andreas Hantsch: Machine learning with little data - from digital twin to... | PyData Berlin 2019

Tutorial: Using machine learning for Level Generation in Snake (video-game)

Alexander Engelhardt: Interpretable Machine Learning: How to make black box... | PyData Berlin 2019

Jacob Barhak: Visualizing Machine Learning of Units of Measure using PyViz | PyData Austin 2019

Hao Jin: Accelerate large-scale machine learning with NP on MXNet | PyData Austin 2019

Samuel Taylor: Machine Learning Crash Course | PyData Austin 2019

Saloni Jain: Speeding up Machine Learning tasks using GPUs in Python | PyData Austin 2019

Federico Albanese: Machine Learning over graphs | PyData Córdoba

Henrique Lopes: FKLearn, A functional machine learning library | PyData Córdoba

Interpretability of Machine Learning Predictions - Marcel Spitzer [PyData Südwest]

Ethan Rosenthal: Time series for scikit-learn people | PyData New York 2019

Tom Augspurger: Scalable Machine Learning with Dask | PyData New York 2019

Samuel Rochette: Quantifying uncertainty in machine learning models | PyData New York 2019

Aditya Lahiri: Dealing With Imbalanced Classes in Machine Learning | PyData New York 2019

Thomas J Fan: Deep Dive into scikit-learn's HistGradientBoosting Classifier.. | PyData New York 2019

Moussa Taifi: Clean Machine Learning Code: Practical Software Engineering... | PyData New York 2019

Marianne Hoogeveen: The physics of deep learning using tensor networks | PyData New York City 2019

PyData Tel Aviv Meetup: Monitoring Machine Learning at Scale - Naama Horesh and Anna Reznikov

How to easily set up and version control your Machine Learning Pipelines | PyData Amsterdam 2019

Alejandro Saucedo: Guide towards algorithm explainability in machine learning | PyData London 2019

Elina Naydenova: Bridging health inequalities through machine learning | PyData London 2019

Maria Navarro: Quantifying uncertainty in Machine Learning predictions | PyData London 2019

Igor Gotlibovych: Deep Learning and Time Series Forecasting for Smarter Energy | PyData London 2019

Max Halford: Online Machine Learning with Creme | PyData Amsterdam 2019

Wilder Rodrigues: Improving Machine Learning Workflow | PyData Amsterdam 2019

Marianne Hoogeveen: Plant Factory: Sensor, Data, Machine Learning | PyData Amsterdam 2019

Benjamin Bengfort: Visual Diagnostics for More Effective Machine Learning | PyData Miami 2019

Chao Han: Deep Learning vs. Conventional Machine Learning | PyData Miami 2019

Jules Damji: Platform for Complete Machine Learning Lifecycle | PyData Miami 2019

Mash Zahid: Applying Rigorous Machine Learning Methods in Business Strategy | PyData Miami 2019

Michoel Snow: Machine Learning & Healthcare | PyData Miami 2019

Zachary S. Brown: Deep Learning and Modern NLP | PyData Miami 2019

PyData Ann Arbor: Alexandra Johnson | Machine Learning Infrastructure

PyData Tel Aviv Meetup: Deep Learning for Named Entity Recognition - Kfir Bar

MedSpace - Medical Image Analysis with Bayesian Deep Learning - Felix Laumann

Deep Learning on Mobile Devices - William Grisaitis

PyData Tel Aviv Meetup: Deep Learning for NLP Workshop

PyData Ann Arbor: Sebastian Raschka | An Introduction to Deep Learning with TensorFlow

It is never too much: training deep learning models with more than one modality - Adam Słucki

Distributed deep learning and why you may not need it - Jakub Sanojca, Mikuláš Zelinka

Deep Learning Semantic Segmentation for Nucleus Detection - Dawid Rymarczyk

The Lifecycle of Artificial Intelligence with IBM's Deep Learning as a Service - Justin McCoy

The new kid on the block: deep learning with GluonNLP - Sneha Jha

Michael Bronstein - Geometric deep learning on graphs: going beyond Euclidean data

Machine Learning in the age of increasing Data Privacy Consciousness - Sam Talasila

End to End Machine learning pipelines for Python driven organizations - Nick Harvey

RESTful Machine Learning with Flask and TensorFlow Serving - Carlo Mazzaferro

Skorch - A Union of Scikit-learn and PyTorch - Thomas Fan

Building a Machine Learning Enabled Bot in the Cloud - Jerry Hargrove

PyData Ann Arbor: Haitham Maya & Brandon Stange | Methods for Interpretable Machine Learning

Keynote: Anima Anandkumar - Tensorly: A Flexible Python Framework for Machine Learning

Learning to Scale Data Science, Machine Learning, and Pandas with Ray and Modin - Devin Petersohn

Tamara Louie: Applying Statistical Modeling & Machine Learning to Perform Time-Series Forecasting

Detecting Signed and Unsigned Documents with Deep Learning - Beyond Transfer... - Jordan Bramble

Deploy and Use a Multiframework Distributed Deep Learning Platform on... - Animesh Singh, Tommy Li

Dean Allsopp - Hermeneutic Investigations: What Can Interpretable Machine Learning Do Today?

PyData Ann Arbor: Irmak Sirer | Deep Learning Reveals the Essence of Matt Damon

Data versioning in machine learning projects - Dmitry Petrov

The Face of Nanomaterials: Insightful Classification Using Deep Learning - Angelo Ziletti

Industrial ML - Overview of the technologies available...machine learning - Alejandro Saucedo

Towards automating machine learning - Dr Thorben Jensen

Deploying a machine learning model to the cloud using AWS Lambda - Dr. Benjamin Weigel

Scaling and reproducing deep learning on Kubernetes with Polyaxon - Mourad Mourafiq

PyData Tel Aviv Meetup: Deep Learning and Medical Imaging - Bella Fadida Specktor

PyData Tel Aviv Meetup: Scalable Deep Learning of Atomic Forces - Nataly Kuritz

Michael Craig - Machine Learning on molecular data

Hands-on introduction to Deep Learning with Keras and Tensorflow - Rodrigo Agundez

Forecasting airline passengers using designer machine learning - Alexander Backus, Jan van der Vegt

Thompson Sampling for Machine Learning - Ruben Mak

Scaling Machine Learning jobs with Kubernetes - Tarek Mehrez, Carsten Lygteskov Hansen

Bayesian Deep learning with 10% of the weights - Rob Romijnders

From cells to drug responses - machine learning in cancer research - Julian de Ruiter, Nanne Aben

Challenges in building Machine Learning models in production - Elena Sokolova

Evaluating fairness in machine learning with PyMC3 - Oliver Laslett

Who's singing? Automatic bird sound recognition with machine learning - Dan Stowell

Automatic tagging of short texts with scikit-learn and NLTK - Gilbert François Duivesteijn

PyData Tel Aviv Meetup: Building Machine Learning Models for Production - Elena Sokolova

Belal Chaudary - Transfer Learning for translating Sign Language from video to text

PyData Ann Arbor: Scott Sievert | NEXT - Machine Learning, Crowdsourcing, and Cartoons

Eric J. Ma - An Attempt At Demystifying Bayesian Deep Learning

Jeffrey Yau: Time Series Forecasting using Statistical and Machine Learning Models | PyData NYC 2017

Leon Yin - Reverse image search engines using out of the box machine learning libraries

Andrew Therriault - Learning in Cycles: Implementing Sustainable Machine Learning Models...

TensorTraffic - traffic prediction using machine learning - Pawel Gora

Building a Gesture Recognition System using Deep Learning - Joanna Materzyńska

Debugging machine learning - Michał Łopuszyński

Image generation with deep learning - Michał Jamroż

Teaching Machine Learning - Piotr Migdał

PyTorch a framework for research of dynamic deep learning models - Adam Paszke

Eduardo Peire - Using Machine Learning in Python to diagnose Malaria

PyData Tel Aviv Meetup: Uncertainty in Deep Learning - Inbar Naor

PyData Tel Aviv Meetup: Fundamentals of Deep Learning based ‘Object Detection’ - Idan Bassuk

Dave DeBarr - Using CNTKs Python Interface for Deep Learning

Vaibhav Singh, Jaroslaw Szymczak - Machine Learning to moderate ads

Ethics in Machine Learning Panel

Jo-fai Chow - Introduction to Machine Learning with H2O and Python

Irina Vidal Migallon - Deep Learning for detection on a phone

Andreas Dewes - Fairness and transparency in machine learning: Tools and techniques

Françoise Provencher - Biases are bugs: algorithm fairness and machine learning ethics

Adrin Jalali - The path between developing and serving machine learning models.

Robert Meyer - Analysing user comments with Doc2Vec and Machine Learning classification

Ethics in Machine Learning Panel Q&A

Sumit Kumar - In database Machine Learning with Python in SQL Server

Olivia Gunton - Designing for Guidance in Machine Learning

Rob Story - Machine Learning Infrastructure at Stripe: Bridging from Python JVM

Tom Radcliffe - Robust Algorithms for Machine Learning

Katie Porterfield - BrainDrain Using Machine Learning and Brain Waves to Detect Errors in Human

Claudia Guirao - Machine Learning to know if you R coming

Pascal van Kooten - DeepCare Chatbot - Generating answers to customers using Deep Learning and NLP

PyData Tel Aviv Meetup: Machine Learning Applied to Mice Diet and Weight Gain - Daphna Rothchild

Soledad Galli - Machine Learning in Financial Credit Risk Assessment

PyData Ann Arbor: Jennifer Marsman | Azure Machine Learning: Predict Who Survives the Titanic

PyData Ann Arbor: Daniel Whitenack | Scalable, Distributed, and Reproducible Machine Learning

Ian Ozsvald, Dr Gusztav Belteki & Giles Weaver - Machine learning with ventilator data

Nuno Castro - Ranking hotel images using deep learning

Andrew Rowan - Bayesian Deep Learning with Edward (and a trick using Dropout)

Stephen Whitworth - Building robust machine learning systems

Alexandr Notchenko - Analyzing 3D objects with power of Deep Learning and Cython

Mike Innes - Julia: A Fresh Approach to Machine Learning

Nitin Borwankar | Applying machine learning to software development to reduce bugs

Michael Manapat: Counterfactual evaluation of machine learning models

Cameron Davidson Pilon: Mistakes I've Made

Jay (Haijie) Gu: SFrame and SGraph

Chris Wilcox: Using Python and Azure Machine Learning

Shawn Scully: Creating an intelligent world at Dato

Josh Bloom: Keynote - A Systems View of Machine Learning

Joseph Sirosh: Keynote- Clouded Intelligence

Carl Kadie: PySnpTools - A New Open Source Library for Reading & Manipulating Matrix Data

Jake VanderPlas: Machine Learning with Scikit Learn

Stephanie Kim: Investigating User Experience with Natural Language Analysis

Alex Korbonits: Deep Learning with Python: getting started & getting from ideas to insights in mins

Alejandro Correa Bahnsen: CostCla a cost-sensitive classification library

Trent McConaghy: Rewiring the Internet for Ownership with Big Data and Blockchains

Paul Balzer: Running, walking, sitting or biking? - Motion prediction with acceleration and rotation

Ronert Obst: Smart cars of tomorrow: real-time driving patterns

Bugra Akyildiz - A Thorough Machine Learning Pipeline via Scikit Learn

Rajat Arya and Yucheng Low - Building Machine Learning Applications in Python

Cliff Click - H₂O Machine Learning

Rajat Arya - Deploying scikit learn Models in Production

Yucheng Low - SFrame - A Scalable, Out of Core Dataframe for Machine Learning

Andreas Mueller - Advanced scikit-learn

Michelle Fullwood - Grids, Streets & Pipelines: Making a linguistic streetmap with scikit-learn

Akira Shibata - Putting Together World’s Best Data Processing Research with Python

Daniel Blanchard - Simple Machine Learning with SKLL 1.0

Bugra Akyildiz - A Machine Learning Pipeline with Scikit-Learn

Alain Ledon & Amit Bhattacharyya - Logistics Regression & NFL

Radim Řehůřek - Faster than Google? Optimization lessons in Python.

Andreas Mueller - Commodity Machine Learning

Christian Thurau - Low-rank matrix approximations in Python

Trent McConaghy - Driving Moore's Law with Python-Powered Machine Learning: An Insider's Perspective

Burc Arpat - Why Python is Awesome When Working With Data at any Scale

Portia Burton - Know Thy Neighbor an Introduction to scikit-learn and K-NN

Sarah Guido - K-means Clustering with scikit-learn

Ryan Rosario - Sentiment Classification Using scikit-learn

Peter Prettenhofer - Gradient Boosted Regression Trees in scikit-learn

Bart Baddeley - Measuring Similarity & Clustering Data

Peter Prettenhofer - Gradient Boosted Regression Trees in scikit-learn

Linda Uruchurtu: A Beginner's Guide to Random Forests - R vs Python | PyData London 2014

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