PyVideo.org · PyData LA 2019 (original) (raw)
Accelerate your NumPy Data Science Workloads and Deep Learning Applications
AI meets Fashion for product retrieval with multi-modally generated data
Build an AI-powered Pet Detector in Visual Studio Code
Building Named Entity Recognition Models Efficiently using NERDS
Carol Willing: Keynote
Datasets and machine learning models versioning using open source tools
Dynamic programming for machine learning: Hidden Markov Models
Fitting Many Dimensions into One: The Promise of Hierarchical Indices for Data Beyond Two Dimensions
IBM Code and Response: Open Sourcing Natural Disaster Preparedness and Relief
Machine Learning Model Evaluation Metrics
MAP all the things
Modeling Search Term Revenue: Using Embedding Layers to Manage High Cardinality Categorical Data
Open Source is Better Together: GPU Python Libraries Unite
Preparing messy data for supervised learning with vtreat
RAPIDS: Open Source GPU Data Science
Sameer Singh: Keynote | PyData LA 2019
Simplicity For Scale: Analyzing 15 Million DNA Samples With Python
Tackling Homelessness with Open Data
What You Got Is What You Got
A Guide to Modern Hyperparameter Tuning Algorithms
A "Supremely" Light introduction to Quantum Computing
A/B Testing in Python
Ahead in the Clouds: How to get started with serverless on Google, Amazon & Microsoft
Bokeh Maps: Making an interactive map for your next web application
Building a Data Driven Organization
Data and ETL with Notebooks in Papermill
Evaluation of Traditional and Novel Feature Selection Approaches
Gradient Boosting for data with both numerical and text features
Introducing Autoimpute: a Python Package for Grappling with Missing Data
Kyle Polich: Keynote | PyData LA 2019
Learning Topology: Topological Techniques for Unsupervised Learning
Lightning Talks | PyData LA 2019
Making data relevant to business. Its harder than you think!
Milana Lewis: Keynote | PyData LA 2019
To Production and Beyond: How to Manage the Machine Learning Lifecycle with MLflow
What's Data Science Reporting?
Write the Docs!
Analyzing genetic networks using neural networks
Computer Vision with PyTorch
Experimental Machine Learning with HoloViz and PyTorch in Jupyterlab
Git-ting along with others
Introduction to Data Analysis with Python datatable
Introduction to H2O AutoML with Python
Kedro + MLflow – Reproducible and versioned data pipelines at scale
Reinforcement Learning: Pac-Man
Turn Python Scripts into Beautiful ML Tools
