AI Learning Accelerator
Sathya Chandran, PhD
Free

WEBINAR: Dumb & Dumber vs Ocean’s 11: Tackling evolving, sophisticated fraud with AI

Viktor Gamov
Free

MEETUP: Kafka on Kubernetes: Just because you can, doesn't mean you should!

Dimitris Katsios
Free

MEETUP: Data pipeline and Model training: A simple DL project

Anais Dotis-Georgiou
Free

When Holt-Winters is better than Machine Learning for Time Series Data

ODSC West
Free

ODSC West 2019 Warm-Up: Machine Learning

Walter Menendez
Free

Luigi the 10x Plumber: Containerizing & Scaling Luigi in Kubernetes

Gabriel Bianconi
Free

Introduction to Face Processing with Computer Vision

Sean Owen
Free

Detecting Bias with SHAP: What do Developer Salaries Tell us about the Gender Pay Gap?

ODSC India
Free

ODSC India 2019 Warm-Up: Deep Real Learnathon

Francesca Lazzeri
Free

Automated and Interpretable Machine Learning

ODSC India
Free

ODSC India 2019 Warm-Up

Stepan Pushkarev
Free

Kubeflow, MLFlow and beyond - augmenting ML delivery

Kriti Doneria
Free

Explainable AI and interpret-ability of AI solutions: Strategic overview challenges and caveats

Avni Gupta
Free

Introduction to GANs with TensorFlow

ODSC India
Free

ODSC India 2019 Warm-Up: Machine Learning & Deep Learning

Błażej Osiński
Free

Model-based Reinforcement Learning for Atari

Aaron K. Baughman Baughman
Free

Ethical Large-Scale Artificial Intelligence within Sports

Randy Zwitch
Free

OmniSci and RAPIDS: An End-to-End Open-Source Data Science Workflow

Alessio Antonioli
Free

Predicting advertising campaign delivery using Machine Learning (Random Forests)

Guglielmo Iozzia
Free

Distributed Deep Learning with Keras and TensorFlow on Apache Spark

John Kane
Free

Mitigating gender-bias in speech emotion recognition

Aonghus McGovern, Ph.D.
Free

Responsible AI - Principles and Methods

Felix Dorrek
Free

Generative Models and Synthetic Data for Data Privacy

Giulia Carella
Free

Going spatial: statistical learning for spatial data

In this webinar, the speaker will talk you through the best practices to make statistically sound decisions in the field of spatial data science.