0:26
InfoQ Live (Microservices Virtual Event on Aug 25th)
InfoQ
10:59
Very Large Datasets with the GPU Data Frame
11:18
What One Should Know About Spark MLlib
14:47
(Past), Present, and Future of Apache Flink
10:05
Optimizing Spark
13:10
A Whirlwind Overview of Apache Beam
11:26
Gimel: Commoditizing Data Access
11:15
Deep Learning for Language Understanding (at Google Scale)
9:29
Detecting Similar Id Documents Using Deep Learning
8:07
Basics of Deep Learning: No Math Required
TensorFlow Jumpstart
10:12
Machine Learning: Predicting Demand in Fashion
10:26
JupyterLab: The Next Generation Jupyter Web Interface
10:10
Building a Security System with Image Recognition & an Amazon DeepLens
5:52
The Basics of ROS Applied to Self-Driving Cars
8:50
pDB: Abstraction for Modeling Predictive Machine Learning Problems
11:41
Building (Better) Data Pipelines with Apache Airflow
8:29
Transmogrification: The Magic of Feature Engineering
11:37
Continuous Delivery for AI Applications
11:29
Serverless for Data Science
TensorBoard: Visualizing Learning
8:37
A/B Testing for Logistics: It All Depends
11:36
Tooling & Setup for My Neural Network
11:13
PyTorch by Example
11:23
When Do You Use Machine Learning vs. a Rules Based System?
14:20
Two Effective Algorithms for Time Series Forecasting
9:07
NVIDIA Jetson
11:48
Introduction to Forecasting in Machine Learning and Deep Learning
49:19
What does it take to build a data science capability?
