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QCon.ai San Francisco 2018
Videos
1 — Serverless for Data Science
2 — Very Large Datasets with the GPU Data Frame
3 — TensorFlow Jumpstart
4 — JupyterLab: The Next Generation Jupyter Web Interface
5 — Building a Security System with Image Recognition & an Amazon DeepLens
6 — The Basics of ROS Applied to Self-Driving Cars
7 — Basics of Deep Learning: No Math Required
8 — Building (Better) Data Pipelines with Apache Airflow
9 — A Whirlwind Overview of Apache Beam
10 — (Past), Present, and Future of Apache Flink
11 — Continuous Delivery for AI Applications
12 — Transmogrification: The Magic of Feature Engineering
13 — TensorBoard: Visualizing Learning
14 — A/B Testing for Logistics: It All Depends
15 — Tooling & Setup for My Neural Network
16 — PyTorch by Example
17 — When Do You Use Machine Learning vs. a Rules Based System?
18 — NVIDIA Jetson
19 — Introduction to Forecasting in Machine Learning and Deep Learning
20 — Deep Learning for Language Understanding (at Google Scale)
21 — Detecting Similar Id Documents Using Deep Learning
22 — Optimizing Spark
23 — What does it take to build a data science capability?
24 — Two Effective Algorithms for Time Series Forecasting
25 — Gimel: Commoditizing Data Access
26 — What One Should Know About Spark MLlib
27 — pDB: Abstraction for Modeling Predictive Machine Learning Problems
28 — Machine Learning: Predicting Demand in Fashion
