January 4, 2020

174 words 1 min read

Visualizing machine learning models in the Jupyter Notebook (sponsored by Bloomberg LP)

Visualizing machine learning models in the Jupyter Notebook (sponsored by Bloomberg LP)

Chakri Cherukuri offers an overview of the interactive widget ecosystem available in the Jupyter notebook and illustrates how Jupyter widgets can be used to build rich visualizations of machine learning models. Along the way, Chakri walks you through algorithms like regression, clustering, and optimization and shares a wizard for building and training deep learning models with diagnostic plots.
Talk Title Visualizing machine learning models in the Jupyter Notebook (sponsored by Bloomberg LP)
Speakers Chakri Cherukuri (Bloomberg LP)
Conference JupyterCon in New York 2018
Conf Tag The Official Jupyter Conference
Location New York, New York
Date August 22-24, 2018
URL Talk Page
Slides Talk Slides
Video

Chakri Cherukuri offers an overview of the interactive widget ecosystem available in the Jupyter notebook, including ipywidgets and bqplot, and illustrates how Jupyter widgets can be used to build rich visualizations of machine learning models. Along the way, Chakri walks you through algorithms like regression, clustering, and optimization and shares a wizard for building and training deep learning models with diagnostic plots. This session is sponsored by Bloomberg LP.

algorithm ecosystem jupyter visualization deep learning machine learning book cluster optimization
comments powered by Disqus
Distributed TensorFlow on Hops

Distributed TensorFlow on Hops

December 30, 2019

Fabio Buso offers demonstrations of frameworks for building distributed TensorFlow applications on the Hops platform and walks you through the whole model lifecycle, from debugging and visualizing models on TensorBoard to parallel experimentation and distributed training (with the help of Spark) to model deployment and inferencing using TensorFlow Serving and Kubernetes.
Practical considerations when shifting to using deep learning for your text data

Practical considerations when shifting to using deep learning for your text data

December 3, 2019

Emmanuel Ameisen and Yan Kou share a guide for moving your company toward deep learning using a collection of NLP best practices gathered from conversations with 75+ teams from Google, Facebook, Amazon, Twitter, Salesforce, Airbnb, Capital One, Bloomberg, and others.
Apache Spark programming

Apache Spark programming

November 29, 2019

Brooke Wenig walks you through the core APIs for using Spark, fundamental mechanisms and basic internals of the framework, SQL and other high-level data access tools, and Sparks streaming capabilities and machine learning APIs.
Visualizing high-dimensional biological data with Clustergrammer-Widget in the Jupyter Notebook

Visualizing high-dimensional biological data with Clustergrammer-Widget in the Jupyter Notebook

January 4, 2020

Nicolas Fernandez offers an overview of Clustergrammer-Widget, an interactive heatmap Jupyter widget that enables users to easily explore high-dimensional data within a Jupyter notebook and share their interactive visualizations using nbviewer.
Deep learning with TensorFlow and Spark using GPUs and Docker containers

Deep learning with TensorFlow and Spark using GPUs and Docker containers

December 10, 2019

In the past, you needed a high-end proprietary stack for advanced machine learning, but today, you can use open source machine learning and deep learning algorithms available with distributed computing technologies like Apache Spark and GPUs. Nanda Vijaydev and Thomas Phelan demonstrate how to deploy a TensorFlow and Spark with NVIDIA CUDA stack on Docker containers in a multitenant environment.
Building deep reinforcement learning applications on BigDL and Spark

Building deep reinforcement learning applications on BigDL and Spark

December 3, 2019

Deep reinforcement learning is a thriving area and has wide applications in industry. Arsenii Mustafin shares his experience developing deep reinforcement learning applications on BigDL and Spark.