Up and Running with Deep Learning

Table of Contents

Creating Intelligence: An Applications-First Approach to Machine Learning

The State of Machine Learning Today - Carlos Guestrin (Dato, Inc.)

Intelligent Applications Using Machine Learning - Carlos Guestrin (Dato, Inc.)

Thinking in Terms of Apps, Not Algorithms - Carlos Guestrin (Dato, Inc.)

Making Machine Learning Simple to Use - Carlos Guestrin (Dato, Inc.)

Build Applications Quickly Scalably - Carlos Guestrin (Dato, Inc.)

Fundamentals of GraphLab Create - Carlos Guestrin (Dato, Inc.)

Demo: Building an End-to-End Intelligent Application - Carlos Guestrin (Dato, Inc.)

Defining Intelligent Microservices - Carlos Guestrin (Dato, Inc.)

Building Trust in Machine Learning Models - Carlos Guestrin (Dato, Inc.)

Exploring Why a Machine Learning Model Makes a Prediction - Carlos Guestrin (Dato, Inc.)

Dato Machine Learning Platform - Carlos Guestrin (Dato, Inc.)

Deploying Deep Learning at Scale

What is Deep Learning? - Naveen Rao (Nervana)

Deep Learning Model Architectures - Naveen Rao (Nervana)

Automating Human Tasks - Naveen Rao (Nervana)

Challenges in Deep Learning - Naveen Rao (Nervana)

Nervana Compute Topology - Naveen Rao (Nervana)

Nervana Platform, with Neon Deep Learning Framework - Naveen Rao (Nervana)

TensorFlow: Machine Learning for Everyone

Pushing the Boundaries of Machine Learning - Rajat Monga (Google)

What is TensorFlow? - Rajat Monga (Google)

TensorFlow: Under the Hood - Rajat Monga (Google)

Machine Learning in TensorFlow - Rajat Monga (Google)

Updates to TensorFlow - Rajat Monga (Google)

TensorFlow Applications, including Spark - Rajat Monga (Google)

Google Cloud Machine Learning Platform - Rajat Monga (Google)

TensorFlow: Large-scale Analytics and Distributed Machine Learning with TensorFlow, BigQuery, and Dataflow (Apache Beam)

Deep Learning and Distributed Training - Kazunori Sato and Amy Unruhand (Google)

Google Brain, Google Cloud - Kazunori Sato and Amy Unruhand (Google)

Neural Networks in Production - Kazunori Sato and Amy Unruhand (Google)

Demo: Cloud-vision API Explorer - Kazunori Sato and Amy Unruhand (Google)

Demo: Cloud-speech API - Kazunori Sato and Amy Unruhand (Google)

TensorFlow for Deep Learning - Kazunori Sato and Amy Unruhand (Google)

Learning New Image Classes using Apache Beam - Kazunori Sato and Amy Unruhand (Google)

Demo: Using Cloud Datalab - Kazunori Sato and Amy Unruhand (Google)

A Scalable Implementation of Deep Learning on Spark

What is an Artificial Neural Network? - Alexander Ulanov (Hewlett-Packard Labs)

Implementing the Multilayer Perceptron in Spark - Alexander Ulanov (Hewlett-Packard Labs)

Basic Linear Algebra Subprograms (BLAS) - Alexander Ulanov (Hewlett-Packard Labs)

Scalability - Alexander Ulanov (Hewlett-Packard Labs)

Scalability Testing - Alexander Ulanov (Hewlett-Packard Labs)

SparkNet: Training Deep Networks in Spark

Sparknet for Deep Learning - Robert Nishihara (UC Berkeley)

How to Read Data into Neural Nets - Robert Nishihara (UC Berkeley)

How to Use SparkNet - Robert Nishihara (UC Berkeley)

Distributed Training for Neutral Nets - Robert Nishihara (UC Berkeley)

Experiment Using AlexNet - Robert Nishihara (UC Berkeley)

Experiment Using GoogleNet - Robert Nishihara (UC Berkeley)

Parallelization Schemes - Robert Nishihara (UC Berkeley)

SparkNet Code Paper - Robert Nishihara (UC Berkeley)