Books & Videos

Table of Contents

Chapter: Creating Intelligence: An Applications-First Approach to Machine Learning

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

01m 27s

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

03m 52s

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

02m 2s

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

02m 42s

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

03m 24s

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

05m 20s

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

04m 9s

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

01m 59s

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

05m 9s

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

04m 52s

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

02m 24s

Chapter: Deploying Deep Learning at Scale

What is Deep Learning? - Naveen Rao (Nervana)

01m 37s

Deep Learning Model Architectures - Naveen Rao (Nervana)

03m 43s

Automating Human Tasks - Naveen Rao (Nervana)

07m 19s

Challenges in Deep Learning - Naveen Rao (Nervana)

03m 44s

Nervana Compute Topology - Naveen Rao (Nervana)

04m 16s

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

07m 38s

Chapter: TensorFlow: Machine Learning for Everyone

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

02m 39s

What is TensorFlow? - Rajat Monga (Google)

03m 16s

TensorFlow: Under the Hood - Rajat Monga (Google)

02m 59s

Machine Learning in TensorFlow - Rajat Monga (Google)

05m 53s

Updates to TensorFlow - Rajat Monga (Google)

03m 58s

TensorFlow Applications, including Spark - Rajat Monga (Google)

04m 42s

Google Cloud Machine Learning Platform - Rajat Monga (Google)

01m 38s

Chapter: 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)

05m 40s

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

04m 57s

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

03m 7s

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

02m 33s

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

02m 36s

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

05m 5s

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

05m 51s

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

06m 32s

Chapter: A Scalable Implementation of Deep Learning on Spark

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

03m 58s

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

04m 4s

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

07m 52s

Scalability - Alexander Ulanov (Hewlett-Packard Labs)

05m 51s

Scalability Testing - Alexander Ulanov (Hewlett-Packard Labs)

04m 19s

Chapter: SparkNet: Training Deep Networks in Spark

Sparknet for Deep Learning - Robert Nishihara (UC Berkeley)

11m 13s

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

03m 40s

How to Use SparkNet - Robert Nishihara (UC Berkeley)

07m 59s

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

08m 8s

Experiment Using AlexNet - Robert Nishihara (UC Berkeley)

02m 33s

Experiment Using GoogleNet - Robert Nishihara (UC Berkeley)

01m 59s

Parallelization Schemes - Robert Nishihara (UC Berkeley)

02m 32s

SparkNet Code & Paper - Robert Nishihara (UC Berkeley)

04m 44s