Tensorflow Dev Summit Recap

Keynote:

• TF Goal:
  • to introduce ML to everyone
• TF feature:
  • Scalable
  • Performance
• Widely usage for TF
• Multiple device for TF
  • TPU
    • ASIC
    • 10x faster
• TF for everyone
  • TF for form
  • Fried Chicken Nugget Server
  • TensorKart
  • Generative Arts with TensorFlow
  • TV popstar face generator with DCGAN
  • TF for qualcomm’s Hexagon DSP
  • Konpeki https://aucnet-ibs.com/konpeki/2016/09/29/konpeki.html
• TF 1.0
  • Feature:
    • Fast
    • Flexible
    • Product-Ready
  • More ML
    • K-means
    • SVM
    • Random Forest

XLA

• What is XLA (Accelerated Linear Algebra)
  • JIT Complication
  • JIT
    • Program built at runtime
    • Low-Overhead complication
  • TF-Level Block Diagram
• Why excited about XLA
  • Server-side speedups
  • XLA’s JIT compilation and specialization
  • Model-shaped benchmark wins up to 60%
  • SyntaxNet from 200us —> 5 us
  • Mobile footprint reduction
  • Cross-compile for AMR, PPC, x86
  • LSTM model for mobile: 2.6 MB —> 600 KB (4x reduction)
  • XLA’s high-leve optimizer
• Caveats:
  • Not all Ops compile
  • Not everything is faster
• JIT :
  • Improvement lot in GPU
  • But still WIP in CPU (slower when use CPU)

TensorFlow High-Level API

• Original Tensorflow:
  • Flexible,
  • Extensible,
  • Maintainable
  • No Out-of-the-bix algorithm
• Fast Iteration
  • Estimator could train, fit, predict for models.
• Encodes best practices
• Deploy with Tensorflow Serving
• Distribution
• High Level API
  • Layer
  • Estimator (1.1)
  • Canned Estimator (1.2)
• Keras
  • tf.keras (1.2) tf.contrib.keras(1.1)
  • tensorflow.layer and keras.layer is the same
  • run keras on tensorflow help keras user
    • Use distribution training
    • Cloud ML
    • Tensor Serving
• Wide & Deep Learning
  • memorization and generalization
  • Memorization:
    • Bird can fly
  • Generalization
    • Bird with wing can fly
  • https://www.tensorflow.org/tutorials/wide_and_deep

Lightening Talks:

• Rayan Z
  • How to teach machine learning to non-tech people.
  • GDG
  • Women in Tech
• Sin c
  • How your data could be trusted.
• Alex B
  • bbx.ai NLU company
• Donghyun Kwak
  • Policy Learning in Sparkse Reward Home Simulation with Introspec
  • Home Robot
  • 26% improvement
• Tomoyuki Chikanaga:
  • Magellan block
    • Make GCP service as building BLOCKS
  • Inspection of cloud machine learning hyper parameter tuning
• Sprawit Saengkyongam (James)
  • Agoda ranking DS
  • Session-basd recommendation with RNN.
    • Collaborative Filter
    • Session-based
  • https://www.tensorflow.org/tutorials/recurrent
• Sung Kim
  • 70% code are redundant
  • Ongoing:
    • iOS to Android
    • Auto determine copy homework
• Jeongkyu Shin
  • A distributed code snippet execution engine.Sorna.io
  • Slide
• Luke (freelancer in Australia)
• Masahiko Adachi (GDE)
  • Robot with Neural Controller
• Norihiro Shimoda
  • TFUG (300 member per meetup)
• Mithuhisa Ohta (deep learning team leader)
  • Image classification by car
  • Obejct Detection
  • Anomaly Detection
  • Robot pickup staff by speaking
• Karthik Muthuswamy(GCPUG)
  • Object Detection
    • Challenge: Need enough resource
• Thia Kai Xin
  • Data Scientist SG
  • Big data SG
  • Current situation:
    • Lots of requirement (Data Scientsit) in SG, but no supply.
    • Student could not meet market
• Andrew Stevens (CTO and Architect : two company)
  • (Security Analysis) Anomalies in time series for RNN
  • Challenge:
    • Data –> Build Data As A Service.
  • Passonate about:
    • Kickoff tensorflow user group in SG and AUS
• Yoshihiro Sugi
  • Face recognition of pop stars
    • 100000 labeled dataset (manually, everyday)
  • Face generation by DCGAN
    • https://github.com/sugyan/tf-dcgan
• Martin Andrews
  • A usb flash drive for jupyter tensorflow 1.0
    • Also include notebook
      • CNN
      • RNN
      • Reinforcement learning
  • github
• “Ta” ex-facebook data scientist
  • News Feed Tanking with Human-in-the-loop
  • Thai Programmer Association
• Sujoy Roy (SAP)
  • SAP Clea: Lots of machine learning product (CV, Bot, IOT…)
• Talha Obaid - Samantec Email Security
  • Semantec
    • Scikit Learn, Spark
  • PyData SG meetup
    • 2.1k members
• Amit Kapoor (Teaching DS)
  • Teach ML
    • Provide learning path, enable sharing
  • Visualization by markdown (model-vis Approach)
• Nichal & Raghotham (UnnatiData Labs)
  • PyData
  • Music Generation
• Christin (Master Student in Malasia)
  • Round text detect (also other direction of text detection)