MLTrain 1

Sep 24 @ 08:30 AM - 05:00 PM

Children's Hospital, Atlanta

Event Speakers

Schedule

  • 24 Sep 2016

24 Sep 2016

08:00 am - 09:00 am
Seminar Room

Registration

09:00 am - 10:00 am

Session 1: TensorFlow paper

  • Symbolic coding: From imperative to declarative programming
  • Tensor as first class citizen
  • The computational graph model
  • ControlFlow: Mutating variables
  • Managing hardware
  • Device communication model
  • Distributed computing
  • Lossy data compression
  • Automatic Differentiation
  • Synchrony vs Asynchrony
  • Debugging
10:00 am - 11:00 am

Session 2: Creating and Visualizing a Computational Graph

  • Variables vs placeholders
  • Operations
  • Creating a computational session
  • Managing Hardware, cpus and gpus
  • Using TensorBoard for Visualization
  • Breaking declaratively: Mutating variables with controlFlow.
Break
11:15 am - 12:15 am

Session 3: Linear Algebra with TF

  • Sparse/Dense Matrix/Vectors operations
  • Kronecker Products in TF
  • From Matrices to Tensors
  • Tensor Tiling: the map operator of TF
  • Reductions on Tensors
  • Thinking in batch
  • Limitations of TF
Lunch
12:45 pm - 12:45 pm

Session 4: Optimization in TF

  • Creating a symbolic objective function
  • Computing the gradients
  • Build your own simple gradient Descent optimizer
  • The mini-batch
  • Inside the optimizer TF class
  • Tweaking predefined optimizer by touching the gradients
  • Presentation and Parameter tuning of famous optimizers: AdamOptimizer, RmsPropOptimizer, and AdaGrad
  • Build your first linear regression in 3 lines!
Break
02:30 pm - 04:00 pm

Session 5: Coding ML algorithms

  • Continue building linear models (logistic)
  • Using different objectives: L1, L2, Cross Entropy, Hinge Loss, and Maximum Likelihood
  • Adding L1/L2 regularization
  • Compare your implementation with the tensorFlow Linear module
  • Regularizing with dropout
  • Code your first multilayer perceptron (MLP)
  • Debugging your model with TensorBoard
04:00 pm - 05:00 pm

Session 6: Wide and deep modeling

  • Presentation of the paper Wide and Deep recommender systems
  • Build your first wide and deep model
  • Beyond recsys, extending to wide and deep classifiers