IDS offers 5 day corporate workshops to train engineers and data scientists on ins and outs of deep learning at scale. Post workshop, we will support your trainees analyze your data, diagnose its problems and produce the fastest, most accurate results possible.

Deep Learning will significantly improve accuracy of your models and help your company gain a competitive advantage.

This workshop introduces basic concepts of Deep Learning. It provides an overview of Deep Learning algorithms and Frameworks and their applications. The course comprises of lectures and a series of practical labs. The participants should obtain a solid knowledge of machine learning basics to apply it in practice or for a foundation to a deeper study in the subject area. The training is held in the Python programming language.

Programming experience (preferably in Python) is necessary for maximum benefit. We assume no prior knowledge in machine learning, however some knowledge in statistics will be helpful.


Introduction to Deep Learning
Download and install TensorFlow
Overview of TensorFlow
Data flow graph
TensorFlow at the command line
NN Playground
Train your first TensorFlow neural net model
Building the computation graph
Launching a graph session
Visualize your training using TensorBoard
Evaluating your model


Introduction to convolutional neural networks
History, concepts
CNNs in TensorFlow
Code walkthrough on MNIST data set
Testing the model
Prepare the data
Build the graph
Build a multilayer Convolutional Network
Train and evaluate the model

Day 3

TensorFlow Serving
Architecture overview
Servers, sources, managers
Code walkthrough on TensorFlow Serving
Train and Export TensorFlow Model
Serve the exported model
Test and run the Server
Further applications using CNNs
Code walkthrough on CIFAR-10 data set
Basic analysis on CIFAR-10 data set

Day 4

Introduction to recurrent neural networks
History, concepts
RNNs in TensorFlow
Code walkthrough on language data set
Identify and load Penn Tree Bank dataset
Preliminary analysis
Penn Tree Bank dataset

Day 5

RNNs for machine translation
Code walkthrough on second language data set
Machine translation data set
Download a machine translation data set
Begin translation
More on RNNs
WMT 15 data set
Completion of machine translation exercise

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