Intel® Deep Learning Workshop
Using Intel® Distribution for Python, you can

a) achieve faster Python application performance—right out of the box—with minimal or no changes to your code
b) Accelerate NumPy*, SciPy*, and scikit-learn* with integrated Libraries such as Intel® MKL and Intel® DAAL and
c) Access the latest vectorization and multithreading instructions

The Intel® DAAL helps speed big data analytics, by providing highly optimized algorithmic building blocks for all data analysis stages. It provides a rich set of algorithms, ranging from the most basic descriptive statistics for datasets to more advanced data mining and machine learning algorithms. Intel DAAL is a highly optimized library of computationally intensive routines supporting a variety of Intel CPU architectures.

Intel® VTune™ Amplifier helps in performance analysis, by collecting key profiling data and presents it with a powerful interface that simplifies its analysis and interpretation

TensorFlow* is a leading deep learning and machine learning framework. It now integrates optimizations for Intel® Xeon® processors. This workshop provides information for developers who want to build, install, and explore TensorFlow optimized on Intel architecture.

What You Will Learn:
  • Build and Install TensorFlow* on Intel® Architecture Neural Networks with TensorFlow
  • Hands on Session on Training the model from scratch for a Sample Dataset , Intel® optimization for TensorFlow*- for Performance & Short exercise for Model optimization
  • Training a Convolutional Neural Network with TensorFlow to perform image classification.
  • Optimizations and performance comparisons.


    • Welcome
    • Intel® Distribution for Python
    • Parallel Python w/ Intro to MPI
    • Intel® VTune™ Amplifier for Python
    • Intel® Data Analytics Acceleration Library (Intel® DAAL)
    • Introduction to Intel Optimized Frameworks
Deep learning Hands-on:
    • Training the model from scratch for a Sample Dataset
    • Use transfer learning from a pre-trained Model
    • Intel® optimization for TensorFlow*- for Performance
    • Short exercise for Model optimization
Participants’ pre-requisites:
This workshop is suitable for participants with Intermediate level of Python and Linux awareness. Expected to bring their own Laptops.

Laptop: Recommended -4GB RAM with modern browser and WiFi adapter


Jayaraman Mahalingam (JAY)

Jayaraman (Geo Lead for APJ Intel® AI Academy Tech Program) develops strategic partnership and engagement with top APJ Universities to take the Intel® Artificial Intelligence (AI) Story & vision. He works with technical content & infrastructure enabling teams and lead the Intel® program for the GEO. He is Masters in IT from University of Newcastle and got Certificate in Data Science from University of Washington

Mukesh Gangadhar

Mukesh Gangadhar (Staff Tech Lead) is an architect working on optimizing software applications on x86 platforms, especially on the cloud and artificial intelligence.


Date: 24 November 2018

Timings: 9:30 AM to 4:30 PM

Duration of Workshop: 6-7 Hours

Location: INTEL campus, Intel Technology India Pvt Ltd 136, HAL Old Airport road Opp Leela Palace Hotel Bangalore 560069 Bengaluru

Venue: BUM Room, Ground Floor

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