Rajesh Jeyapaul
Cloud Solution Architect
IBM
location_on India
Member since 4 years
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Rajesh Jeyapaul
Specialises In (based on submitted proposals)
Rajesh K Jeyapaul is a Cloud Solution Architect in the IBM EcoSystem & Developer team.He is focused on enabling partners and developers on IBM cloud solution especially on Bluemix , IoT , Mobility and Big Data. He has been with IBM for 16+ years and having the industry experience for around 19 years.
He started his carrier on Avionics real time simulation as a Research Fellow in India’s Defense organization. At IBM , he has worked on various technologies involving: Java virtual machine, Operating System, Server virtualization, Cloud Management etc. He is an IBM Plateau holder with 4 patents and 3 publication. He has co-authored various technical books and is a IBM RedBooks author. He blogs regularly at IBM’s developer works.
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Accelerating Deep Learning - Developers checklist !!
45 Mins
Case Study
Intermediate
It took 4 days to run FFT (Fast Fourier transform) on a scanned grey scale image using 486 processor system way back in 1994, my engineering project , Digital Image Processing. Now, it takes few hours to train a deep learning model on a 32 core CPU system, say Music information retrieval (MIR), which has around 100k audio tracks of size 1000GB.
The evolution in the neural network has brought in the capability of rich feature extraction from thousands of images to identify a pattern in the data for further classification. This needs a high compute system. An image of say , 320 x 280 resolution needs 268,800 flops of computation (320x280x3 (RGB) )
We have the GPU enabled system now which is capable of supporting parallel processing with its huge numbers of processing threads.So why to wait for days/hours if it can be done in hours or minutes . CUDA (Computer Unified Device Architecture) is the framework which supports the device level data movement and memory management.
As a Deep Learning developer how we can leverage the GPU processor to accelerate the DL training process ? I am sharing my experience of training the DL models using the GPU processor based environment and with Keras & TF framework.
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Cognitive application development on cloud using Bluemix
90 Mins
Workshop
Intermediate
We are moving into an era from smarter homes to cognitive home based solution. In this session, we will talk about the cognitive home solution based on IBM Bluemix Watson cognitive api and Bluemix Watson IoT.
.The Demo cum hands on lab will involve working on Watsion api and IoT platform.
step1:
Train Watson to classify the image.It could be a selfie of yours. Allow Watson to decide whether you are a friend or foe.
Step 2:
Register and connect the Intel Edison board with Bluemix IoT. Implement the MQTT communication using the IBM Bluemix watson IoT library ,"iotf"
Step 3:
Take the input from Watson and communicate to IoT device. IoT device will take an action based on the response from Watson. If Watson says, you are a friend, you are allowed to enter the home (LED glow), If watson says, you are a foe, a buzzer will alarm
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