+65 6591 8608

Wednesday28 June 2017

Deep Learning on GPGPUs

We introduce the most important applications of deep learning, using CAFFE and TensorFlow platforms.

We focus on performance considerations - for the training phase using multicore CPUs, distributed systems (cluster, cloud) and Nvidia GPU accelerators using NVIDIA CUDA compatible frameworks (NVIDIA Tesla / NVIDIA GTX), as well as the deployment phase on various platforms including embeddable boards such as NVIDIA Jetson TK1. Trainees will have deep understanding of Deep Learning frameworks and their real world usage for developing production applications. 

 Deep learning on GPGPUs training (3 days)


This course will provide deep understanding of Deep Learning platforms, their performance, and real world usage for developing applications

 Knowledge in C++ and Python 
Audience: Developers, Project Leaders 
OS: Linux




Day 1


Morning (9AM-12PM) - Introduction & Demo


• Deep Learning & Machine Learning 

• Usages & applications

• Supervised & Unsupervised Learning

• Learning strategies

 Neural Network history and classification



Day 2


Morning (9AM-12PM) - Supervised Learning Fundamentals: Theory and Practice with TensorFlow / Caffe (2/2)


• Convolutional neural networks

• Image classification using convolutional filtering

• BackPropagation and Recurrent Neural Network 

Afternoon (1PM-5PM) - Supervised Learning Fundamentals: Theory and Practice with TensorFlow / Caffe (1/2)


• Deep Learning available Frameworks overview

• Supervised Learning Fundamental 1: feed-forward network, stochastic gradient descent, testing and validation, regularization

• non-linear classifier using TensorFlow / Caffe - define network, training model, refine accuracy, visualize network, deploy

Afternoon (1PM-5PM) - Caffe and TensorFlow comparison, Supervised Learning Application



• Introduction
• Comparison between TensorFlow and Caffe: TensorFlow Serving & other applications

• cuDNN & Caffe, cuDNN and TensorFlow






Day 3


Morning (9AM-12PM) - Unsupervised Learning Theory and Practice


• Auto-encoders

• Dynamic Networks

• Competitive Networks




Afternoon (1PM-5PM) - Practical Issues


• Data Preprocessing

• Training algorithms

• Training initialization and Stop Conditions

• Post-Training Analysis and case Studies: Fitting, Pattern Recognition, Prediction, Sensitivity analysis, Overfitting, Clustering

JA Minisite

2857.orig.q75.o0 - Copy IntelTPP amd  tesla preferred partner  quadro partner  qctlogo e   Mellanox APAC Partner   1