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Machine learning with TensorFlow
- 1. invites you to attend weekend workshop on Machine Learning with TensorFlow Saturday & Sunday Sep 22-23 Sat Sep 23 10am - 12:30pm Introduction & experiences with TensorFlow Pavan Vutukuru & Sruti Jain Sat Sep 23 1 - 5pm Hands-on demo/exercises with TensorFlow Pavan Vutukuru & Sruti Jain Sun Sep 24 11am - 12noon Analytics using ML with TensorFlow - WebEx Dr. Scott Streit Sun Sep 24 1 - 2:30pm IOT TensorFlow Deep Learning Demo Russ Bodnyk $5 for UTD folks, $25 fee for all guests Lunch included on Saturday Register @ bit.ly/prof-dev-utd
- 2. Workshop – Saturday, Sep 23 - Contents Introduction + Need of Computationally efficient frameworks in Deep learning and Deep Mind project. + The origin of TensorFlow and progress since open-source launch. + Tensorflow Performance & scalability. Machine learning - Complete implementation & Comparison + Normal implementation (Gradient Computation) & Tensorflow implementation execution time comparisons, the use of broadcasting and understanding the graphical computational model of Tensorflow for basic operations like the dot product, argmax, element-wise multiplication etc. Using operators like argmax or matmul or anything + demo for TensorFlow contrib and why they have these basic implementations + TensorBoard: Visualize TensorFlow Graphs, monitor training performance & exploring how the models represent the data step by step. Our experiences with the TF Framework + ML at eBay: how ebay is leveraging Tensorflow, Tensorflow serving and kubernetes to increase scalability and reliability of machine learning models in production. + Sruti will speak about image processing in TF, ML toolkit, integration of Keras & Tensorflow, general problems one encounter while using TF in research. Tensorflow internal features + TensorFlow Serving Models: TF Serving production models can be used for applying a trained model in another application that are used in production environments. + Tensor2Tensor (Newly introduced Google Library based on TF) : T2T facilitates the creation of state-of-the art models for a wide variety of ML applications, such as translation, parsing, image captioning and more, enabling the exploration of various ideas much faster than previously possible. + Support for implementation of large scale linear models that lets you jointly train a linear model and a deep neural network. External features + TensorFlow external compilers: Speed is everything for machine learning and Tensorflow can make use of XLA, JIT or other compilation techniques to minimize execution time & optimize computing resources. + Scaling up ML models using Distributed TensorFlow up to hundreds of TPU’s & GPU’s and briefing on architectural designs. + Mobile & Embedded TensorFlow: Android to launch TensorFlow Lite for mobile machine learning. Conclusion + Comparison with other Deep learning frameworks like theano, caffe, Pytorch, CNTK (Computational Network Toolkit by Microsoft) + Other exciting big-time AI models built on Tensorflow in various domains speech recognition, image recognition, various visual detection tasks, language modeling & language translation.
- 3. Talk & Demo – Sunday, Sep 24 Analytics using ML with TensorFlow – WebEx presentation Presenter: Scott Streit, Computer Science Innovations, LLC (CSI), www.compscii.com CSI focuses on Machine Learning and Computer Security. CSI performs analytics using Machine Learning, primarily with Tensorflow. CSI work with Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs) and a variety of other model types. CSI have merged Big Data with Machine Learning in developing production systems for clients. IOT TensorFlow Deep Learning Demo Presenter: Russ Bodnyk, Coded Intelligence IOT data is exploding in a world of increasing complexity as new devices connect every second. Applying intelligence to data is no longer optional, it is requisite. Security, responsiveness, and interactivity can be improved by increasing number of intelligence processes that run on IOT devices. Russ will demonstrate it with TensorFlow Deep Learning processes running on device.