• [5 Day Training Course] Neural Networks and Deep Learning

    Find the detailed agenda here: https://bit.ly/2ltBVTu This five-day hands-on course is designed for data scientists seeking a better understanding and knowledge of main technology trends driving Deep Learning. Attendees will get a clear understanding of Deep Learning technology, practical scenarios to build, train and apply algorithms of fully connected Deep Neural Networks, strategies to configure the key parameters in a neural network's architecture.

  • [3 Day Training Course] Machine Learning

    Needs a location

    More details are here: http://bit.ly/2yrHzi9 Why this training? In this three-day course you will be given clear explanations of machine learning theory, practical scenarios of machine learning algorithms, linear and non-linear regression, overfitting, regularization, loss functions, neural networks, introduction to the backpropagation algorithm and best practices in using different neural networks architectures. The participants will gain experience in machine learning techniques, neural network configuration and model optimization in R and Python. This course is based on hands-on exercises and gives developers an extended overview of various tools, services and frameworks which become essential in machine learning. More details are here: http://bit.ly/2yrHzi9

  • [5 Day Training Course] Neural Networks and Deep Learning

    Find the detailed agenda here: https://bit.ly/2ltBVTu This five-day hands-on course is designed for data scientists seeking a better understanding and knowledge of main technology trends driving Deep Learning. Attendees will get a clear understanding of Deep Learning technology, practical scenarios to build, train and apply algorithms of fully connected Deep Neural Networks, strategies to configure the key parameters in a neural network's architecture.

  • [3 Day Training Course] Machine Learning

    Needs a location

    More details are here: http://bit.ly/2yrHzi9 Why this training? In this three-day course you will be given clear explanations of machine learning theory, practical scenarios of machine learning algorithms, linear and non-linear regression, overfitting, regularization, loss functions, neural networks, introduction to the backpropagation algorithm and best practices in using different neural networks architectures. The participants will gain experience in machine learning techniques, neural network configuration and model optimization in R and Python. This course is based on hands-on exercises and gives developers an extended overview of various tools, services and frameworks which become essential in machine learning. More details are here: http://bit.ly/2yrHzi9

  • ChatBots – the next generation of messaging apps

    Agenda: 6:30 - Doors open. Networking. Beer & snacks. 6:45 - Opening remarks. 7:00 - Building Natural Language Chatbots by Ruze Richards, Data Scientist 7:40 - Q&A break 7:45 - Business Reports Using NLP: languages tracking contexts and scaled errors by Vishal Anand, Data Scientist 8:25 - Q&A break 8:35 - Wrap-up _________________________________________________________ DETAILED AGENDA: Speaker: Ruze Richards, Data Scientist Title: Building Natural Language Chatbots Abstract: Chatbots are a great way to interact with users and customers while obtaining and providing data needed to get things done within a familiar natural language interface. In this presentation, Ruze will talk about his own experiences developing a chatbot, and tools and techniques which will make it easier to create a chatbot with personality. Bio: Ruze is a Data Scientist working in the cybersecurity field, where he builds machine learning and NLP based systems to collect, analyze and present data from a number of sources in order to detect and thwart attackers, and make it easier for internal teams to coordinate a response. He has also been a Big Data and distributed systems architect previously for enterprises and startups through his career. _______________________________________________________ Speaker: Vishal Anand, Data Scientist Title: Business Reports Using NLP: languages tracking contexts and scaled errors Abstract: Vishal will talk of how wildly the language resources available may vary for creating business reports for customers and dive deep into the paradigms of changing contexts amongst the multiple resources. Represent information available into knowledge graphs and using adaptive models to respond to ever-changing streaming data via some of more recent models like GANs, LSTMS and VAEs. This data treatment for business reports is an interface to market data, as chatbots is for human conversations. Speaker Bio: Vishal is an inventor and seasoned data-scientist authoring two patent-filings & a trade-secret in the Big-Data, AI/ML space, and has created state-of-the-art pattern-prediction systems at the scale of Fortune 500 firms. He is also an alumnus of the Indian Institute of Technology (IIT) and pursues NLP as a unified model extending from the traditional text-space into sound, images, videos, and anything natural Please register here: http://bit.ly/2GwKKbo

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  • ONLINE WEBINAR: Image Classification Done Simply Using Keras and TensorFlow

    *Please register HERE (https://goo.gl/IIaqKP)to get the unique link to join the webinar* Are you willing to learn how to build an image classifier using Keras with a TensorFlow backend? Join the webinar (https://goo.gl/IIaqKP) to learn more! Overview The fact that computers can see is just not that amazing anymore. But, the techniques for teaching a computer to do this are now simpler and more refined than ever. In this webinar, Rajiv Shah (https://www.linkedin.com/in/rcshah?authType=NAME_SEARCH&authToken=TVD5&locale=en_US&srchid=854662381470146485173&srchindex=1&srchtotal=1&trk=vsrp_people_res_name&trkInfo=VSRPsearchId%3A854662381470146485173%2CVSRPtargetId%3A15452467%2CVSRPcmpt%3Aprimary%2CVSRPnm%3Atrue%2CauthType%3ANAME_SEARCH) will describe the process of building an image classifier using Keras with a TensorFlow backend and discuss how to extend the code to your own pictures to make a custom image classifier. The approach here uses Keras, which is emerging as the best library for building neural networks. The code here also assumes you are using TensorFlow as the underlying library. The presentation will give a basic understanding of image classification and show the techniques used in industry to build image classifiers. You will learn: - How to build a simple convolutional network - How to augment the data - How to use a pretrained network - How to use transfer learning by modifying the last few layers of a pretrained network The classification will be based on the classic example of classifying cats and dogs. The code for the presentation can be found here (https://github.com/rajshah4/image_keras). About the Presenter: Rajiv Shah (https://www.linkedin.com/in/rcshah/) is a senior data scientist at Caterpillar and an Adjunct Assistant Professor at the University of Illinois at Chicago. Rajiv is an active member of the data science community in Chicago with an interest into public policy issues, such as surveillance in Chicago. He has a PhD from the University of Illinois at Urbana Champaign. You find more of his projects at www.rajivshah.com (https://rajivshah.com/).

  • Deep Dive into TensorFlow #3

    eBay NYC

    ***WAITLIST ONLY*** Please register HERE (http://bit.ly/2mvOhss)as the venue needs full names for security purposes Many thanks to eBay (http://www.ebay.com/)for sponsoring and hosting the TensorFlow meetup! Agenda: 6:30 - Doors open. Networking. 7:00 - NLP from scratch in TensorFlow by Neal Lewis 7:45 - Q&A break. 8:00 - Talk #2 by Adi Haviv 8:30 - Q&A break 8:40 - Wrap-up. DETAILED AGENDA: NLP from scratch in TensorFlow by Neal Lewis Natural Language Processing (almost) from Scratch by Ronan Collobert et. al ( https://arxiv.org/abs/1103.0398 ) was a game shifting approach to NLP machine learning tasks. It helped introduce Neural Network architectures to the NLP world by creating a two architectures to accomplish multiple NLP tasks with less feature engineering and near state of the art performance. In this talk Neal Lewis will share his implementation - in TensorFlow - of the two NN designs - an MLP with windowed input, and a CNN with sentence input - for two NLP tasks: Part Of Speech tagging and Chunking. Neal will go over the tasks and architecture, then jump into the code. Feedback and discussions on best practices for TensorFlow will be much welcomed! Speaker: Neal Lewis Neal is an engineer specializing in Machine Learning and Natural Language Processing. Talk #2 to be announced soon Speaker: Adi Haviv Adi is Managing an Applied Research and engineering team at eBay NYC, focusing on building Personalized Search at eBay

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  • ONLINE WEBINAR: Deep Learning Using TensorFlow and TensorFlow-Slim

    *******PLEASE REGISTER HERE (http://paas.ly/TensorFlow-webinar)****** A Convolutional Neural Network (CNN) is a powerful machine learning technique from the field of deep learning. Do you want learn how CNNs work and how to build and train such networks? Join the webinar (http://paas.ly/TensorFlow-webinar) to learn more! In this webinar, Dipendra Jha (https://www.linkedin.com/in/dipendra009?authType=OUT_OF_NETWORK&authToken=k4Fx&locale=en_US&srchid=854662381478188385692&srchindex=1&srchtotal=2413&trk=vsrp_people_res_name&trkInfo=VSRPsearchId%3A854662381478188385692%2CVSRPtargetId%3A194620481%2CVSRPcmpt%3Aprimary%2CVSRPnm%3Atrue%2CauthType%3AOUT_OF_NETWORK), Ph.D. student in Computer Science from Northwestern University, will provide a brief introduction to Deep Learning and TensorFlow, followed by actual implementation and demonstration of MNIST image classification using convolutional neural networks (CNNs). Agenda: -Introduction to the Fundamentals of Deep Learning -The Strengths of Using TensorFlowImage Classification Using CNNs for MNIST Dataset - How CNNs work and How to Build and Train Such Networks - The Usage of TensorFlow for large-scale application of Deep Learning to Big Datasets in IndustryQ&A Join the webinar to (http://www.altoros.com/blog/event/deep-learning-using-tensorflow-and-tensorflow-slim/#get_record): - Learn more about the fundamentals of deep learning, followed by the strengths of using TensorFlow - Look about image classification using CNNs for MNIST dataset - Discover how CNNs work and how to build and train such networks - Examine how TensorFlow can be used for large-scale application of deep learning to big datasets in industry About the presenter: Dipendra Jha (https://www.linkedin.com/in/dipendra009?authType=OUT_OF_NETWORK&authToken=k4Fx&locale=en_US&srchid=854662381478188385692&srchindex=1&srchtotal=2413&trk=vsrp_people_res_name&trkInfo=VSRPsearchId%3A854662381478188385692%2CVSRPtargetId%3A194620481%2CVSRPcmpt%3Aprimary%2CVSRPnm%3Atrue%2CauthType%3AOUT_OF_NETWORK) is is a fourth-year Ph.D. student in Computer Science from Northwestern University. He is exploring the field of Deep Learning and Machine Learning using High Performance Computing (HPC) systems in the CUCIS lab under Prof. Alok Choudhary. His research focuses on scaling up deep learning and machine learning models using HPC (CPU and GPU) clusters, and their application to Material Science and Social Media Analytics. Before this, he completed his Master’s in Computer Science from Northwestern University. He worked in the field of Computer Networks, Distributed Systems and Cellular Networks in Aqualab under Prof. Fabian Bustamante. During this period, his research spanned from Web Page Performance Optimizations, Network Measurements and Community WiFi to Inter-domain Routing in Cellular Networks, IXPs and Content Distribution Networks (CDNs). He completed his Bachelors’ in Computer Engineering from Tribhuvan University in Nepal.

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  • WEBINAR: Teaching Recurrent Neural Networks Using TensorFlow

    PLEASE REGISTER HERE (http://www.altoros.com/blog/event/teaching-recurrent-neural-networks-using-tensorflow/) Recurrent neural networks (RNNs) are designed to model sequential information and are widely used to solve the problems of speech recognition, language modeling, translation, and image captioning. Are you willing to learn how to perform basic mathematical calculations or recognize handwriting using RNNs and TensorFlow together? Join the webinar to learn more! In this webinar, Rajiv Shah (https://www.linkedin.com/in/rcshah?authType=NAME_SEARCH&authToken=TVD5&locale=en_US&srchid=854662381470146485173&srchindex=1&srchtotal=1&trk=vsrp_people_res_name&trkInfo=VSRPsearchId%3A854662381470146485173%2CVSRPtargetId%3A15452467%2CVSRPcmpt%3Aprimary%2CVSRPnm%3Atrue%2CauthType%3ANAME_SEARCH), Adjunct Assistant Professor at University of Illinois, will provide a brief introduction to recurrent neural networks. Agenda: - How Recurrent Neural Networks are used - Tensorflow Playground - Learn a Sine Wave - Learn to Add - Learning HandwritingQ&A Why join the webinar (http://www.altoros.com/blog/?post_type=tribe_events&p=15405&preview=true#get_record): Discover how recurrent neural networks operate.Learn the prime reasons for choosing an RNN rather than a standard networkWalk through code in TensorFlow for modeling a sine wave, performing basic addition, and generating handwriting Who should attend: This webinar will be of interest to Data Scientists, Software Engineers and Entrepreneurs in the areas of Connected Cars, Internet of Things/Industrial Internet, Medical Devices, Financial Technology (blockchain) and predictive apps/APIs of all sorts. About the presenter: Rajiv Shah (https://www.linkedin.com/in/rcshah?authType=NAME_SEARCH&authToken=TVD5&locale=en_US&srchid=854662381470146485173&srchindex=1&srchtotal=1&trk=vsrp_people_res_name&trkInfo=VSRPsearchId%3A854662381470146485173%2CVSRPtargetId%3A15452467%2CVSRPcmpt%3Aprimary%2CVSRPnm%3Atrue%2CauthType%3ANAME_SEARCH) is a data scientist at a Global Supply Network Division and an Adjunct Assistant Professor at the University of Illinois at Chicago. He is an active member of the data science community in Chicago with projects and publications related to surveillance and red light cameras. He has a PhD from the University of Illinois at Urbana Champaign.

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