tag:peatix.com,2011:1 2021-11-15T09:44:22+08:00 Peatix iTrain tag:peatix.com,2018:event-443877 2018-10-30T09:30:00MYT 2018-10-30T09:30:00MYT

Back by popular demand! Fee: FREEVenue: Asean Data Analytics Exchange (ADAX) @ Bangsar South"Mainstream access to deep learning technology will greatly impact most industries over the next three to five years."So what exactly is deep learning? How does it work? And most importantly, why should you even care? Deep learning is used in the research community and in industry to help solve many big data problems such as computer vision, speech recognition, and natural language processing. Practical examples include: Vehicle, pedestrian and landmark identification for driver assistance Image recognition Speech recognition and translation Natural language processing Life sciences What You Will Learn Understand the intuition behind Artificial Neural Networks Apply Artificial Neural Networks in practice Understand the intuition behind Convolutional Neural Networks Apply Convolutional Neural Networks in practice Understand the intuition behind Recurrent Neural Networks Apply Recurrent Neural Networks in practice Understand the intuition behind Self-Organizing Maps Apply Self-Organizing Maps in practice Understand the intuition behind Boltzmann Machines Apply Boltzmann Machines in practice Understand the intuition behind AutoEncoders Apply AutoEncoders in practice Agenda 9:00am - 9:30am: Arrivals and registration9:30am - 11:30am: Deep Learning by Tarun Sukhani11:30am-12:00pm: QnA12:00pm - END: EnquiriesYou may bring your laptop along to follow on some exercises, but not required.  Pre-requisite No background in deep learning is required for this training Basic python understanding can be useful for some exercises The mathematical and theoretical aspects of deep learning will NOT be covered by this training - and they're not a requirement to complete the labs, reading the Wikipedia page of Deep Learning would be a good start if you're interested. Who Should Attend Anyone interested in Deep Learning Any intermediate level people who know the basics of Machine Learning or Deep Learning, including the classical algorithms like linear regression or logistic regression and more advanced topics like Artificial Neural Networks, but who want to learn more about it and explore all the different fields of Deep Learning Anyone who is not that comfortable with coding but who is interested in Deep Learning and wants to apply it easily on datasets Anyone who wants to start a career in Data Science Any data analysts who want to level up in Deep Learning Any people who are not satisfied with their job and who want to become a Data Scientist Any people who want to create added value to their business by using powerful Deep Learning tools Any business owners who want to understand how to leverage the Exponential technology of Deep Learning in their business Any Entrepreneur who wants to create disruption in an industry using the most cutting edge Deep Learning algorithms Instructors Biodata: Tarun SukhaniTarun Sukhani has 16 years of both academic and industry experience as a data scientist over the course of his career. Starting off as an EAI consultant in the USA, Tarun was involved in a number of integration/ETL projects for a variety of Fortune 500 and Global 1000 clients, such as BP Amoco, Praxair, and GE Medical Systems.While completing his Master's degree in Data Warehousing, Data Mining, and Business Intelligence at Loyola University Chicago GSB in 2005, Tarun also worked as a BI consultant for a number of Fortune 500 clients at Revere Consulting, a Chicago-based boutique IT firm focusing on Data Warehousing/Mining projects. Tarun continues to work within the BI space, most recently focusing his time on Deep/Reinforcement Learning projects within the Fintech sector.Tarun Sukhani has worked on parametric statistical modeling as well within the Data Science and Big Data Science space, using tools such as SciPy in Python and R and R/Hadoop for Big Data projects.

tag:peatix.com,2018-10-08 02:09:34 2018-10-08 02:09:34 The event description was updated. Diff#376744