- Artificial Neural Networks
- Convolutional Neural Networks
- Recurrent Neural Networks
- Self-Organizing Maps
- Boltzmann Machines
- AutoEncoders
- Anyone who is interested in Deep Learning
- Students who have at least high school knowledge in math and who want to know more about Deep Learning
- Anyone with an intermediate level knowledge who knows 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 is interested in Deep Learning and want to apply it easily on datasets
- Any student in college who wants to start a career in Data Science
- Any data analyst who wants to level up in Deep Learning
- Anyone who is not satisfied with their job and who wants to become a Data Scientist
- Anyone who wants 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 entrepreneurs who want to create disruption in an industry using the most cutting edge Deep Learning algorithms
- 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.
- You may bring your laptop along to follow on some exercises, but not required.
Deep Learning Technology (Preview Session)
Session Summary
Deep learning is a fast-growing methodology used in research and various industries to help solve big data problems such as computer vision, speech recognition, and natural language processing. Some practical examples of deep learning applications include image recognition, vehicle, pedestrian, and landmark identification for driver assistance and more.
What will you learn?
Learn the fundamental concepts in Deep Learning and gain the understanding of the intuition and application of:
Agenda
9:00am - 9:30am: Arrivals and registration
9:30am - 11:30am: Deep Learning by Tarun Sukhani
11:30am-12:00pm: QnA
12:00pm - END: Enquiries
Who Should Attend?
Tarun Sukhani has 16 years of both academic and industry experience as a data scientist. Starting off as an Enterprise Application Integration (EAI) consultant in the USA, Tarun was involved in a number of integration and 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 Business Intelligence space, most recently focusing his time on Deep/Reinforcement Learning projects within the Fintech sector. For this, 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.