What is Deep Learning, and Why is it Important?
What is Deep Learning, and Why is it Important?
Deep Neural Networks (Deep Learning) seeks to replicate the way brains process information. Imagine a progression of nodes (neurons), each containing some aspect of your knowledge, and with the ability to communicate back and forth – only algorithmically. This is a game-changing innovation that will enable practitioners to harness previously untapped knowledge and dive deeper than has ever been possible.
Deep Learning is what enables self-driving cars to function, Spotify to create highly customized playlists, Google to identify faces & animals in videos, and how Siri can understand and process free speech in milliseconds. Deep Learning is revolutionizing almost every facet of our lives, and those who truly know how to harness its power are in extremely high demand.
Join us for the Accelerated Programme in Deep Learning and Artificial Intelligence Starting February 5, 2018
Babbage Simmel, in partnership with Soothsayer Analytics, brings you this unique program that’s been designed to put you on track for the hottest job of the century in the fastest possible time.
This program provides a highly applicable, hands-on learning experience involving Deep Learning (Convolution & Recurrent Networks, etc.) and other advanced forms of Artificial Intelligence.
Participants will learn to leverage such innovations for:
- Predictive Analytics
- Text Analytics & NLP
- Pattern Recognition
- Feature Engineering
- Time Series Analysis
- Computer Vision
Goal
This is an intensive, 2-week program designed to help working professionals and STEM grads become proficient in the most disruptive technologies ever developed in Data Science. You will learn to apply Deep Neural Networks towards a variety of problems and how to ensure that what you build is fully leveraged by your company.
Who Should Attend?
Current and aspiring Data Scientists looking to increase their value, learn bleeding-edge skills, and solve real-world problems with A.I. & Deep Learning. Though not required, it is recommended that participants hold at least a STEM Bachelor’s. Lab sessions will take place in Python, so knowledge of that or other programming languages (e.g. R, Java, C#) is ideal.
Learn to…
- Architect A.I. & Deep Learning solutions.
- Build A.I. & Deep Learning applications.
- Interface with business users who may struggle to understand the technology.
- Recognize the limitations of AI & Deep Learning and avoid mistakes during production.
- Utilize best practices to ensure proper implementation and adoption.
- Demystify and visualize results for easy consumption by non-technical stakeholders.
- Lead A.I. & Deep Learning projects.
Curriculum
Week One
- Day 1: Intro to Artificial Intelligence/Machine Learning
- Day 2: Shallow Learning Fundamentals
- Day 3: Deep Learning (Deep Neural Networks) | Multi-Layered Perceptron
- Project + Test (Out of Class)
Week Two
- Day 4: Convolution Neural Networks
- Day 5: Recurrent Neural Networks
- Day 6: Scaling Deep Neural Networks + Program Recap
- Final Project & Evaluation (Out of Class)
Mentor & Professor
Murthy Kolluru | Ph.D., Carnegie Mellon University
Dr. Murthy holds a Ph.D. from Carnegie Mellon, started his career as a Rocket Scientist, and founded one of the top Data Science programs in the world (International School of Engineering). In addition to being a globally-recognized Data Science academician, he has 2 decades of experience consulting with, training, and conducting research for dozens of Fortune 1000 companies.
For more information, and to register for the program, click HERE.
