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After clicking the “Registration” tab, please select “Operations Excellence” as the Program Category in order to select this Deep Learning Course.
This synchronous/live hands-on course, featuring direct access to the instructor, is designed for business analytics alumni and those with similar knowledge who wish to start using deep learning in their organizations. Virtual meetings occur twice per week during business hours, with each meeting lasting 75 minutes. This course also offers office hours, enabling students to obtain advice on their current deep learning company projects.
Bring your own project
Learners will be asked to complete an individual semester-long deep learning project and write a paper. This can be a project that a learner is currently working on or planning to start working on in their company. Open and one-on-one office hours will be offered on a weekly basis to talk through math, coding, and conceptual questions and issues.
- TensorFlow 2 in Python
- Computing with massive datasets using recursive estimation
- Deep supervised learning
- Predictive applications in business
High-level overview of topics covered in this course
- What are deep neural networks?
- When should I use deep neural networks?
- How can I look inside the deep neural network black box?
- Why should I use deep neural networks?
- How are neural networks trained from scratch?
Fundamentals of neural networks with TensorFlow
- Binary classification
- Multiclass classification
- Multi-label classification
- Activation functions
Training deep neural networks
- Faster optimization
- Vanishing/exploding gradients and solutions
Analyzing sequence data
- Recurrent Neural Networks
- How does a computer store images?
- Convolutional neural networks
The course features several group assignments and a bring-your-own project for learners to further develop their understanding of the content. In addition to the group assignments, short individual assignments are offered every session. This course is ideal for learners who work full-time and want to begin working with deep learning or refresh their knowledge of it.
Basic knowledge of Python and statistics is required to start this course.
Available Industries: Business Analytics & Statistics
Duration & Dates
Michel Ballings is an Assistant Professor, the James and Joanne Ford Faculty Research Fellow, and the Director of the JTV Center Intelligence Lab at the Department of Business Analytics and Statistics, Haslam College of Business, University of Tennessee. He is the President of the INFORMS Social Media Analytics Section and a Research Scientist at Amazon through the Amazon Visiting Academics program. Michel holds a bachelor’s degree in business administration from Leuven University College, a master’s degree in business economics from VLEKHO Business School Brussels, and a Ph.D. in applied economic sciences from Ghent University. He has published articles in peer-reviewed journals such as Journal of Marketing, Journal of the Academy of Marketing Science, and Decision Sciences. His research agenda focuses on improving long-term financial business outcomes through the development of end-to-end machine learning systems for large-scale data. Michel teaches data engineering, deep learning, and deep reinforcement learning. He has been awarded multiple excellence in teaching and commitment awards and has a successful track record raising research funds from industry. Connect with Michel at https://www.linkedin.com/in/michelballings
MSBA Alumni and BAS Premier Corporate Partners can contact Julie Ferrara (email@example.com) for a price reduction.