Deep Learning: Mastering Neural Networks
Course Overview
Deep learning has transformed how organizations analyze data and make data-driven decisions, making it crucial for professionals to gain AI-related skills to stay competitive. This 8-week course from MIT xPRO offers you a comprehensive introduction to this field, combining theory and hands-on practice. You will gain skills and tools like Python to design and optimize neural networks for classification, regression, and sequential data processing. Also, master techniques like transfer learning, convolutional (CNN) and recurrent neural networks (RNN).
Program Highlights
Python tools and libraries to build training neural networks
Deep Neural Networks: GANs and Transformers
Guided cases studies about Deep Neural Networks
Learning methodology that allows participants to practice their newly acquired skills
Networking with fellow participants to share experiences and build collaborative relations
Certificate in Deep Learning in addition to 4.8 Continuing Education Unit (MIT CEUs)
Who is this virtual course for?
The course is designed for experienced professionals looking for a deeper understanding of neural networks and their applications. This program is best suited for:
Software engineers and developers aiming to build and optimize AI-driven applications.
Data scientists and analysts looking to deepen their expertise in AI and machine learning.
AI & ML professionals seeking to leverage neural network techniques to solve complex problems with innovative solutions.
Technology professionals eager to explore cutting-edge advancements in AI and deep learning.
Past Participant Profiles
Past learners engaged in this deep learning course have come from diverse industries, roles, cultures, and professional experiences, enriching the program with fresh perspectives. This diversity creates discussions that offer valuable insights and innovative solutions to organizational challenges.
Diverse Functional Responsibilities
A Wide Range of Work Experience
Learning Objectives
Explore the core mathematical and conceptual ideas underlying deep neural networks.
Experiment with deep learning models and algorithms using available machine learning toolkits.
Examine application approaches and case studies where deep learning is being used throughout a variety of industries.
Understand advanced neural network architectures for application in software products.
Gain strategic insights into AI and its potential impact on business models.
Course Outline
A basic knowledge of programming languages is a prerequisite for this program. The course will, however, explain the necessary foundational concepts of machine learning and programming, allowing those without previous experience to participate fully.
Key Takeaways
Learn the essentials of building neural networks.
Develop an understanding of how Python works with machine learning models.
Define and train neural networks and metrics.
Use autoencoding to customize neural networks.
Apply transfer learning for advanced applications.
Train deep learning GANs and Transformers.
Learn through case studies how to apply deep learning models to solve organizational challenges.
Meet your instructor
Clarence J. LeBel Professor in Electrical Engineering, and Professor of Electrical Engineering and Computer Science in the EECS Department at MIT
What Past Learners are Saying
Watch Program Overview
FAQs
Didn't find what you were looking for? Schedule a call with one of our Program Advisors or call us at +1 857 3766818.
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