Deep Learning: Master Neural Networks and AI
Comprehensive course on deep learning, covering neural networks, CNNs, RNNs, and more. Ideal for aspiring ML engineers and data scientists.
Comprehensive course on deep learning, covering neural networks, CNNs, RNNs, and more. Ideal for aspiring ML engineers and data scientists.
Dive into the world of deep learning with this comprehensive course from Illinois Tech. Covering neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, generative models, and more, this course is designed for aspiring machine learning engineers and data scientists. Learn from expert instructors as you explore the foundations of deep learning, practical applications, and advanced techniques. Gain hands-on experience through assignments and projects, preparing you for real-world AI challenges.
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What you'll learn
Understand the fundamentals of neural networks and deep learning architectures
Master Convolutional Neural Networks (CNNs) for image processing tasks
Explore Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) for sequential data
Learn about generative models including GANs and diffusion models
Study self-attention mechanisms and transformer architectures
Discover techniques for neural network compression and transfer learning
Skills you'll gain
This course includes:
4.75 Hours PreRecorded video
32 assignments
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FullTime access
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There are 9 modules in this course
This comprehensive deep learning course covers a wide range of topics in artificial neural networks and their applications. Students will learn about fundamental concepts such as feedforward neural networks and backpropagation, as well as advanced architectures like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The course also explores cutting-edge areas including transformers, generative models (GANs and diffusion models), and transfer learning. Practical deep learning tips, neural network compression techniques, and real-world applications are discussed throughout the modules. By the end of the course, students will have a solid foundation in deep learning theory and practice, preparing them for careers in machine learning engineering and data science.
Neural Networks
Module 1 · 7 Hours to complete
Convolutional Neural Networks (CNNs)
Module 2 · 6 Hours to complete
Deep Learning Tips
Module 3 · 8 Hours to complete
Recurrent Neural Networks (RNNs)
Module 4 · 6 Hours to complete
Generative Models (GANs) and Diffusion Models (DMs)
Module 5 · 5 Hours to complete
Self-attention and Transformers
Module 6 · 5 Hours to complete
Neural Network Compression
Module 7 · 6 Hours to complete
Transfer Learning
Module 8 · 6 Hours to complete
Summative Course Assessment
Module 9 · 3 Hours to complete
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Instructors
Expert in Deep Learning at Illinois Tech
Gady Agam is an instructor at Illinois Tech, where he teaches the course "Deep Learning." This course provides an in-depth exploration of deep learning techniques and applications, focusing on neural networks, backpropagation, and various optimization methods. Students will engage with practical assignments and discussions to enhance their understanding of how deep learning can be applied across different fields.
Assistant Teaching Professor of Computer Science
Shouvik Roy is an Assistant Teaching Professor of Computer Science at Illinois Tech, where he instructs in the field of deep learning as part of the Data Analytics and Deep Learning Specialization. He covers critical AI and machine learning concepts including neural networks, convolutional and recurrent networks, transformers, generative models, model compression, and transfer learning. Shouvik’s teaching aims to equip future engineers and data scientists with hands-on skills and foundational understanding for a career in advanced machine learning.
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