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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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
Fee Structure
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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.
Gladwin Development Chair - Assistant Professor of Computer Science
Yan Yan serves as the Gladwin Development Chair - Assistant Professor of Computer Science at the Illinois Institute of Technology. His research focuses on computer vision, machine learning, and medical image analysis, particularly in the context of neuroscience. Recently, he was awarded a $1 million grant from the National Science Foundation to develop innovative imaging and machine learning strategies aimed at reconstructing neural circuits in fruit flies, which involves creating a unified framework for brain image generation and analysis2. With a strong background in computer science, Yan has contributed significantly to the field through various presentations and publications, including work on enhancing neuronal structure reconstruction and addressing challenges in neuron segmentation. His projects leverage advanced imaging techniques to improve the accuracy of neural circuit mapping, reflecting his commitment to integrating cutting-edge technology with biological research
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4.7 course rating
47 ratings
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