Modern Deep Learning Foundations
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Modern Deep Learning Foundations
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What you'll learn
Understand core deep learning principles, including neural networks and backpropagation.
Gain hands-on experience with advanced architectures like CNNs, RNNs, and Transformers.
Learn techniques for improving model performance and deploy deep learning models using TensorFlow and PyTorch.
Skills you'll gain
Tools you'll learn
Details to know
April 2026
6 assignments
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There are 5 modules in this course
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A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Unlock the world of deep learning by understanding the key principles behind machine learning and neural networks. You’ll dive into the fundamentals, such as loss functions, optimization techniques, and the powerful role of backpropagation in model training. Throughout this course, you'll explore essential concepts, core architectures, and advanced techniques in deep learning, equipping you with the tools to implement cutting-edge solutions across various domains. The course follows a structured path, starting with an introduction to deep learning principles and progressing into core architectures, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). You’ll then explore advanced training techniques like data augmentation, advanced optimization, and understanding model decision-making. Finally, you’ll explore industrial tools and deployment, learning practical skills with frameworks like TensorFlow and PyTorch, as well as model deployment strategies. This course is ideal for individuals looking to deepen their understanding of deep learning, whether you're a beginner or have some experience in machine learning. The course assumes no prior experience with deep learning, but some familiarity with basic programming and machine learning principles would be beneficial. By the end of the course, you will be able to implement deep learning models using state-of-the-art architectures, optimize and evaluate their performance, and deploy them effectively in real-world scenarios.
In this module, we will lay the groundwork for understanding deep learning. You will explore the fundamental concepts of machine learning and deep learning, including neural networks, training processes, and key techniques like backpropagation and regularization. By the end of this section, you'll be equipped to assess and optimize deep learning models effectively.
What's included
6 videos1 reading
6 videos•Total 36 minutes
- Machine Learning vs. Deep Learning•5 minutes
- What Is a Neural Network•7 minutes
- Loss Function, Backpropagation, Optimization•8 minutes
- How Does Training Actually Work?•5 minutes
- Performance Evaluation Metrics•8 minutes
- Overfitting and Regularization•4 minutes
1 reading•Total 10 minutes
- Full Course Resources•10 minutes
In this module, we will dive into the core deep learning architectures that power cutting-edge models. You’ll learn how Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) work, as well as explore the benefits of autoencoders and the Transformer model. Understanding these architectures is essential for mastering applications like computer vision and natural language processing.
What's included
5 videos1 assignment
5 videos•Total 28 minutes
- Why Do We Need Convolution?•6 minutes
- How Does a CNN Work?•4 minutes
- Sequences and Time: RNN, GRU, and LSTM•6 minutes
- Autoencoders for Dimensionality Reduction•8 minutes
- Self-Attention and the Transformer Principle•4 minutes
1 assignment•Total 15 minutes
- Core Architectures - Assessment•15 minutes
In this module, we will explore advanced techniques that refine deep learning models. You will gain insights into optimization, normalization, and data augmentation strategies, while also learning about model explainability methods to ensure transparency and trust in AI systems. These techniques are key for improving performance and making models more reliable.
What's included
4 videos1 assignment
4 videos•Total 22 minutes
- Normalization and Initialization•6 minutes
- Data Augmentation•5 minutes
- Advanced Optimization•5 minutes
- Explainability – Understanding Model Decisions•6 minutes
1 assignment•Total 15 minutes
- Advanced Techniques for Training and Model Understanding - Assessment•15 minutes
In this module, we will cover essential tools and practices for deploying deep learning models in real-world applications. You’ll gain hands-on experience with platforms like Google Colab, learn about the strengths of TensorFlow vs. PyTorch, and discover strategies for efficient model deployment and version control. These skills are critical for taking deep learning projects from research to production.
What's included
6 videos1 assignment
6 videos•Total 27 minutes
- TensorFlow vs. PyTorch•4 minutes
- Working Effectively with Google Colab•4 minutes
- Mixed Precision Training•5 minutes
- Transfer Learning and Fine-Tuning•6 minutes
- Saving, Loading, and Versioning Models•5 minutes
- Basic Industrial Deployment•5 minutes
1 assignment•Total 15 minutes
- Industrial Tools and Deployment - Assessment•15 minutes
In this module, we will guide you on how to advance into specialized areas of deep learning and offer a roadmap to become an industrial deep learning engineer. Whether you are interested in computer vision, natural language processing, or reinforcement learning, this section provides a pathway to deepening your expertise and building a successful career in the field.
What's included
2 videos3 assignments
2 videos•Total 10 minutes
- Advancing into Specialized Domains•5 minutes
- Roadmap for the Industrial DL Engineer•5 minutes
3 assignments•Total 90 minutes
- Full Course Practice Assessment•15 minutes
- Next Steps and Specialization - Assessment•15 minutes
- Full Course Assessment•60 minutes
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Frequently asked questions
Modern Deep Learning Foundations is a comprehensive introduction to the principles, architectures, and techniques used in deep learning. The course covers essential topics such as neural networks, loss functions, CNNs, and transformers, and explores important techniques like optimization and regularization. It is relevant because deep learning has revolutionized fields like computer vision, natural language processing, and more, making it a crucial area of knowledge for anyone interested in artificial intelligence.
This course covers the fundamentals of deep learning, offering insights into how neural networks work, the significance of various architectures such as CNNs and LSTMs, and the techniques used to train and evaluate deep learning models effectively. It also introduces advanced topics like self-attention mechanisms, optimization strategies, and deployment in real-world scenarios.
After completing this course, you will have a solid understanding of the foundational concepts of deep learning. You will be able to implement basic deep learning models, apply core architectures like CNNs and RNNs, and utilize optimization techniques to improve model performance. You will also be able to evaluate model performance using metrics like accuracy and recall, and understand how deep learning is applied in real-world settings.
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