Programming Generative AI: Unit 2

External: Coursera Courses ↗ · Coursera

Open Course on External: Coursera

Free to audit · Opens on External: Coursera

Programming Generative AI: Unit 2

Coursera · Beginner ·🧬 Deep Learning ·4mo ago

Key Takeaways

Introduces generative AI concepts and hands-on experience in cutting-edge deep learning techniques

Original Description

Step confidently into the world of generative AI with our expertly crafted online course, designed to equip you with both foundational knowledge and hands-on experience in cutting-edge deep learning techniques. This course guides you through the essential concepts of how computers interpret and generate images and text, starting with the basics of image representation and progressing through advanced architectures like convolutional neural networks and autoencoders. You’ll explore the power of variational autoencoders and diffusion models, learning how these state-of-the-art tools drive modern image generation and enhancement. With practical exercises using industry-standard libraries such as PyTorch and Hugging Face, you’ll gain direct experience building and deploying generative models for both images and text. The course culminates with an in-depth look at natural language processing pipelines and transformer architectures, empowering you to harness large language models for real-world applications. By the end, you’ll have developed a robust skill set in generative AI, ready to innovate in research, creative industries, or technology-driven businesses. Join us and unlock your potential in the rapidly evolving field of artificial intelligence.
Watch on External: Coursera ↗ (saves to browser)
AI explanation not available for this lesson yet
This lesson is still being prepared for the AI tutor. In the meantime, explore lessons that are ready.
Browse explainer-ready lessons →

Related Reads

📰
Train Neural Networks without Draining your Pocket: Mixed Precision and Pixel Precision in Native…
Train neural networks faster with mixed precision and pixel precision using PyTorch, reducing computational costs
Medium · Deep Learning
📰
Optimizers in Deep Learning: From Gradient Descent to Adam:
Learn how optimizers like Gradient Descent and Adam enable Artificial Neural Networks to learn from mistakes in Deep Learning
Medium · Deep Learning
📰
Deep Learning CNN and Recurrence Analysis for Alpha Gamma EEG Biomarkers in Fragile X Syndrome
Apply deep learning CNN and recurrence analysis to identify alpha gamma EEG biomarkers for Fragile X Syndrome diagnosis and research
ArXiv cs.AI
📰
The Operator Assumed a Grid
Learn how six key changes enabled DeepONet to work with irregular GNSS Slant TEC data, improving its performance and applicability
Medium · Deep Learning
Up next
How Spotify Recommends Songs | Spotify Algorithm Explained | #Shorts #Simplilearn
Simplilearn
Watch →