Autoencoders Courses and Certifications

Career Certificate

Generative AI Models and Transformer Networks Certification

14 ratings at Coursera

Master Generative AI from autoencoders to transformers. Build VAEs, GANs, and deploy GPT/BERT models with RAG techniques for real-world applications using TensorFlow.

• Coursera
• 4 weeks, 10 hours/week
• Self-Paced
• Paid Course
• 3 courses

Edureka

Getting Started with Generative AI

Master foundational and advanced Generative AI concepts, from model architectures to deployment strategies, with hands-on experience in GANs, transformers, LLMs, and optimization techniques.

• Coursera
• 18 hours 57 minutes
• Self-Paced
• Paid Course

Packt

Advanced Machine Learning and Deep Learning

Master advanced machine learning and deep learning techniques, from neural networks to CNNs and RNNs. Apply knowledge through hands-on labs, covering regression, classification, and specialized topics like autoencoders and transfer learning.

• Coursera
• 7 hours 22 minutes
• Self-Paced
• Paid Course

Image Generation with AI Training Course

Master Stable Diffusion, denoising techniques, and generative learning to create high-quality images from text prompts and build multimodal AI applications with shared embedding spaces.

• Coursera
• 3 hours 6 minutes
• Self-Paced
• Paid Course

Programming Generative AI: Unit 2

Explore advanced generative AI techniques for images and text using CNNs, autoencoders, diffusion models, and transformers with hands-on PyTorch and Hugging Face implementation.

• Coursera
• 8 hours 26 minutes
• Self-Paced
• Paid Course

Edureka

Generative AI Models and GPU Systems

Master generative AI by exploring VAEs, GANs, and diffusion models, then scale and deploy them using GPU optimization, mixed precision, and distributed training strategies.

• Coursera
• 13 hours 16 minutes
• Self-Paced
• Paid Course

Steve Brunton

Deep Learning of Dynamics and Coordinates with SINDy Autoencoders

Discover effective coordinate systems and models simultaneously using SINDy autoencoders, a novel approach for analyzing high-dimensional data in dynamical systems.

• YouTube
• 25 minutes
• Self-Paced
• Free Video

NPTEL;  IIT Ropar

Deep Learning

In this course, we will learn about the building blocks used in these Deep Learning based solutions. Specifically, we will learn about feedforward neural networks, convolutional neural networks, recurrent neural networks and attention mechanisms.

• YouTube
• 1 day 3 hours
• Self-Paced
• Free Video

Steve Brunton

Physics Informed Machine Learning - Embedding Physics in AI and Discovering New Physics with ML

Discover how to enhance machine learning by embedding physics principles, creating interpretable models that incorporate symmetries, conservation laws, and sparse dynamics.

• YouTube
• 9 hours 56 minutes
• Self-Paced
• Free Video

Yannic Kilcher

Regularizing Trajectory Optimization with Denoising Autoencoders - Paper Explained

Explore regularizing trajectory optimization in reinforcement learning using denoising autoencoders to improve planning and sample efficiency in motor control tasks.

• YouTube
• 30 minutes
• Self-Paced
• Free Video

Data Science Courses

Generative Adversarial Networks and Adversarial Autoencoders - Lecture 16

Dive into advanced concepts of GANs and AAEs, exploring conditional implementations, mode collapse solutions, and practical applications in supervised and unsupervised learning scenarios.

• YouTube
• 1 hour 20 minutes
• Self-Paced
• Free Video

Data Science Courses

Variational Autoencoders and Performers in Deep Learning - Lecture 15

Delve into advanced deep learning concepts including Variational Autoencoders (VAE), variational inference, ELBO computation, and Performer-based Transformer architectures.

• YouTube
• 1 hour 13 minutes
• Self-Paced
• Free Video

TensorFlow 2.0 Practical Advanced

354 ratings at Udemy

Master Tensorflow 2.0, Google’s most powerful Machine Learning Library, with 5 advanced practical projects

• Udemy
• 12 hours 38 minutes
• Self-Paced
• Paid Course

TensorFlow and the Google Cloud ML Engine for Deep Learning

306 ratings at Udemy

CNNs, RNNs and other neural networks for unsupervised and supervised deep learning

• Udemy
• 17 hours 23 minutes
• Self-Paced
• Paid Course

Mastering Deep Learning

Comprehensive exploration of Artificial Neural Networks and Convolutional Neural Networks, covering fundamental principles, applications, and practical implementation in diverse domains.

• Swayam
• 6 weeks
• Self-Paced
• Free Online Course