Eric Zelikman

Eric Zelikman is a deep learning engineer fascinated by how (and whether) algorithms learn meaningful representations. A recent graduate from Stanford’s Symbolic Systems program, Eric studies efficient, robust, and disentangled representations across ML fields. Eric hopes machine learning can teach us about non-machine learning and help us overcome the challenges facing humanity.

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Build Basic Generative Adversarial Networks (GANs) (Coursera) Coursera
DeepLearning.AI

Build Basic Generative Adversarial Networks (GANs) (Coursera)

Data Science

Discover the power of Generative Adversarial Networks (GANs) in this introductory course. Whether you're new to machine learning or looking to deepen your expertise, this course will guide you through understanding GAN basics, their real-world applications, and implementing various architectures to generate compelling data samples. Start your journey into the fascinating world of GANs today!

Jun 8th 2026
4 Weeks
Machine Learning Data Science PyTorch
Details
Apply Generative Adversarial Networks (GANs) (Coursera) Coursera
DeepLearning.AI

Apply Generative Adversarial Networks (GANs) (Coursera)

Data Science

Dive into the world of Generative Adversarial Networks (GANs) with this in-depth online course. Whether you're interested in enhancing data privacy, augmenting datasets, or exploring creative applications like image translation, this course provides a solid foundation and practical experience with GANs. Learn to implement Pix2Pix and CycleGAN models, and understand the nuances between paired and unpaired image-to-image translations.

Jun 8th 2026
3 Weeks
Machine Learning Convolutional Neural Networks PyTorch
Details
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