What are GANs (Generative Adversarial Networks)?

What are GANs (Generative Adversarial Networks)?

Generative Adversarial Networks (GANs) are a type of unsupervised machine learning algorithm where two submodels, a generator and a discriminator, compete against each other. The generator creates fake samples, while the discriminator attempts to distinguish between real samples from a domain and fake samples from the generator. The adversarial nature of GANs lies in this competition. The generator iteratively creates samples, updating its model until it can generate samples convincing enough to fool both the discriminator and humans. Both the generator and discriminator receive feedback on their performance, and the loser updates its model accordingly. This process continues until the generator becomes so proficient that the discriminator can no longer identify its fakes. While often used in image generation, GANs have various applications, including video frame prediction, image enhancement, and encryption.

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Episoder(151)

AI Model vs Agentic Harness

AI Model vs Agentic Harness

AI models alone aren’t what makes systems powerful. Martin Keen explains the difference between AI models and agentic harness components like tools, memory, and loops. Learn how generative AI agents w...

28 Sep 20min

Harnesses in AI: A Deep Dive

Harnesses in AI: A Deep Dive

The agent hit a login page, panicked, reported success anyway, and the upvote never happened. Tejas Kumar's diagnosis: not a prompt problem. A harness problem.The demo builds a browser agent on GPT-3....

21 Sep 21min

How Harness Engineering Creates AI Agents

How Harness Engineering Creates AI Agents

Most developers have heard of prompt engineering. Many know about context engineering and RAG. But the real frontier in AI today is harness engineering — the structured environment that transforms an ...

14 Sep 20min

Harness Engineering Fixes AI Digital Amnesia

Harness Engineering Fixes AI Digital Amnesia

Agent harnessing and harness engineering is a growing topic - and yet the term requires more clarification on what it is and why agentic systems evolved the way it did to where we are today.

8 Sep 23min

Llama.cpp vs vLLM

Llama.cpp vs vLLM

Choosing a local LLM engine can make or break performance. Cedric Clyburn breaks down Llama.cpp versus vLLM for real‑world local inference. Learn which tool fits personal hardware, production scale, a...

2 Sep 25min

AI in the SDLC

AI in the SDLC

AI promises speed, but where are the real gains? Cedric Clyburn breaks down why productivity stalls across the software development lifecycle despite faster coding. Learn how redesigning SDLC workflow...

25 Aug 21min

What is OpenClaw?

What is OpenClaw?

We've all been using AI chatbots, but AI agents can now move from knowing to doing. Cedric Clyburn breaks down how AI agents, LLMs, tools, and the agentic loop enable real autonomous workflows. Learn ...

19 Aug 23min

The 7 Skills You Need to Build AI Agents

The 7 Skills You Need to Build AI Agents

As AI agents become more capable, the skills needed for AI jobs are shifting. Bri Kopecki breaks down the 7 skills you need to move from prompt engineering to full agent engineering, including system ...

11 Aug 19min

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