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An award-winning cannabis podcast for women, by women. Hear joyful stories and useful advice about cannabis for health, well-being, and fun—especially for needs specific to women like stress, sleep, and sex. We cover everything from: What’s the best weed for sex? Can I use CBD for menstrual cramps? What are the effects of the Harlequin strain or Gelato strain? And, why do we prefer to call it “cannabis” instead of “marijuana”? We also hear from you: your first time buying legal weed, and how ...
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Manage episode 471896487 series 2417399
Content provided by Patrick Wheeler and Jason Gauci, Patrick Wheeler, and Jason Gauci. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Patrick Wheeler and Jason Gauci, Patrick Wheeler, and Jason Gauci or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://staging.podcastplayer.com/legal.
Intro topic: Grills
News/Links:
- You can’t call yourself a senior until you’ve worked on a legacy project
- Recraft might be the most powerful AI image platform I’ve ever used — here’s why
- NASA has a list of 10 rules for software development
- AMD Radeon RX 9070 XT performance estimates leaked: 42% to 66% faster than Radeon RX 7900 GRE
Book of the Show
- Patrick:
- The Player of Games (Ian M Banks)
- https://a.co/d/1ZpUhGl (non-affiliate)
- The Player of Games (Ian M Banks)
- Jason:
- Basic Roleplaying Universal Game Engine
Patreon Plug https://www.patreon.com/programmingthrowdown?ty=h
Tool of the Show
- Patrick:
- Pokemon Sword and Shield
- Jason:
- Features and Labels ( https://fal.ai )
Topic: Reinforcement Learning
- Three types of AI
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Online vs Offline RL
- Optimization algorithms
- Value optimization
- SARSA
- Q-Learning
- Policy optimization
- Policy Gradients
- Actor-Critic
- Proximal Policy Optimization
- Value optimization
- Value vs Policy Optimization
- Value optimization is more intuitive (Value loss)
- Policy optimization is less intuitive at first (policy gradients)
- Converting values to policies in deep learning is difficult
- Imitation Learning
- Supervised policy learning
- Often used to bootstrap reinforcement learning
- Policy Evaluation
- Propensity scoring versus model-based
- Challenges to training RL model
- Two optimization loops
- Collecting feedback vs updating the model
- Difficult optimization target
- Policy evaluation
- Two optimization loops
- RLHF & GRPO
187 episodes
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