Takeaways from DeepMind's RoboCat Paper
A summary and analysis of DeepMind's RoboCat paper, a self-improving foundation agent for robotic manipulation using Transformer models.
A summary and analysis of DeepMind's RoboCat paper, a self-improving foundation agent for robotic manipulation using Transformer models.
Explains how regularly reading academic papers improves data science skills, offering practical advice on selection and application.
Explores the human effort behind AI training data, covering challenges of data annotation and techniques like transfer learning to reduce labeling workload.
Building a Mortal Kombat controller using TensorFlow.js, CNNs, and transfer learning for posture classification from a webcam feed.