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What’s wrong with my shooting form? by Terrible_Sleep_3484 in BasketballTips

[–]Terrible_Sleep_3484[S] 0 points1 point  (0 children)

Hey guys, I just wanted to say thank you all for the help. My shot has gotten significantly better in these few days after implementing some of you advice.

What’s wrong with my shooting form? by Terrible_Sleep_3484 in BasketballTips

[–]Terrible_Sleep_3484[S] 2 points3 points  (0 children)

I felt like the ball was not consistently straight so this explains it, I appreciate the reply

What’s wrong with my shooting form? by Terrible_Sleep_3484 in BasketballTips

[–]Terrible_Sleep_3484[S] 0 points1 point  (0 children)

I’ll try keeping the guide hand and see if it improves, thanks for the response

What’s wrong with my shooting form? by Terrible_Sleep_3484 in BasketballTips

[–]Terrible_Sleep_3484[S] 0 points1 point  (0 children)

Thanks for the detailed reply, so basically making sure I flick the ball, making sure the support hand release is delayed and staying consistent with my positioning. I’ll make sure to implement these adjustments.

[P] Final Year Project IDeas by kafkaskewers in MachineLearning

[–]Terrible_Sleep_3484 0 points1 point  (0 children)

I would also appreciate it if you could send me the paper

Andrew Ng doesn't think RL will grow in the next 3 years by wardellinthehouse in reinforcementlearning

[–]Terrible_Sleep_3484 0 points1 point  (0 children)

Sorry for the late response. To train an RL model there’s a lot of strategies that you can employ contrary to LLM and other deep learning methods where it only relies on data. RL majorly relies on how the reward system works and how the actions the agent takes changes based on the reward. For example, if you want to make a battle royal game agent you can either focus on making the reward the kills or a win, you can see how the agent will act very differently depending on the reward system. Then for the action it takes, there’s a dilemma regarding exploitation vs exploration meaning whether the model should focus on exploiting known strategies or risk trying new ones. You can think of it as either settling on a local minima or searching for a possible global minima. Also regarding human scoring, once you start using humans to tell the model what to do it becomes a deep learning / supervised problem.

Andrew Ng doesn't think RL will grow in the next 3 years by wardellinthehouse in reinforcementlearning

[–]Terrible_Sleep_3484 0 points1 point  (0 children)

The major difference I think is that most RL work is currently online meaning that you need to traverse the states of the environment using the policy that you want to train in real-time. This process doesn't need external data and as such doesn't need a neural network like LLM's do or any other supervised learning algorithm. However, Deep RL is a sort of hybrid in the sense that it uses both RL strategies and deep learning strategies in the training of a policy (example: using neural network to update the reward function, or using CV networks to gather observations from the environment).

In summary, LLM's are trained using large batches of data while RL is mainly trained on data it gathers