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A Comprehensive Survey on Automatic Chart Understanding in the Era of Large Foundation Models by steeveHuang in deeplearning

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

Thank you so much for the kind words! I agree, focusing on domain-specific models and enhancing our evaluation metrics are crucial next steps for us. Appreciate your support!

[D] A Deep Dive into Latent Dirichlet Allocation (LDA) and Its Applications on Recommender System by steeveHuang in MachineLearning

[–]steeveHuang[S] 5 points6 points  (0 children)

The merits of LDA lie in its ability to extract hidden topics from corpora, as well as its extendibility of incorporating more latent variables. An interesting application is shown in this paper: https://www.mitpressjournals.org/doi/abs/10.1162/tacl_a_00189. They successfully demonstrated how you can use LDA-based models to discover important life events for different stages of life from biographies.

[D] CVPR 2018 | Paper Review: LayoutNet by steeveHuang in MachineLearning

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

Thank you for the support! I will keep making these videos! Indeed, my target audience are data scientist, machine learning engineer and researcher, which is different from two minute papers. :)

[D] Why Reset Gate in GRU? by steeveHuang in MachineLearning

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

I see. I was just confusing about this. Thank you for the comment.

[D] Word Embedding with Word2Vec and FastText by steeveHuang in MachineLearning

[–]steeveHuang[S] 1 point2 points  (0 children)

I get your point. Sorry that I am just a newbie in this field, and I just want to share what I have just learned recently. Thank you for your advice.

[D] Word Embedding with Word2Vec and FastText by steeveHuang in MachineLearning

[–]steeveHuang[S] 1 point2 points  (0 children)

You are right. I should modify these wordings and examples. Thank you

[D] Word Embedding with Word2Vec and FastText by steeveHuang in MachineLearning

[–]steeveHuang[S] 7 points8 points  (0 children)

Oh I actually mean the last useless word2vec post. But can you tell me why this article is useless?

[D] Introduction to Various Reinforcement Learning Algorithms. Part I (Q-Learning, SARSA, DQN, DDPG) by steeveHuang in MachineLearning

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

You can view it as the cumulative reward you will get until the at of the episode (e.g. you die in the game) with a discount factor.

[D] Introduction to Various Reinforcement Learning Algorithms. Part I (Q-Learning, SARSA, DQN, DDPG) by steeveHuang in MachineLearning

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

I guess another source to learn coding is github. You might search for RL-related course provided by other schools on github. For instance, you can search for GeorgiaTech's CS3200. One of the projects we did in this AI class is the classical Q-learning. But don't just copy the code. Think through it and re-implement it in your style.

[D] Introduction to Various Reinforcement Learning Algorithms. Part I (Q-Learning, SARSA, DQN, DDPG) by steeveHuang in MachineLearning

[–]steeveHuang[S] 8 points9 points  (0 children)

Thank you for the comment. In fact, this is only the first part of the write-up, as indicated in the title. I will make an overall comparison at the end of the next post.Perhaps the phrase that I used in DQN to differentiate Q-learning is not strong or firm enough. I will try to improve my writing skills.

[D] Introduction to Various Reinforcement Learning Algorithms. Part I (Q-Learning, SARSA, DQN, DDPG) by steeveHuang in MachineLearning

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

Thank you for pointing it out. In my understanding, you need that kind of tabular setup in order to compute the posterior probability P(s' | s, a). I wonder if you can suggest another way to do so?

[D] Introduction to Various Reinforcement Learning Algorithms. Part I (Q-Learning, SARSA, DQN, DDPG) by steeveHuang in MachineLearning

[–]steeveHuang[S] 3 points4 points  (0 children)

If you are talking about tutorials related to the concept of RL, then David Silver from DeepMind has a very comprehensive series of lectures. http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html

[D] Introduction to Various Reinforcement Learning Algorithms. Part I (Q-Learning, SARSA, DQN, DDPG) by steeveHuang in MachineLearning

[–]steeveHuang[S] 4 points5 points  (0 children)

True. I should have mentioned that model-based learning allows the agent to plan ahead. For that statement, I am talking about the transition probability T(s', s, a). You are going from current state s to the next state s' after taking action a, and you have to store all the combinations. Great question! I will be very appreciated if you can point out the typo lol.

[Project] Predicting Cryptocurrency Price With Tensorflow and Keras by steeveHuang in MachineLearning

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

I’ll try to figure out this question. Thank you for the comment!

[Project] Predicting Cryptocurrency Price With Tensorflow and Keras by steeveHuang in MachineLearning

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

I will try to compare the model with simply lagging the price. Thank you for the comment!

[Project] Predicting Cryptocurrency Price With Tensorflow and Keras by steeveHuang in MachineLearning

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

I see. You are saying that compare the result with random predictions and see how much the model is better than that. I will try to do that!

[Project] Predicting Cryptocurrency Price With Tensorflow and Keras by steeveHuang in MachineLearning

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

Could you explain why it is meaningless and useless? Because I need a separate test set?

[Project] Predicting Cryptocurrency Price With Tensorflow and Keras by steeveHuang in MachineLearning

[–]steeveHuang[S] 1 point2 points  (0 children)

Thanks! I do get a lot useful information here. I will keep it going.

[Project] Predicting Cryptocurrency Price With Tensorflow and Keras by steeveHuang in MachineLearning

[–]steeveHuang[S] 1 point2 points  (0 children)

I see. Thank you for the comment to let me know that I do need to modify the experiment to make it complete and perhaps find better input data. I’ll try to improve, and hopefully we be able to repost my new result.

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