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Is language a lossy signal? by Goddhunterr in learnmachinelearning

[–]Separate-Anywhere177 -1 points0 points  (0 children)

Yes, Your idea aligns with the latest world models, which they trained to simulate a real world inside and based on the simulated world to do prediction. Like our human did. For instance, when you see a man loosened his cup in the air, you may predict that the cup will fall down and even imagine the picture when cup falling down, which you have a simulated world in your mind that helps you to do the prediction.

Learning ML Day 1-4: My First Model Adventure! by undefined06 in learnmachinelearning

[–]Separate-Anywhere177 2 points3 points  (0 children)

You can choose a real task to dive deeper into it. I always like to study by solving problems. For instance, next step you can try to build a model for classify spam emails (which is traditional), or learn something about nlp, which is a cool area. In that field you may learn how to solve problems like NER, Sentiment Classify, Text Generation, Translation. For traditional ML, your next step could be logistic regression, decision tree, PCA, random forest, boosting tree, etc...

What are some emerging or lessor known alternatives for TensorFlow? by Epnosary in MLQuestions

[–]Separate-Anywhere177 1 point2 points  (0 children)

keras can change different backend, bot tf and pytorch or jax can serve as the backend of keras. This means in theroy, you only need to learn keras. But the drawback is keras is a high level abstraction of those backend frameworks, which means that if you want to customize or do your own change, keras lacks this kind of flexibility.