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Once upon a time, I received an offer to pursue my PhD study at the school of Philosophy, Psychology & Language Science, University of Edinburgh. I wanted to study what are the differences/similarities between human and RL agents in cooperative problem-solving. But, situation did not go well, and I could not start the PhD program at Edinburgh.
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I have published this post at Medium too. You can find Medium version here
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I have started to learn more about the topic of causal inference and causal learning. Therefore, I have decided to put together here every resource I am using during my journey towards understanding this topic. My interest in this topic originated from the philosophical debate about causality, and recently, I have become interested in the current trends and attempt in the machine learning community for reconciling causal graphs with deep learning.
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Deep learning algorithms have achieved a state of the art performance in a lot of different tasks. Convolutional Neural Network (CNN) can be used to achieve considerable performance in image classification, object detection, and semantic segmentation tasks. Recurrent Neural Networks (RNNs) and their descendants like LSTMs and GRUs are the first things that come to mind to tackle problems like neural language translation, speech recognition, and even they are used to generate new texts and music.