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Hey there and welcome to this video! Today I will talk about the Embedding layer of PyTorch. Im going to explain what it does and show you some common use cases and finally I will code up an example that implements a character level language model that can generate any text whatsoever. As you can see Im on the official documentation of PyTorch and they describe the Embedding layer in the following way: Simple lookup table that stores embeddings of a fixed dictionary and size. What we can also see is that it has two positional arguments. One of them being the number of embeddings and the second one being the embeddings dimension. If I were to explain it in my own words eEmbedding is just a two-dimensional array wrapped in the module container with some additional functionality. Most importantly the rows represent different entities one wants to embed. So what do I mean by an entity? One very common example comes from the field of Natural language proc