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blog.keras.io | ||
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www.nicktasios.nl
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| | | | | In the Latent Diffusion Series of blog posts, I'm going through all components needed to train a latent diffusion model to generate random digits from the MNIST dataset. In the second post, we will bu | |
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tiao.io
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| | | | | An in-depth practical guide to variational encoders from a probabilistic perspective. | |
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jaketae.github.io
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| | | | | Recently, a friend recommended me a book, Deep Learning with Python by Francois Chollet. As an eager learner just starting to fiddle with the Keras API, I decided it was a good starting point. I have just finished the first section of Part 2 on Convolutional Neural Networks and image processing. My impression so far is that the book is more focused on code than math. The apparent advantage of this approach is that it shows readers how to build neural networks very transparently. It's also a good introduction to many neural network models, such as CNNs or LSTMs. On the flip side, it might leave some readers wondering why these models work, concretely and mathematically. This point notwithstanding, I've been enjoying the book very much so far, and this post is... | |
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aclanthology.org
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| | | [AI summary] The text provides an overview of various natural language processing (NLP) and machine learning research topics. It covers a wide range of areas including: grammatical error correction, text similarity measures, compositional distributional semantics, neural machine translation, dependency parsing, and political orientation prediction. The text also discusses the development of datasets for evaluating models, the importance of readability in reading comprehension tasks, and the use of advanced techniques such as nested attention layers and error-correcting codes to improve model performance. The key themes include the advancement of NLP models, the creation of evaluation datasets, and the exploration of new methods for text analysis and understa... | ||
