This is the official repository of DeepMTP, a deep learning framework that can be used with multi-target prediction (MTP) problems. MTP can be seen as an umbrella term that cover many subareas of machine learning, which include multi-label classification (MLC), multivariate regression (MTR), multi-task learning (MTL), dyadic prediction (DP), and matrix completion (MC). The implementation is mainly written in Python and uses Pytorch for the implementation of the neural network. The goal is for any user to be able to train a model using only a few lines of code.
AlphaTensor is a novel AI solution to discover mathematical algorithms with Reinforcement Learning. Learn everything you need to know about AlphaTensor in this comprehensive introduction.
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Recommending related products — say, a phone case to go along with a new phone — is a fundamental capability of e-commerce sites, one that saves customers time and leads to more satisfying shopping experiences.
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Diffusion models are a new class of state-of-the-art generative models that generate diverse high-resolution images. They have already attracted a lot of attention after OpenAI, Nvidia and Google managed to train large-scale models. Example architectures that are based on diffusion models are GLIDE, DALLE-2, Imagen, and the full open-source stable diffusion.
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The training/inference processes of deep learning models are involved lots of steps. The faster each experiment iteration is, the more we can optimize the whole model prediction performance given limited time and resources. I collected and organized several PyTorch tricks and tips to maximize the efficiency of memory usage and minimize the run time. To better leverage these tips, we also need to understand how and why they work.
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Deploying deep learning models in production can be challenging, as it is far beyond training models with good performance.
Several distinct components need to be designed and developed in order to deploy a production level deep learning system (seen below):
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