The rumors are true: you CAN scalably turn streaming #data into calls against a REST API With Spark Structure Streaming’s foreachBatch, any output target addressable through Python or Scala code can be the destination. Check out these best practices http://
bit.ly/3yi7OUf
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Spark Streaming scalably converts data into REST API calls
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Free Deep Learning Book Available for Download Now
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You can download a FREE Deep Learning book from this page: Understanding Deep Learning: https://
udlbook.github.io/udbook It's a draft, and the book is almost finished, so it may not stay free for much longer. -
Free Kaggle Machine Learning Course for Beginners
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If you are looking for a Machine Learning course that is hands-on and 100% free "Intro to Machine Learning" from Kaggle is still a great one to start with. Check this out and get started: http://
kaggle.com/learn/intro-to
-machine-learning
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Understanding Stable Diffusion: Working, Training, and Inference Process
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🚀Blog-tastic Mondays🚀
— Satya Mallick (@LearnOpenCV) 13 mars 2023
Gaining a deeper understanding of the working of Stable Diffusion can be an added advantage to creating better images. In our latest blog post, we dive deep into the working, training, and inference process of Stable Diffusion. We https://t.co/F73I97hMmN… pic.twitter.com/pStI4oGvNmBlog-tastic Mondays
Gaining a deeper understanding of the working of Stable Diffusion can be an added advantage to creating better images. In our latest blog post, we dive deep into the working, training, and inference process of Stable Diffusion. We https://
learnopencv.com/stable-diffusi
on-generative-ai/
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Flan-T5-XXL Performance Compared to Decoder-Only Models
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people also forget that the speed of flan-t5-xxl is also equivalent to a ~5B+ decoder-only model because it's an encoder-decoder model.
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NAS Results Reduce Language Model Training Emissions by 1.3X
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It's actually more ironic. The results of one-time NAS (which took 88X less CO2 than the external paper estimated) are open-sourced and make training language models 1.3X faster & produce 1.3X less emissions (see figure 4 in https://
arxiv.org/abs/2104.10350) https://
github.com/tensorflow/ten
sor2tensor/blob/master/tensor2tensor/models/evolved_transformer.py
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Dropout=1.0 Issue: PyTorch Should Raise ValueError
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The model is not "turned off during training". With dropout=1.0, for dropout layers you'll get all zero at train and, apparently, identity at test. I don't think pytorch should have allowed dropout=1.0. It should be ValueError, not sure I get the reasoning there.
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Dropout Layers Leak Training Phase Information in Transformers
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Dropout layers in a Transformer leak the phase bit (train/eval) – small example. So an LLM may be able to determine if it is being trained and if backward pass follows. Clear intuitively but good to see, and interesting to think through repercussions of
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Robots teaching kids to code through AI and robotics
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State-of-the-Art Deep Learning Models for Unstructured Text Analysis
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Over the years, we've built many state-of-art Deep Learning models. Today, you can use them to get insights from unstructured text. And the best part: We cover your solution end-to-end. Bring your data, and we do the rest!