t81_558_deep_learning
100DaysOfML
t81_558_deep_learning | 100DaysOfML | |
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10 | 2 | |
5,671 | 127 | |
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3.4 | 0.0 | |
3 days ago | over 1 year ago | |
Jupyter Notebook | Jupyter Notebook | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 only |
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t81_558_deep_learning
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Wednesday Daily Thread: Beginner questions
I am just getting into Machine Learning with Python. I have an M1 MacBook Air and (somehow) managed to install Tensorflow according to this tutorial https://github.com/jeffheaton/t81_558_deep_learning/blob/master/install/tensorflow-install-mac-metal-jul-2021.ipynb , which is apparently the bread and butter of machine learning.
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Install Tensorflow through Miniforge on M1 Mac
I skimmed through other forums and they suggest using Miniforge instead of Anaconda. Specifically, I was following this guide: https://github.com/jeffheaton/t81_558_deep_learning/blob/master/install/tensorflow-install-mac-metal-jul-2021.ipynb
- Does anyone have Keras for image classification up and running on a Mac?
- layers.Conv2D( ) -> Running this function kills the Python Kernel
- Different Outputs on Mac M1 and Windows
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Learning Roadmap for Beginners in ML (I'm following it). What do you guys think about it?
Applications of Deep Neural Networks with Keras (2021), by Jeff Heaton https://sites.wustl.edu/jeffheaton/t81-558/ https://arxiv.org/pdf/2009.05673.pdf
- i figured out how to animate the GAN i've been training!
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D Simple Questions Thread December 20 2020
dnnlib.SubmitConfig clearly does not exist in that version of dnnlib, and looks to have never existed in the NVlabs/stylegan2-ada repository. However, it does exist in the NVlabs/stylegan2 repository. My hunch is that the code was haphazardly ported from StyleGAN2 to the newer StyleGAN2-ADA, and it is simply an oversight after porting. There is an issue in the jeffheaton/t81_558_deep_learning repository (I assume you are you eluzzi5?), so I'll add this info to that issue.
I am using the following code to try to run StyleGAN on Google Colab: https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_07_3_style_gan.ipynb
100DaysOfML
What are some alternatives?
dnn_from_scratch - A high level deep learning library for Convolutional Neural Networks,GANs and more, made from scratch(numpy/cupy implementation).
mt5-M2M-comparison - Comparing M2M and mT5 on a rare language pairs, blog post: https://medium.com/@abdessalemboukil/comparing-facebooks-m2m-to-mt5-in-low-resources-translation-english-yoruba-ef56624d2b75
image-super-resolution - 🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.
mlreef - The collaboration workspace for Machine Learning
DotA2-Icon-GAN - Using GANs to generate DotA2 Ability Icons
MetalTranslate - Customizable machine translation in C++
Hands-On-Meta-Learning-With-Python - Learning to Learn using One-Shot Learning, MAML, Reptile, Meta-SGD and more with Tensorflow
MAGIST-Algorithm - Multi-Agent Generally Intelligent Simultaneous Training Algorithm for Project Zeta
Artifact_Removal_GAN - A U-net GAN for jpeg artifact removal
Astock - Astock
handwritten-digits-recognizer-webapp - This is my first experience with machine learning
elastic_transformers - Making BERT stretchy. Semantic Elasticsearch with Sentence Transformers