Top 9 Python Mxnet Projects
Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.Project mention: [D] GPU buying recommendation | reddit.com/r/MachineLearning | 2021-07-17
If you just want to run tensorflow or pytorch for a Jupyter notebook, setting the environment shouldn't be difficult. I know that AWS has a marketplace of preconfigured images. However, you can go as advanced as setting up a cluster of gpu-equipped nodes to setup Horovod (https://github.com/horovod/horovod) to do distributed machine learning. Yes, there's a learning curve, but you cannot acquire this skillet any other way.
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 300 universities from 55 countries including Stanford, MIT, Harvard, and Cambridge.Project mention: I created a way to learn machine learning through Jupyter | reddit.com/r/learnmachinelearning | 2021-04-30
There are actually some online books and courses built on Jupyter Notebook ([Dive to Deep Learning Book](https://github.com/d2l-ai/d2l-en) for example). However yours is more detail and could really helps beginners.
Scout APM: A developer's best friend. Try free for 14-days. Scout APM uses tracing logic that ties bottlenecks to source code so you know the exact line of code causing performance issues and can get back to building a great product faster.
State-of-the-art 2D and 3D Face Analysis ProjectProject mention: Why do most facial recognition algorithms use a hypersphere manifold? | reddit.com/r/deeplearning | 2021-12-05
Code for https://arxiv.org/abs/1801.07698 found: https://github.com/deepinsight/insightface
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML. (by microsoft)Project mention: [D] Tools for converting TF code to Pytorch | reddit.com/r/MachineLearning | 2021-02-01
AutoGluon: AutoML for Text, Image, and Tabular DataProject mention: What will the data science job market be like in 5 years? | reddit.com/r/datascience | 2021-08-14
Some AutoML is getting pretty good, AutoGluon is very solid for tabular data. That being said you still need to have your data in tabular format and deployment still requires some effort.
Machine Learning Platform for Kubernetes (MLOps tools for experimentation and automation)Project mention: [D] Productionalizing machine learning pipelines for small teams | reddit.com/r/MachineLearning | 2021-08-08
For running experiments, http://polyaxon.com/ is a really good free open-source package that has lots of nice integrations so you can quickly run experiments in k8s but it might be overkill in some cases.
🔮 A refreshing functional take on deep learning, compatible with your favorite librariesProject mention: good examples of functional-like python code that one can study? | reddit.com/r/functionalprogramming | 2021-06-29
thinc - defining neural nets in functional way jax, a new deep learning framework puts emphasis on functions rather than tensors, I've tested it for a couple of applications and it's really cool, you can write stuff like you'd write math expressions in papers using numpy. That speeds up development significantly, and makes code much more readable
Run Linux Software Faster and Safer than Linux with Unikernels.
AI Face Recognition/Person Detection NVR. Machine Learning On The Edge, turn your Camera into AI-powered with Jetson Nano and telegram to protect your privacy.Project mention: Private Home Security and AI | reddit.com/r/privacytoolsIO | 2021-01-30
Shinobi + DeepCamera?
Transfer Learning library for Deep Neural Networks.Project mention: [R] Fast Adaptation with Linearized Neural Networks | reddit.com/r/MachineLearning | 2021-03-31
Abstract: The inductive biases of trained neural networks are difficult to understand and, consequently, to adapt to new settings. We study the inductive biases of linearizations of neural networks, which we show to be surprisingly good summaries of the full network functions. Inspired by this finding, we propose a technique for embedding these inductive biases into Gaussian processes through a kernel designed from the Jacobian of the network. In this setting, domain adaptation takes the form of interpretable posterior inference, with accompanying uncertainty estimation. This inference is analytic and free of local optima issues found in standard techniques such as fine-tuning neural network weights to a new task. We develop significant computational speed-ups based on matrix multiplies, including a novel implementation for scalable Fisher vector products. Our experiments on both image classification and regression demonstrate the promise and convenience of this framework for transfer learning, compared to neural network fine-tuning. Code is available at this https URL.
Python Mxnet related posts
Why do most facial recognition algorithms use a hypersphere manifold?
3 projects | reddit.com/r/deeplearning | 5 Dec 2021
Finding similarity between two faces using Siamese Network/Oneshot learning
1 project | reddit.com/r/learnmachinelearning | 3 Nov 2021
Can Someone please guide me on this project?
1 project | reddit.com/r/computervision | 23 May 2021
just released my Clojure AI book
4 projects | reddit.com/r/Clojure | 23 May 2021
[R] Fast Adaptation with Linearized Neural Networks
1 project | reddit.com/r/MachineLearning | 31 Mar 2021
Private Home Security and AI
1 project | reddit.com/r/privacytoolsIO | 30 Jan 2021
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