Top 23 Tensorflow Open-Source Projects
An Open Source Machine Learning Framework for EveryoneProject mention: How to install GLIBC>=2.29 on Debian 10? | reddit.com/r/debian | 2021-04-15
https://github.com/tensorflow/tensorflow/issues/53 looks relevant.
Deep Learning for humansProject mention: Input Data Shape in Sequence-to-Sequence Model | reddit.com/r/tensorflow | 2021-04-18
I'm currently programming a seq2seq model for text chunking. I will use Keras seq2seq ( https://github.com/keras-team/keras/blob/master/examples/lstm_seq2seq.py ) as the base and then modify it. But I have a question regarding the input data. More specifically, the shape.
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🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.Project mention: HuggingFace Bert Pytorch Implementation Question | reddit.com/r/learnmachinelearning | 2021-04-02
I'm walking through the BertModel code from HuggingFace (https://github.com/huggingface/transformers/blob/master/src/transformers/models/bert/modeling_bert.py) and it’s mostly straightforward except for the parts related to the “decoder” mode. I am confused about why there's a decoder mode for Bert.. From my understanding (may be wrong?) BERT is just an encoder part of the Transformer with MLM/NSP on top. So when would we need to use cross attention here?
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)Project mention: Tensorman and RTX 30-Series GPU's | reddit.com/r/pop_os | 2021-03-19
When I run this simple project, the log output is below. There is a 5-minute pause at 16:48. There is a second pause at the end of the script before the output of the example (final output excluded). This project runs quickly if I exclude "--gpu" and run it on the CPU.
Clone a voice in 5 seconds to generate arbitrary speech in real-timeProject mention: Voice Cloning App | reddit.com/r/Python | 2021-04-07
I've been looking for something like this for a while. Previous best I could find was https://github.com/CorentinJ/Real-Time-Voice-Cloning but it worked quite poorly on a lot of test data I used. Can you advise on what a minimal training set might be (eg. If we used a phonetic pangram would it be sufficient?). Thanks for the effort anyway - I'll test tomorrow and feedback if I have anything to input!
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.Project mention: Beginner in Python for Data Science | reddit.com/r/learnpython | 2020-12-27
data science ipython notebooks
Mask R-CNN for object detection and instance segmentation on Keras and TensorFlowProject mention: Mask RCNN Implementation for Image Segmentation on LabelMe Annotations Data | reddit.com/r/computervision | 2021-04-10
In This article, we will try image segmentation using Mask RCNN. It's the successor of Faster-RCNN. We will use tensorflow-gpu==1.15 for training purposes. Check the Mask_RCNN Github repository. It's implemented in the TensorFlow framework using Resnet101 as the default backbone.
DeepSpeech is an open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry Pi 4 to high power GPU servers.Project mention: Ask HN: How do you run better meetings? | news.ycombinator.com | 2021-04-19
Deezer source separation library including pretrained models.Project mention: How to add in samples? Beginner here! | reddit.com/r/Beatmatch | 2021-04-21
If you are not shy of using the command line: https://github.com/deezer/spleeter/
Visualizer for neural network, deep learning, and machine learning modelsProject mention: [D] Best Way to Draw Neural Network Diagrams | reddit.com/r/MachineLearning | 2021-01-21
Netron might be of interest https://github.com/lutzroeder/netron but it's more for exploration during model development than publication.
Download the AI models here. You can play around on any models but for this example we will only use ssd mobile net for face detection.
Face recognition using TensorflowProject mention: Facial recognition using cluster | reddit.com/r/RASPBERRY_PI_PROJECTS | 2021-01-15
ML training is practically impossible on micro-controllers. Inferencing on the other hand is quite doable, especially if aided by a [TPU coprocessor](https://coral.ai/products/accelerator/). Supposedly with the TPU you can do some quantization-aware training, but I haven't tried this. I am working on a security system that does facial recognition to recognize me and some friends and considers anyone else as an intruder. How I am doing this is by retraining [Facenet](https://github.com/davidsandberg/facenet) with my facial embeddings. Use something like Haar Cascade in OpenCV to get the bounding box for a face and put it through the model to extract face embeddings. You can then save these embeddings as a sort of databases for the faces you want it to recognize during the inferencing phase. After that you can impose something like a SVM classifier to say who in your face database it is. One thing I will note is that the problem is even easier if you are only concerned with one face - in which case it is technically face identification - not recognition. If that is the case, you only need to do a difference calculation between the embeddings you saved during training and the result output from inferencing. If you do end up using the TPU, you can connect to it over USB from inside a container (I only know how to do this in Docker though) too. Hope this was helpful. I am actually looking to use a k8s cluster eventually too as a sort of smart hub for my security system and other devices so I can handle much more traffic (not sure if this is overkill or not on the pi 4s).
Personal Photo Management powered by Go and Google TensorFlowProject mention: Should I wait for DSM 7 and Synology Photos, to transfer my photos from Google to Synology? | reddit.com/r/synology | 2021-04-19
Start downloading your files now - you may as well since you want to de-google everything. You might find that none of the three options are what you want and in my case I've resorted to using old file/folder structure for organising everything as I personally want to keep everything in the original condition as much as possible. An alternative that I've briefly tried is Photoprism docker, it's not perfect but there promises to be improvement. You'll need extra ram for that one I think.
ncnn is a high-performance neural network inference framework optimized for the mobile platformProject mention: Deep Learning options on Radeon RX 6800 | reddit.com/r/Amd | 2021-04-16
There's a Tencent-developed Open Source CNN library that runs on pretty much anything, as it's using Vulkan. It's called ncnn, you might want to take a look.
Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.Project mention: SKLean, TensorFlow, etc vs Spark ML? | reddit.com/r/apachespark | 2021-02-12
I'm the maintainer for an open source project called Horovod that allows you to distribute deep learning training (e.g., TensorFlow) on platforms like Spark.
Machine Learning Toolkit for KubernetesProject mention: Machine Learning Orchestration on Kubernetes using Kubeflow | dev.to | 2021-03-23
If you are looking for bringing agility, improved management with enterprise-grade features such as RBAC, multi-tenancy and isolation, security, auditability, collaboration for the machine learning operations in your organization, Kubeflow is an excellent option. It is stable, mature and curated with best-in-class tools and framework which can be deployed in any Kubernetes distribution. See Kubeflow roadmap here to look into what's coming in the next version.
Deep learning library featuring a higher-level API for TensorFlow.Project mention: Base ball | dev.to | 2021-03-20
Both the teams in a game are given their individual ID values and are made into vectors. Relevant data like the home and away team, home runs, RBI’s, and walk’s are all taken into account and passed through layers. There’s no need to reinvent the wheel here, there's a multitude of libraries that enable a coder to implement machine learning theories efficiently. In this case we will be using a library called TFlearn, documentation available from http://tflearn.org. The program will output the home and away teams as well as their respective score predictions.
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.Project mention: [D] Efficient ways of choosing number of layers/neurons in a neural network | reddit.com/r/statistics | 2021-04-20
optuna, hyperopt, nni, plenty of less-known tools too.
Mapping a variable-length sentence to a fixed-length vector using BERT modelProject mention: Needed 100% to pass a safety quiz, need to wait a week to retake | reddit.com/r/mildlyinfuriating | 2021-01-12
You joke but
TensorFlow Tutorials with YouTube VideosProject mention: Plagiarism is just bad | reddit.com/r/github | 2021-02-20
The majority of this code is taken from the TensorFlow-Tutorials. I highly recommend them to those who want to get started with TensorFlow.
AutoML library for deep learningProject mention: SVM training taking forever on my local machine. Will using AWS Sagemaker be faster for training SVM (Linear) models? | reddit.com/r/learnmachinelearning | 2021-04-09
🤗 The largest hub of ready-to-use NLP datasets for ML models with fast, easy-to-use and efficient data manipulation toolsProject mention: Build an Embeddings index with Hugging Face Datasets | dev.to | 2021-01-28
This article shows how txtai can index and search with Hugging Face's Datasets library. Datasets opens access to a large and growing list of publicly available datasets. Datasets has functionality to select, transform and filter data stored in each dataset.
Swift for TensorFlow (by tensorflow)Project mention: Flashlight: Fast and flexible machine learning in C++ | news.ycombinator.com | 2021-04-16
What are some of the best open-source Tensorflow projects? This list will help you: