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Machine learning books and papers
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Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn
Ko'proq ko'rsatish📈 Telegram kanali Machine learning books and papers analitikasi
Machine learning books and papers (@machine_learn) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 24 502 obunachidan iborat bo'lib, Taʼlim toifasida 8 036-o'rinni va Eron mintaqasida 13 785-o'rinni egallagan.
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“Admin: @Raminmousa
ID: @Machine_learn
link: https://t.me/Machine_learn”
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24 502
Obunachilar
-524 soatlar
-207 kunlar
-12730 kunlar
Postlar arxiv
DENet: a deep architecture for audio surveillance applications
GIthub: https://github.com/MiviaLab/DENet
Paper: https://link.springer.com/article/10.1007/s00521-020-05572-5
@Machine_learn
👉Lecture Notes for Linear Algebra Featuring Python
.
GitHub link : https://github.com/MacroAnalyst/Linear_Algebra_With_Python
@Machine_learn
TensorFlow Lite on GPU
https://www.tensorflow.org/lite/performance/gpu_advanced
@Machine_learn
🔇 Deep Noise Suppression Challenge – ICASSP 2021
Github: https://github.com/microsoft/DNS-Challenge
Paper: https://arxiv.org/abs/2101.01902v1
Training and test datasets: https://github.com/microsoft/DNS-Challenge/tree/master/datasets
Challenge: https://www.microsoft.com/en-us/research/academic-program/deep-noise-suppression-challenge-icassp-2021/
@Machine_learn
🚀 DALL·E is a 12-billion parameter version of GPT-3 trained to generate images from text descriptions
Introduction: https://openai.com/blog/tags/multimodal/
Deepmind Blog: https://openai.com/blog/dall-e/
Github: https://github.com/openai/CLIP
Paper: https://cdn.openai.com/papers/Learning_Transferable_Visual_Models_From_Natural_Language.pdf
Colab: https://colab.research.google.com/github/openai/clip/blob/master/Interacting_with_CLIP.ipynb
@Machine_learn
Not All Memories are Created Equal: Learning to Expire
Github: https://github.com/lucidrains/learning-to-expire-pytorch
Paper: https://openreview.net/forum?id=ZVBtN6B_6i7
@Machine_learn
How to Create Custom Real-time Plots in Deep Learning
https://www.kdnuggets.com/2020/12/create-custom-real-time-plots-deep-learning.html
@Machine_learn
180 Data Science and Machine Learning Projects with Python
https://medium.com/coders-camp/180-data-science-and-machine-learning-projects-with-python-6191bc7b9db9
@Machine_learn
Generating Beautiful Neural Network Visualizations
https://www.kdnuggets.com/2020/12/generating-beautiful-neural-network-visualizations.html
@Machine_learn
💉 MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining
Github: https://github.com/BruceWen120/medal
Paper: https://arxiv.org/abs/2012.13978v1
Dataset: https://www.kaggle.com/xhlulu/medal-emnlp
Pre-trained: https://huggingface.co/xhlu/electra-medal
@Machine_learn
Feature Selection with Stochastic Optimization Algorithms
https://machinelearningmastery.com/feature-selection-with-optimization/
@Machine_learning
Adversarial Examples in Deep Learning.
https://blog.djsarkar.ai/adversarial-learning-attacks-1/
Github: https://github.com/dipanjanS/adversarial-learning-robustness
@Machine_learn
Rotated Binary Neural Network
Github (Pytorch implementation): https://github.com/lmbxmu/RBNN
Paper: https://arxiv.org/abs/2009.13055
@Machine_learn
Visualisation of the attention patterns of Vision Transformer
link: https://epfml.github.io/attention-cnn/
@Machine_learn
🌐 Optimization Algorithms in Neural Networks
https://datascience-enthusiast.com/DL/Optimization_methods.html
Most used optimizers: https://www.theaidream.com/post/optimization-algorithms-in-neural-networks
@Machine_learn
🤩 7 Awesome Open Source Machine Learning Project Repos👍
1. DeOldify - https://github.com/jantic/DeOldify
2. Real-Time Voice Cloning - https://github.com/CorentinJ/Real-Time-Voice-Cloning
3. Face Recognition - https://github.com/ageitgey/face_recognition
4. NeuralTalk2 - https://github.com/karpathy/neuraltalk2
5. U-GAT-IT - https://github.com/taki0112/UGATIT
6. Srez - https://github.com/david-gpu/srez
7. TecoGAN - https://github.com/thunil/TecoGAN
@Machine_learn
MIT launched a New free Course on Machine learning : Click here
@Machine_learn
📈 کارگاه Kernel Methods for Pattern Analysis
📅 تاریخ برگزاری: پنج شنبه ۲۷ آذر از ساعت ۱۳ الی ۱۶.۳۰ و جمعه ۲۸ آذر ۱۰ الی ۱۶.۳۰
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