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Machine learning books and papers
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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 8 028-o'rinni va Eron mintaqasida 13 775-o'rinni egallagan.
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Postlar arxiv
LaSOT
Large-scale Single Object Tracking (LaSOT) aims to provide a dedicated platform for training data-hungry deep trackers as well as assessing long-term tracking performance.
http://vision.cs.stonybrook.edu/~lasot/
Github: https://github.com/HengLan/LaSOT_Evaluation_Toolkit
Dataset: http://vision.cs.stonybrook.edu/~lasot/download.html
Paper: https://arxiv.org/abs/2009.03465
@Machine_learn
Must Download : CheatSheet Collection For Data Science in ZIP
Total Folder - 22
Total Size - 216 MB
- Artificial Intelligence
- Machine learning
- Big Data
- OpenCV CheetSheet
- Dev Ops
- Data Analytics
- Python Cheetsheet
- Mathematics
- Excel
- Probability
- SQL
- Statistics
- Deep learning
- Data Warehouse
- Linux
- Interview Question
- Docker & Kubernetes
- Matlab & R Cheatsheet
- Scala CheetSheet
@Machine_learn
This AI Creates Human Faces From Your Sketches!
https://www.youtube.com/watch?v=5NM_WBI9UBE
Paper: https://arxiv.org/abs/2006.01047
@Machine_learn
🧙♂️ How to Create a Cartoonizer with TensorFlow Lite
https://blog.tensorflow.org/2020/09/how-to-create-cartoonizer-with-tf-lite.html
Code: https://github.com/margaretmz/cartoonizer-with-tflite
E2E TFLite Tutorials: https://github.com/ml-gde/e2e-tflite-tutorials
@Machine_learn
MushroomRL
Reinforcement Learning Python library
Github: https://github.com/MushroomRL/mushroom-rl
Project page: https://github.com/openai/mujoco-py
@Machine_learn
TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
👉👉 Watch Here 👉👉
https://youtu.be/tPYj3fFJGjk
⭐️ About the Author ⭐️
The author of this course is Tim Ruscica, otherwise known as “Tech With Tim” from his educational programming YouTube channel. Tim has a passion for teaching and loves to teach about the world of machine learning and artificial intelligence. Learn more about Tim from the links below:
🔗 YouTube: https://www.youtube.com/channel/UC4JX...
🔗 LinkedIn: https://www.linkedin.com/in/tim-ruscica/
⭐️ Course Contents ⭐️
⌨️ Module 1: Machine Learning Fundamentals (00:03:25)
⌨️ Module 2: Introduction to TensorFlow (00:30:08)
⌨️ Module 3: Core Learning Algorithms (01:00:00)
⌨️ Module 4: Neural Networks with TensorFlow (02:45:39)
⌨️ Module 5: Deep Computer Vision - Convolutional Neural Networks (03:43:10)
⌨️ Module 6: Natural Language Processing with RNNs (04:40:44)
⌨️ Module 7: Reinforcement Learning with Q-Learning (06:08:00)
⌨️ Module 8: Conclusion and Next Steps (06:48:24)
TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
@Machine_learn
The Little W-Net that Could
State-of-the-Art Retinal Vessel Segmentation with Minimalistic Models.
Github: https://github.com/agaldran/lwnet
Paper: https://arxiv.org/abs/2009.01907v1
@Machine_learn
Machine learning – Linear Regression Course (Free)
.
Linear regression is perhaps one of the most popular and widely used algorithms in statistics and machine learning.
.
Link : https://bit.ly/31W6yH1
@Machine_learn
Introducing Opacus: A high-speed library for training PyTorch models with differential privacy
https://ai.facebook.com/blog/introducing-opacus-a-high-speed-library-for-training-pytorch-models-with-differential-privacy/
Github: https://github.com/pytorch/opacus
Differential Privacy Series Part 1 | DP-SGD Algorithm Explained: https://medium.com/pytorch/differential-privacy-series-part-1-dp-sgd-algorithm-explained-12512c3959a3
@Machine_learn
1. Cassie Kozyrkov : https://www.linkedin.com/in/cassie-kozyrkov-9531919/
• Medium : https://medium.com/@kozyrkov
2. Ben Taylor : https://www.linkedin.com/in/bentaylordata/
3. Dat Tran : https://www.linkedin.com/in/dat-tran-a1602320/
4. Ian Goodfellow : https://www.linkedin.com/in/ian-goodfellow-b7187213
5. Jose Marcial Portilla : https://www.linkedin.com/in/jmportilla/
6. Koo Ping Shung : https://www.linkedin.com/in/koopingshung/
7. Lex Fridman : https://www.linkedin.com/in/lexfridman/
8. Kristen Kehrer : https://www.linkedin.com/in/kristen-kehrer-datamovesme/
9. Srivatsan Srinivasan : https://www.linkedin.com/in/srivatsan-srinivasan-b8131b/
10. Andrew Ng : https://www.linkedin.com/in/andrewyng
@Machine_learn
Documentation:
1) https://bigml.com/developers
2) https://predictionio.apache.org/datacollection/eventapi/
3) https://docs.anaconda.com/
4) https://github.com/blue-yonder
5) https://docs.mljar.com/
6) http://nupic.docs.numenta.org/
7) https://docs.recombee.com/
8) https://indico.io/docs/
9) http://api.animetrics.com/documentation
10) http://face.eyedea.cz:8080/api/face/docs
11) https://www.betafaceapi.com/wpa/index.php/documentation
12) https://docs.imagga.com/
13) https://wit.ai/docs
14) https://docs.api.bitext.com/
15) https://api.geneea.com/
16) https://www.diffbot.com/dev/docs/
17) https://yactraq.com/contact-trial/
18) https://monkeylearn.com/api/v3/
19) https://help.hutoma.ai/article/ym34wr87lx-hutoma-chat-api
20) http://php-nlp-tools.com/documentation/
@Machine_learn
@Machine_learn
Axial-DeepLab: Long-Range Modeling in All Layers for Panoptic Segmentation
https://ai.googleblog.com/2020/08/axial-deeplab-long-range-modeling-in.html
A Smarter Way to Learn Python: Learn it faster. Remember it longer
#book #python
@Machine_learn
Free course on Data Visualisation Methods
@Machine_learn
Link : bit.ly/2XY4Suw
Top 20+ highly ranked Coursera Courses for Data Science & Machine Learning beginners and advanced
@Machine_learn
https://nuggetsnetwork.com/blog/Top-Coursera-DataScience-Courses.html
