Intro to Deep Learning with PyTorch By Facebook AI
β Free Course
#Deep_Learning #DL #PyTorch
π Link Review
π£ @AI_Python_Arxiv
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
β Free Course
#Deep_Learning #DL #PyTorch
π Link Review
π£ @AI_Python_Arxiv
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
Model evaluation, model selection, and algorithm selection in machine learning
#ML #AI #BigData #DL ##neuralnetworks
π Link Review
π£ @AI_Python_Arxiv
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
#ML #AI #BigData #DL ##neuralnetworks
π Link Review
π£ @AI_Python_Arxiv
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
New NLP News:
#NLP #ML #DL #Training #RNN #RL
π Link Review
π£ @AI_Python_Arxiv
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
ML on code, Understanding RNNs, Deep Latent Variable Models, Writing Code for NLP Research, Quo vadis, NLP?, Democratizing AI, ML Cheatsheets, Spinning Up in Deep RL, Papers with Code, Unsupervised MT, Multilingual BERT
#NLP #ML #DL #Training #RNN #RL
π Link Review
π£ @AI_Python_Arxiv
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
Forwarded from DLeX: AI Python (Farzadπ¦
)
Guide to learn DataScience and MachineLearning with Python:
#ML #DL
#Ω ΩΨ§Ψ¨ΨΉ #ΨΉΩΩ _Ψ―Ψ§Ψ―Ω
---START---
β Step 1
πΈ Download and Install Anaconda
β Step 2
a. Learn the basics of Python (Lists, Tuples, Dictionaries, etc)
b. Understand the basics of data structures and algorithms :
π Link Review
---Beginner Level Completed---
β Step 3
a. Understand the use of regular expressions
πΈ Do more practice problems in PythonHacker Rank
πΈ Codeacademy
β Step 4
Learn the scientific libraries (NumPy, SciPy, Pandas)
πΈ Pandas
β Step 5
Data Visualization (Matplotlib, plotly, seaborne, etcβ¦)
πΈ Matplotlib
πΈ Python Gallery
---Intermediate Level Done---
β Step 6
πΈMachine Learning with Scikit-LearnMachine Learning in 20min
πΈ Skcikit-Learn Tutorial
β Step 7:
Practice your machine learning skillsKaggle Machine Learning Tutorial
---Advanced Level Completed--
β Step 8:
Deep Learning
Deeplearning.ai (Andrew Ng)
πΈ Kaggle Deep Learning Tutorial
βοΈ @AI_Python
β΄οΈ @AI_Python_EN
#ML #DL
#Ω ΩΨ§Ψ¨ΨΉ #ΨΉΩΩ _Ψ―Ψ§Ψ―Ω
---START---
β Step 1
πΈ Download and Install Anaconda
β Step 2
a. Learn the basics of Python (Lists, Tuples, Dictionaries, etc)
b. Understand the basics of data structures and algorithms :
π Link Review
---Beginner Level Completed---
β Step 3
a. Understand the use of regular expressions
πΈ Do more practice problems in PythonHacker Rank
πΈ Codeacademy
β Step 4
Learn the scientific libraries (NumPy, SciPy, Pandas)
πΈ Pandas
β Step 5
Data Visualization (Matplotlib, plotly, seaborne, etcβ¦)
πΈ Matplotlib
πΈ Python Gallery
---Intermediate Level Done---
β Step 6
πΈMachine Learning with Scikit-LearnMachine Learning in 20min
πΈ Skcikit-Learn Tutorial
β Step 7:
Practice your machine learning skillsKaggle Machine Learning Tutorial
---Advanced Level Completed--
β Step 8:
Deep Learning
Deeplearning.ai (Andrew Ng)
πΈ Kaggle Deep Learning Tutorial
βοΈ @AI_Python
β΄οΈ @AI_Python_EN
A Concise Handbook of TensorFlow (https://tf.wiki ) Online book for those who already knows #ML / #DL theories and want to focus on learning #TensorFlow itself
https://tf.wiki/en/preface.html
βοΈ @AI_Python_EN
π£ @AI_Python_arXiv
β΄οΈ @AI_Python
https://tf.wiki/en/preface.html
βοΈ @AI_Python_EN
π£ @AI_Python_arXiv
β΄οΈ @AI_Python
A Brief History of #Artificialintelligence by
πhttp://bit.ly/2oNyCGm #MachineLearning #ML #Robotics #Algorithms #Analytics #NeuralNetworks #NLP #BigData #DataScience #DeepLearning #DL #RT
π£ @AI_Python_Arxiv
β΄οΈ @AI_Python_EN
πhttp://bit.ly/2oNyCGm #MachineLearning #ML #Robotics #Algorithms #Analytics #NeuralNetworks #NLP #BigData #DataScience #DeepLearning #DL #RT
π£ @AI_Python_Arxiv
β΄οΈ @AI_Python_EN
What are the best resources to learn major libraries for #DataScience in #Python. Here is my updated full list.
Will recommend to use Jupyter-Spyder environment to practice all these.
#DataLoading and #DataManipulation
βοΈNumpy - https://bit.ly/1OLtuIF
βοΈScipy - https://bit.ly/2f3pitB
βοΈPandas - https://bit.ly/2qs1lAJ
#DataVisualization
βοΈMatplotlib https://bit.ly/2gxxViI
βοΈSeaborn https://bit.ly/2ABypQC
βοΈPlotly https://bit.ly/2uJwULB
βοΈBokeh https://bit.ly/2uOFbxQ
#ML #DL #ModelEvaluation
βοΈScikit-Learn - https://bit.ly/2uYFNkw
βοΈH20 - https://bit.ly/2M0hJnG
βοΈXgboost - https://bit.ly/2M3Vdut
βοΈTensorflow - https://bit.ly/2vfI5es
βοΈCaffe- https://bit.ly/2a05bgt
βοΈKeras - https://bit.ly/2vfDyZj
βοΈPytorch - https://bit.ly/2uXWY5U
βοΈTheano - https://bit.ly/2v3N805
#analytics #artificialintelligence #machinelearning
#recommend
β΄οΈ @AI_Python_EN
Will recommend to use Jupyter-Spyder environment to practice all these.
#DataLoading and #DataManipulation
βοΈNumpy - https://bit.ly/1OLtuIF
βοΈScipy - https://bit.ly/2f3pitB
βοΈPandas - https://bit.ly/2qs1lAJ
#DataVisualization
βοΈMatplotlib https://bit.ly/2gxxViI
βοΈSeaborn https://bit.ly/2ABypQC
βοΈPlotly https://bit.ly/2uJwULB
βοΈBokeh https://bit.ly/2uOFbxQ
#ML #DL #ModelEvaluation
βοΈScikit-Learn - https://bit.ly/2uYFNkw
βοΈH20 - https://bit.ly/2M0hJnG
βοΈXgboost - https://bit.ly/2M3Vdut
βοΈTensorflow - https://bit.ly/2vfI5es
βοΈCaffe- https://bit.ly/2a05bgt
βοΈKeras - https://bit.ly/2vfDyZj
βοΈPytorch - https://bit.ly/2uXWY5U
βοΈTheano - https://bit.ly/2v3N805
#analytics #artificialintelligence #machinelearning
#recommend
β΄οΈ @AI_Python_EN
Despite attempts at standardisation of DL libraries, there are only a few that integrate classification, segmentation, GAN's and detection. And everything is in #PyTorch :)
https://lnkd.in/eTsqKWZ
#ai #objectdetection #machinelearning #gpu #classification #dl
β΄οΈ @AI_Python_EN
https://lnkd.in/eTsqKWZ
#ai #objectdetection #machinelearning #gpu #classification #dl
β΄οΈ @AI_Python_EN
"Leading your organization to responsible AI"
http://bit.ly/2WoekED
#AI #Artificialintelligence #MI #MachineIntelligence
#ML #MachineLearning #DL #Analytics #BigData #IoT
β΄οΈ @AI_Python_EN
http://bit.ly/2WoekED
#AI #Artificialintelligence #MI #MachineIntelligence
#ML #MachineLearning #DL #Analytics #BigData #IoT
β΄οΈ @AI_Python_EN
Google Tutorial on Machine Learning
This presentation was posted by Jason Mayes, senior creative engineer at Google, and was shared by many data scientists on social networks. Chances are that you might have seen it already. Below are a few of the slides. The presentation provides a list of machine learning algorithms and applications, in very simple words. It also explain the differences between #AI, #ML and #DL (deep learning.) 1/4
β΄οΈ @AI_Python_EN
This presentation was posted by Jason Mayes, senior creative engineer at Google, and was shared by many data scientists on social networks. Chances are that you might have seen it already. Below are a few of the slides. The presentation provides a list of machine learning algorithms and applications, in very simple words. It also explain the differences between #AI, #ML and #DL (deep learning.) 1/4
β΄οΈ @AI_Python_EN
image_2019-05-24_09-41-04.png
245 KB
End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography
Researchers from #GoogleAi and #Stanford published work today in #Nature that shows great potential to use machine learning to help catch more lung cancer cases earlier and increase survival likelihood.
Link: https://lnkd.in/fUMtA-3
#LungCancer #Cancer #biolearning #healthcare #DL
β΄οΈ @AI_Python_EN
Researchers from #GoogleAi and #Stanford published work today in #Nature that shows great potential to use machine learning to help catch more lung cancer cases earlier and increase survival likelihood.
Link: https://lnkd.in/fUMtA-3
#LungCancer #Cancer #biolearning #healthcare #DL
β΄οΈ @AI_Python_EN
Supervised Machine Learning.pdf
2 MB
Why Should you Learn AI and Machine Learning
Why Machine Learning Fascinates Me?
Supervised Machine Learning
Do you know what is Machine Learning All About?
The Science of Machine Learning is about Learning the Models that Generalize Well Machine learning is an area of artificial intelligence and computer science This includes the development of software and algorithms that can make predictions based on data.
Data Science Enthusiasts, I have Created a Community for Us to Learn Togetherπ
Interested people let me know in the Comments and I will send you the invite link to our Communityππ£
#reinforcementlearning #machinlearning #Datascience #ArtificialIntelligence #gans
#SupervisedMachineLearning #ML #dl #iot #bigdata
β΄οΈ @AI_Python_EN
Why Machine Learning Fascinates Me?
Supervised Machine Learning
Do you know what is Machine Learning All About?
The Science of Machine Learning is about Learning the Models that Generalize Well Machine learning is an area of artificial intelligence and computer science This includes the development of software and algorithms that can make predictions based on data.
Data Science Enthusiasts, I have Created a Community for Us to Learn Togetherπ
Interested people let me know in the Comments and I will send you the invite link to our Communityππ£
#reinforcementlearning #machinlearning #Datascience #ArtificialIntelligence #gans
#SupervisedMachineLearning #ML #dl #iot #bigdata
β΄οΈ @AI_Python_EN
Google announced the updated YouTube-8M dataset
Updated set now includes a subset with verified 5-s segment level labels, along with the 3rd Large-Scale Video Understanding Challenge and Workshop at #ICCV19.
Link: https://lnkd.in/f_6Jb7Y
#DL #datasets
β΄οΈ @AI_Python_EN
Updated set now includes a subset with verified 5-s segment level labels, along with the 3rd Large-Scale Video Understanding Challenge and Workshop at #ICCV19.
Link: https://lnkd.in/f_6Jb7Y
#DL #datasets
β΄οΈ @AI_Python_EN
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Classifying Legendary Pokemon Birds π¦π¦π¦
πππ Try it yourself:
https://lnkd.in/eYhKNAh πππ
After only the second fastai "Practical Deep Learning for Coders" class I was able to complete an end-to-end deep learning project! π€π€π€
The main goal is to classify an image as either one of the Legendary Pokemon Birds - Articuno, Moltres or Zapdos - or an alternative class which includes everything else. Needless to say that sometimes my model gets confused about the alternative class since not so many diverse images were feed into it...
Source code:
https://lnkd.in/eRfkBx8
Forked from:
https://lnkd.in/e_k4nqN
#ai #ml #dl #deeplearning #cnn #python
β΄οΈ @AI_Python_EN
πππ Try it yourself:
https://lnkd.in/eYhKNAh πππ
After only the second fastai "Practical Deep Learning for Coders" class I was able to complete an end-to-end deep learning project! π€π€π€
The main goal is to classify an image as either one of the Legendary Pokemon Birds - Articuno, Moltres or Zapdos - or an alternative class which includes everything else. Needless to say that sometimes my model gets confused about the alternative class since not so many diverse images were feed into it...
Source code:
https://lnkd.in/eRfkBx8
Forked from:
https://lnkd.in/e_k4nqN
#ai #ml #dl #deeplearning #cnn #python
β΄οΈ @AI_Python_EN
What makes a good conversation?
How controllable attributes affect human judgments
A great post on conversation scoring.
Link:
http://www.abigailsee.com/2019/08/13/what-makes-a-good-conversation.html
Paper:
https://www.aclweb.org/anthology/N19-1170
#NLP #NLU #DL
βοΈ @ai_python_en
How controllable attributes affect human judgments
A great post on conversation scoring.
Link:
http://www.abigailsee.com/2019/08/13/what-makes-a-good-conversation.html
Paper:
https://www.aclweb.org/anthology/N19-1170
#NLP #NLU #DL
βοΈ @ai_python_en
ββFSGAN: Subject Agnostic Face Swapping and Reenactment
New paper on #DeepFakes creation
YouTube demo:
https://www.youtube.com/watch?v=duo-tHbSdMk
Link:
https://nirkin.com/fsgan/
ArXiV:
https://arxiv.org/pdf/1908.05932.pdf
#FaceSwap #DL #Video #CV
New paper on #DeepFakes creation
YouTube demo:
https://www.youtube.com/watch?v=duo-tHbSdMk
Link:
https://nirkin.com/fsgan/
ArXiV:
https://arxiv.org/pdf/1908.05932.pdf
#FaceSwap #DL #Video #CV
YouTube
New Face Swapping AI Creates Amazing DeepFakes!
π The paper "FSGAN: Subject Agnostic Face Swapping and Reenactment" is available here:
https://nirkin.com/fsgan/
β€οΈ Pick up cool perks on our Patreon page: https://www.patreon.com/TwoMinutePapers
π We would like to thank our generous Patreon supportersβ¦
https://nirkin.com/fsgan/
β€οΈ Pick up cool perks on our Patreon page: https://www.patreon.com/TwoMinutePapers
π We would like to thank our generous Patreon supportersβ¦