How to Build a Simple Artificial Neural Network (#ANN)
#ArtificialNeuralNetwork
🌎 Artificial Neural Network
✴️ @AI_Python_EN
#ArtificialNeuralNetwork
🌎 Artificial Neural Network
✴️ @AI_Python_EN
If you're bootstrapping yourself into deep learning research, here’s what I would do:
1. FastAI (3m)
2. Personal projects/reproduce papers/consulting (3 - 12m)
3. Flashcard the Deep Learning Book (4-6m)
4. Flashcard ~100 papers in a niche (2m)
5. Publish your first paper (6m)
✴️ @AI_Python_EN
1. FastAI (3m)
2. Personal projects/reproduce papers/consulting (3 - 12m)
3. Flashcard the Deep Learning Book (4-6m)
4. Flashcard ~100 papers in a niche (2m)
5. Publish your first paper (6m)
✴️ @AI_Python_EN
Now is the time to build an AI startup! The ecosystem has matured, the 4 pillars of AI are now freely accessible
1. Data - Kaggle, Google Dataset Search
2. Algorithms - Arxiv, GitHub
3. Compute - Google Colab, Kaggle Kernels
4. Education - School of AI, KhanAcademy, Fast.AI
1. Data - Kaggle, Google Dataset Search
2. Algorithms - Arxiv, GitHub
3. Compute - Google Colab, Kaggle Kernels
4. Education - School of AI, KhanAcademy, Fast.AI
CS234: Reinforcement Learning | Winter 2019
By Emma Brunskill: https://lnkd.in/eyNjZBR
#DeepLearning #MachineLearning #ReinforcementLearning
✴️ @AI_Python_EN
By Emma Brunskill: https://lnkd.in/eyNjZBR
#DeepLearning #MachineLearning #ReinforcementLearning
✴️ @AI_Python_EN
Amazing project success for #DeepLearning for #Radiologists
This CNN model for breast cancer did screening exam classification, trained and evaluated on over 200,000 exams (over 1,000,000 images).
Accuracy? ~ 90% in predicting whether there is a cancer in the breast, when tested on the screening population.
It was a two-stage training procedure, which allows us to use a very high-capacity patch-level network to learn from pixel-level labels alongside a network learning from macroscopic breast-level labels.
Paper on #ArXiv https://lnkd.in/ggj5Z6W
Code: https://lnkd.in/gScbpUs
Explanation: https://lnkd.in/gfa9gzM
#ai #deeplearning #radiology #model #breast #mammography
✴️ @AI_Python_EN
This CNN model for breast cancer did screening exam classification, trained and evaluated on over 200,000 exams (over 1,000,000 images).
Accuracy? ~ 90% in predicting whether there is a cancer in the breast, when tested on the screening population.
It was a two-stage training procedure, which allows us to use a very high-capacity patch-level network to learn from pixel-level labels alongside a network learning from macroscopic breast-level labels.
Paper on #ArXiv https://lnkd.in/ggj5Z6W
Code: https://lnkd.in/gScbpUs
Explanation: https://lnkd.in/gfa9gzM
#ai #deeplearning #radiology #model #breast #mammography
✴️ @AI_Python_EN
An overview for using #R for validated work:
1.) Base R #Validation for #FDA: https://lnkd.in/ep8TRM8
2.) #RStudio IDE Validation: https://lnkd.in/e34FCXn
3.) Evaluating Package Stability
4.) Evaluating Package Dependencies: https://lnkd.in/eniCXgG
5.) Organizing Packages with an Internal Repository: https://lnkd.in/etSGuk4
#rstats
✴️ @AI_Python_EN
1.) Base R #Validation for #FDA: https://lnkd.in/ep8TRM8
2.) #RStudio IDE Validation: https://lnkd.in/e34FCXn
3.) Evaluating Package Stability
4.) Evaluating Package Dependencies: https://lnkd.in/eniCXgG
5.) Organizing Packages with an Internal Repository: https://lnkd.in/etSGuk4
#rstats
✴️ @AI_Python_EN
We released a new large-scale corpus of English speech derived for TTS; LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech
Dataset: http://www.openslr.org/60/
Paper: http://arxiv.org/abs/1904.02882
✴️ @AI_Python_EN
Dataset: http://www.openslr.org/60/
Paper: http://arxiv.org/abs/1904.02882
✴️ @AI_Python_EN
We released our new interactive annotation approach, which outperforms Polygon-RNN++ and is 10x faster.
Paper:
https://arxiv.org/pdf/1903.06874.pdf
Video:
https://www.youtube.com/watch?v=ycD2BtO-QzU …
Code:
https://github.com/fidler-lab/curve-gcn
✴️ @AI_Python_EN
Paper:
https://arxiv.org/pdf/1903.06874.pdf
Video:
https://www.youtube.com/watch?v=ycD2BtO-QzU …
Code:
https://github.com/fidler-lab/curve-gcn
✴️ @AI_Python_EN
A preprint for our #naacl2019 paper "Combining Sentiment Lexica with a Multi-View Variational #Autoencoder" is now online! We combine lexica with different polarity scales with a novel multi-view VAE.
https://arxiv.org/abs/1904.02839
✴️ @AI_Python_EN
https://arxiv.org/abs/1904.02839
✴️ @AI_Python_EN
PaintBot: A Reinforcement Learning Approach for Natural Media Painting
Jia et al.: https://lnkd.in/ez5Vqav
#ComputerVision #PatternRecognition #ReinforcementLearning #Painting
✴️ @AI_Python_EN
Jia et al.: https://lnkd.in/ez5Vqav
#ComputerVision #PatternRecognition #ReinforcementLearning #Painting
✴️ @AI_Python_EN
Introduction to the math of backprop
By Deb Panigrahi: https://lnkd.in/ddtyj_U
#ArtificialIntelligence #BackPropagation #DeepLearning #NeuralNetworks
✴️ @AI_Python_EN
By Deb Panigrahi: https://lnkd.in/ddtyj_U
#ArtificialIntelligence #BackPropagation #DeepLearning #NeuralNetworks
✴️ @AI_Python_EN
Music Transformer
Huang et al.: https://lnkd.in/dzHEH4E
#DeepLearning #Transformer #MachineLearning #SpeechProcessing #Music
✴️ @AI_Python_EN
Huang et al.: https://lnkd.in/dzHEH4E
#DeepLearning #Transformer #MachineLearning #SpeechProcessing #Music
✴️ @AI_Python_EN
How to run #Pytorch 1.0 and http://Fast.ai 1.0 on an Nvidia Jetson Nano Board ($99), an ARM Cortex A57 processor board with 4GB of RAM https://forums.fast.ai/t/share-your-work-here/27676/1274
✴️ @AI_Python_EN
✴️ @AI_Python_EN
Four troubling trends in Machine Learning scholarship:
1. failure to distinguish between explanation and speculation;
2. failure to identify the sources of empirical gains, e.g., emphasizing unnecessary modifications to neural architectures when gains actually stem from hyper-parameter tuning;
3. mathiness: the use of mathematics that obfuscates or impresses rather than clarifies, e.g., by confusing technical and non-technical concepts; and
4. misuse of language, e.g., by choosing terms of art with colloquial connotations or by overloading established technical terms.
https://arxiv.org/abs/1807.03341
✴️ @AI_Python_EN
1. failure to distinguish between explanation and speculation;
2. failure to identify the sources of empirical gains, e.g., emphasizing unnecessary modifications to neural architectures when gains actually stem from hyper-parameter tuning;
3. mathiness: the use of mathematics that obfuscates or impresses rather than clarifies, e.g., by confusing technical and non-technical concepts; and
4. misuse of language, e.g., by choosing terms of art with colloquial connotations or by overloading established technical terms.
https://arxiv.org/abs/1807.03341
✴️ @AI_Python_EN
Six easy ways to run your Jupyter Notebook in the cloud
By Data School: https://lnkd.in/exbAJ-S
✴️ @AI_Python_EN
By Data School: https://lnkd.in/exbAJ-S
✴️ @AI_Python_EN
Understanding Neural ODE's
Blog by Jonty Sinai: https://lnkd.in/e2SEzmZ
#artificialintelligence #machinelearning #neuralnetworks
✴️ @AI_Python_EN
Blog by Jonty Sinai: https://lnkd.in/e2SEzmZ
#artificialintelligence #machinelearning #neuralnetworks
✴️ @AI_Python_EN