Pranav Dar in his Medium post has classified the pretrained models into three different categories based on their application and here it is!

Multi-Purpose #NLP Models: ULMFiT, Transformer, Google’s BERT, Transformer-XL, OpenAI’s GPT-2.
Word Embeddings: ELMo, Flair.
Other Pretrained Models: StanfordNLP.

🌎 Link Review

✴️ @AI_Python_EN

Yoshua Bengio, Geoffrey Hinton and Yann LeCun, the fathers of #DeepLearning, receive the 2018 #ACMTuringAward for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing today. http://bit.ly/2HVJtdV

✴️ @AI_Python_EN

Tracking Progress in Natural Language Processing

By Sebastian Ruder: https://lnkd.in/e6tkGHH

#deeplearning #machinelearning #naturallanguageprocessing

✴️ @AI_Python_EN

One of my favorite tricks is adding a constant to each of the independent variables in a regression so as to shift the intercept. Of course just shifting the data will not change R-squared, slopes, F-scores, P-values, etc., so why do it?

Because just about any software package capable of doing regression, even Excel, can give you standard errors and confidence intervals for the Intercept, but it is much harder to get most packages to give you standard errors and confidence intervals around the predicted value of the dependent variable for OTHER combinations of the independent variables. Shifting the intercept is an easy way to get confidence intervals for arbitrary combinations of the independent variables.

This sort of thing becomes especially important at a time when the Statistics community is loudly calling for a move away from P-values. Instead it is recommended that researchers give confidence intervals in clinically meaningful terms.
#data #researchers #statistics #r #excel #regression

✴️ @AI_Python_EN

SciBERT: Pretrained Contextualized Embeddings for Scientific Text

Beltagy et al.: https://lnkd.in/eAT3mSK

#ArtificialIntelligence #DeepLearning #MachineLearning

✴️ @AI_Python_EN

All Data Science ***Cheat Sheets*** in one place.


Github link - https://lnkd.in/fGeGXQs

#datascience #machinelearning #excel #deeplearning #python #R

✴️ @AI_Python_EN

GAN Lab: Play with Generative Adversarial Networks (GANs) in your browser!

https://lnkd.in/dfiFvrc

Research paper: https://lnkd.in/eeYFK4J

#AI #ArtificialIntelligence #GenerativeDesign #GenerativeAdversarialNetworks

✴️ @AI_Python_EN

How about you?
✴️ @AI_Python_EN

The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning)

✴️ @AI_Python_EN

Basic Neural Network Math
link

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Pretrained ULMFiT language models for 10 Indian languages! https://github.com/goru001/inltk

#nlp

✴️ @AI_Python_EN

Self-Supervised Learning via Conditional Motion Propagation #CVPR2019 It learns kinematically-sound representations! State-of-the-art results on PASCAL VOC 2012 segmentation task. Paper: https://arxiv.org/abs/1903.11412 Project Page: http://mmlab.ie.cuhk.edu.hk/projects/CMP/

✴️ @AI_Python_EN

TensorFlow is dead, long live TensorFlow!

#TensorFlow just went full #Keras! (!!!!!) Here's why that's an earthquake for #AI and #DataScience...

🌎 TensorFlow

✴️ @AI_Python_EN

How This Researcher Is Using #DeepLearning To Shut Down Trolls And Fake Reviews. #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #IoT #IIoT #PyTorch #Python #RStats #JavaScript #ReactJS #GoLang #Serverless #DataScientist #Linux

🌎 https://bit.ly/2U2J5BX

✴️ @AI_Python_EN

NODE - Neural Ordinary Differential Equations

This was recently presented as a new approach in NeurIPS.

The idea?
Instead of specifying a discrete sequence of hidden layers, they parameterized the derivative of the hidden state using a neural network. The output of the network is computed using a black- box differential equation solver.

They also propose CNF - or Continuous Normalizing Flow

The continuous normalizing flows, a generative model that can train by maximum likelihood, without partitioning or ordering the data dimensions. For training, we show how to scalably backpropagate through any ODE solver, without access to its internal operations. This allows end-to-end training of ODEs within larger models.

Paper: https://lnkd.in/ddMJQAS
#Github: Examples of implementations coming soon to our repository
#neuralnetwork #deeplearning #machinelearning

✴️ @AI_Python_EN

NEW POST: Six easy ways to run your Jupyter Notebook in the cloud 📔☁️
👉 https://lnkd.in/exK-bit 👈

In-depth comparison of Binder, Kaggle Kernels, Google Colaboratory, Azure Notebooks, CoCalc, Datalore

Comparison table: https://lnkd.in/eXP5Sv5

#Python #DataScience

✴️ @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

✔️Numpy - https://bit.ly/1OLtuIF
✔️Scipy - https://bit.ly/2f3pitB
✔️Pandas - https://bit.ly/2qs1lAJ

#DataVisualization
✔️Matplotlib https://bit.ly/2gxxViI
✔️Seaborn https://bit.ly/2ABypQC
✔️Plotly https://bit.ly/2uJwULB
✔️Bokeh https://bit.ly/2uOFbxQ

#ML #DL #ModelEvaluation
✔️Scikit-Learn - https://bit.ly/2uYFNkw
✔️H20 - https://bit.ly/2M0hJnG
✔️Xgboost - https://bit.ly/2M3Vdut
✔️Tensorflow - https://bit.ly/2vfI5es
✔️Caffe- https://bit.ly/2a05bgt
✔️Keras - https://bit.ly/2vfDyZj
✔️Pytorch - https://bit.ly/2uXWY5U
✔️Theano - https://bit.ly/2v3N805


#analytics #artificialintelligence #machinelearning
#recommend

✴️ @AI_Python_EN

PyTorch primer on Google Colab

By Mark Riedl: https://lnkd.in/efxibtj

#ArtificialIntelligence #Colab #DeepLearning #NeuralNetworks #Pytorch

✴️ @AI_Python_EN

Lex Fridman

"Research is exploration in the space of ideas." Congrats to Yoshua Bengio for winning the Turing Award (the Nobel Prize of computing) with Geoffrey Hinton and Yann LeCun. It was an honor to have a conversation with him a few months back. Full video:

#deeplearning

https://lnkd.in/ep7ZG6u

✴️ @AI_Python_EN

All ***Cheat Sheets*** in one place.

Github link - https://lnkd.in/fGeGXQs

#datascience #machinelearning #excel #deeplearning #python #R

✴️ @AI_Python_EN

Here are 4 awesome articles to learn #ObjectDetection from scratch:

• Understanding and Building an Object Detection Model from Scratch in #Python - https://bit.ly/2ErXMVK

• Part 1: A Step-by-Step Introduction to the Basic Object Detection #Algorithms - https://bit.ly/2V4nqp8

• Part 2: A Practical Implementation of the Faster R-CNN Algorithm for Object Detection - https://bit.ly/2Ugrjdx

• Part 3: A Practical Guide to Object Detection using the Popular YOLO Framework - https://bit.ly/2uq7n9y

✴️ @AI_Python_EN