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

The Illustrated Word2vec: The blog explains the concept of embedding, and the mechanics of generating embeddings with word2vec.

By Jay Alammar

#deeplearning #nlp

https://jalammar.github.io/illustrated-word2vec/

✴️ @AI_Python_EN

Brandon Rohrer is a data scientist at Facebook. He's very knowledgeable in Machine Learning and knows how to explain complex concepts in an easy to understand manner.
Here comes his free course on #deeplearning #neuralnetwork #DeepNeuralNetworks.

How Deep Neural Networks Work

https://end-to-end-machine-learning.teachable.com/p/how-deep-neural-networks-work/


✴️ @AI_Python_EN

Google PhD Fellowship Program

Google PhD Fellowships directly support graduate students as they pursue their PhD, as well as connect them to a Google Research Mentor.

https://ai.google/research/outreach/phd-fellowship/

✴️ @AI_Python_EN

Python - Python NLP Libraries
TextBlob - Providing a consistent API for diving into common natural language processing (NLP) tasks. Stands on the giant shoulders of Natural Language Toolkit (NLTK) and Pattern, and plays nicely with bot.
spaCy - Industrial strength NLP with Python and Cython
textacy - Higher level NLP built on spaCy
gensim - Python library to conduct unsupervised semantic modelling from plain text
scattertext - Python library to produce d3 visualizations of how language differs between corpora
AllenNLP - An NLP research library, built on PyTorch, for developing state-of-the-art deep learning models on a wide variety of linguistic tasks.
PyTorch-NLP - NLP research toolkit designed to support rapid prototyping with better data loaders, word vector loaders, neural network layer representations, common NLP metrics such as BLEU.
Rosetta - Text processing tools and wrappers (e.g. Vowpal Wabbit).
PyNLPl - Python Natural Language Processing Library. General purpose NLP library for Python. Also contains some specific modules for parsing common NLP formats, most notably for FoLiA, but also ARPA language models, Moses phrasetables, GIZA++ alignments.
#naturallanguage #machinelearning #ai #language #nlp #spacy #pytorch

✴️ @AI_Python_EN

CS224N Natural Language Processing with Deep Learning 2019
YouTube playlist:
https://www.youtube.com/playlist?list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z

http://onlinehub.stanford.edu/cs224 #NLProc

✴️ @AI_Python_EN

Yoshua: Research is like a random exploration guided by intuition. It's okay to fail, but more important is to try. At an informal event at MILA Montreal

✴️ @AI_Python_EN

Are you a Data Scientists? Do you use Jupyter? Please help us understand how do you consume content and get connected with other professionals Just answer this 3 minute survey : http://bit.ly/Jupyter-survey-1 #DataScience #MachineLearning

✴️ @AI_Python_EN

Successful deployed an image classifier using #flask and #docker #100DaysOfMLCode #100daysofcode #MachineLearning #DeepLearning

https://github.com/hrishikeshmane87933/keras_webapp

✴️ @AI_Python_EN

Bill Gates: A.I. is like nuclear energy — 'both promising and dangerous' - CNBC Read more here: https://ift.tt/2uuayNC #ArtificialIntelligence #AI #DataScience #MachineLearning #BigData #DeepLearning #NLP #Robots #IoT

✴️ @AI_Python_EN

MIT Introduction to #DeepLearning http://bit.ly/2JOvTf4 #MachineLearning #TensorFlow

✴️ @AI_Python_EN

Checklist for debugging neural networks

http://bit.ly/2HSI0W5 #AI #DeepLearning #MachineLearning #DataScience

✴️ @AI_Python_EN

How to write a good machine learning tutorial.

https://bit.ly/2TFUTF6

#MachineLearning #DeepLearning

✴️ @AI_Python_EN

Machine Learning Cheat Sheet #Learning #MachineLearning

✴️ @AI_Python_EN

ArcFace: Additive Angular Margin Loss for Deep Face Recognition. The author used PyTorch 1.0 which is nice.

"We present arguably the most extensive experimental
evaluation of all the recent state-of-the-art face recognition
methods on over 10 face recognition benchmarks including
a new large-scale image database with trillion level
of pairs and a large-scale video dataset. We show that ArcFace consistently outperforms the state-of-the-art and can be easily implemented with negligible computational overhead. We release all refined training data, training codes, pre-trained models and training logs , which will help reproducet he results in this paper."

https://lnkd.in/e5Q2qP3
https://lnkd.in/ezWbVhH
✴️ @AI_Python_EN