Google Research • Representation Learning for Information Extraction from Templatic Documents such as receipts, bills, insurance quotes. We propose a novel approach using representation learning for tackling the problem of extracting structured information from form-like document images.

Blogpost

https://ai.googleblog.com/2020/06/extracting-structured-data-from.html?m=1

Paper

https://research.google/pubs/pub49122/
We propose an extraction system that uses knowledge of the types of the target fields to generate extraction candidates, and a neural network architecture that learns a dense representation of each candidate based on neighboring words in the document. These learned representations are not only useful in solving the extraction task for unseen document templates from two different domains, but are also interpretable, as we show using loss cases. #machinelearning #deeplearning #datascience #dataengineer #nlp

The Most Important Fundamentals of PyTorch you Should Know

https://blog.exxactcorp.com/the-most-important-fundamentals-of-pytorch-you-should-know/

Code: https://github.com/tirthajyoti/PyTorch_Machine_Learning

Google Research • Representation Learning for Information Extraction from Templatic Documents such as receipts, bills, insurance quotes. We propose a novel approach using representation learning for tackling the problem of extracting structured information from form-like document images.

Blogpost

https://ai.googleblog.com/2020/06/extracting-structured-data-from.html?m=1

Paper

https://research.google/pubs/pub49122/
We propose an extraction system that uses knowledge of the types of the target fields to generate extraction candidates, and a neural network architecture that learns a dense representation of each candidate based on neighboring words in the document. These learned representations are not only useful in solving the extraction task for unseen document templates from two different domains, but are also interpretable, as we show using loss cases. #machinelearning #deeplearning #datascience #dataengineer #nlp

Lip Reading - Cross Audio-Visual Recognition using 3D Convolutional Neural Networks

https://bit.ly/2CYxFpY

MIT researchers claim augmentation technique can train GANs with less data

https://bit.ly/2CC6GBm

What software engineers can bring to machine learning by Caleb Kaiser

https://bit.ly/2B6thWB

A TensorFlow Modeling Pipeline Using TensorFlow Datasets and TensorBoard

https://www.kdnuggets.com/2020/06/tensorflow-modeling-pipeline-tensorflow-datasets-tensorboard.html

Lecture Notes in Deep Learning: Feedforward Networks — Part 3 | #DataScience #MachineLearning #ArtificialIntelligence #AI

https://bit.ly/2Z2GgQY

TensorFlow, Keras and deep learning, without a PhD access_tim

https://codelabs.developers.google.com/codelabs/cloud-tensorflow-mnist/#2

Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules Mittal et al.: #ArtificialIntelligence #DeepLearning #MachineLearning

https://arxiv.org/abs/2006.16981

How to Write a Makefile - Automating Python Setup, Compilation, and Testing

https://stackabuse.com/how-to-write-a-makefile-automating-python-setup-compilation-and-testing/

How to read Deep Learning research papers.

⚫ A systematic approach to reading a collection of papers to gain knowledge within a domain
⚫ How to properly read a research paper
⚫ Useful online resources that
can aid you in searching for papers and key information "50–100 papers will primarily provide you with a very good understanding of the domain."

https://towardsdatascience.com/how-you-should-read-research-papers-according-to-andrew-ng-stanford-deep-learning-lectures-98ecbd3ccfb3

All the videos for the Computer Vision lecture
on "Detection, Segmentation, and Tracking" are now public!

Videos: https://youtube.com/playlist?list=PLog3nOPCjKBneGyffEktlXXMfv1OtKmCs…
Slides: https://dvl.in.tum.de/teaching/cv3dst-ss20/

In future #AI hiring other AI be like: Job Profile: *human baby sitter*
- Experience : trained on 100 years of past data.
- Test Accuracy : 99.9999
- Precision: blah
- recall : blah
- AUC : blah blah
- Inference time: A.C
- Trained on : Latest "alien" TPUs and GPUs
- Bias : blah Note: AI trained on old TPUs will not be considered. And then AI will gossip with each other about bias and discrimination they have to go through compared to others like:
- "Wouldn't I be considered if I am trained on X country's data?"
- "Why was she considered even though she has outliers in the data?"
- "I am trained on old TPUs, I won't be considered? What!" LOL #artificialintelligence #machinelearning

A Hybrid Approach for Fake News Detection in Twitter Based on User Features and Graph Embeddings

• Using node2vec to extract features from a twitter follower graph. In conjunction with user features provided by Twitter.

This hybrid approach considers both the characteristics of the user and his social graph. The results show that the approach consistently and significantly outperforms existent approaches limited to user features.

Paper is.gd/LP9uKD

Stanford CS224w’s lectures Machine Learning with Graphs, Leskovec et al.: https://lnkd.in/d4Cnahj #DeepLearning #Graphs #MachineLearning

Locality sensitive hashing:
https://medium.com/carbon-consulting/explaining-lsh-minhash-simhash-c3cc33040030

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