Learning to See Through Obstructions
CVPR 2020

Paper:
https://arxiv.org/abs/2004.01180

Project Page:
https://www.cmlab.csie.ntu.edu.tw/~yulunliu/ObstructionRemoval

Github:
https://github.com/alex04072000/ObstructionRemoval

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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

🔹A foolproof way to shrink deep learning models

by Kim Martineau

🔻Researchers unveil a pruning algorithm to make artificial intelligence applications run faster.

As more artificial intelligence applications move to smartphones, deep learning models are getting smaller to allow apps to run faster and save battery power. Now, MIT researchers have a new and better way to compress models. It’s so simple that they unveiled it in a tweet last month: Train the model, prune its weakest connections, retrain the model at its fast, early training rate, and repeat, until the model is as tiny as you want.

🔻Do not miss out this article from MIT News
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📌Via: @cedeeplearning

link: http://news.mit.edu/2020/foolproof-way-shrink-deep-learning-models-0430

#deeplearning #machinelearning
#datascience #math
#AI #neuralnetworks

⚪️Introduction to Deep Learning
by Andrew Ng

🔹Source: Coursera

Neural Networks and Deep Learning (Course 1 of the Deep Learning Specialization)

🔖 Lecture 2 Supervised Learning with Neural Network

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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

#DeepLearning #NeuralNeworks
#machinelearning #AI #coursera
#free #python #supervised_learning

🔻DEEP LEARNING TO ANALYSE HUMAN ACTIVITIES RECORDED ON VIDEOS

by Kamalika Some

Analyzing live videos by leveraging deep learning is the trendiest technology aided by computer vision and multimedia analysis. Analysing live videos is a very challenging task and its application is still at nascent stages. Thanks to the recent developments in deep learning techniques, researchers in both computer vision and multimedia communities have been able to gather momentum to drive business processes and revenues.
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📌Via: @cedeeplearning

https://www.analyticsinsight.net/deep-learning-to-analyse-human-activities-recorded-on-videos/

#deeplearning #imagedetection
#neuralnetworks #computervision
#machinelearning #trend #AI

🔹Computer scientists propose method to make computer vision less biased

by Vivek Kumar

Computer scientists from Princeton and Stanford University are working to address problems of bias in Artificial Intelligence. For that, they have built methods to gain fairer data sets containing images of people. The researchers work closely with ImageNet, a database of over 14 million images that has assisted in advancing computer vision over the past decade.

ImageNet, an image database, comprises images of objects, landscapes and people. It serves as a source of training data for researchers who create machine learning algorithms that classify images. Its unprecedented scale required automated image collection and crowd-sourced image annotation.
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

link: https://www.analyticsinsight.net/computer-scientists-propose-methods-make-computer-vision-less-biased/

#computervision #deeplearning
#machinelearning #datascience
#neuralnetworks

🔹Top 10 Data Visualization Tools for Every Data Scientist

At present, the data scientist is one of the most sought after professions. That’s one of the main reasons why we decided to cover the latest data visualization tools that every data scientist can use to make their work more effective.

1. Tableau
2. D3
3. Qlikview
4. Microsoft Power BI
5. Datawrapper
6. E Charts
7. Plotly
8. Sisense
9. FusionCharts
10. HighCharts
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

link: https://www.kdnuggets.com/2020/05/top-10-data-visualization-tools-every-data-scientist.html

#datascience #visualization #datatools
#machinelearning #tableau #powerbi

📗 Deep Learning: The Free eBook

"Deep Learning" is the quintessential book for understanding deep learning theory, and you can still read it freely online.

🔹The book's table of contents
Introduction

✔️Part I: Applied Math and Machine Learning Basics

Linear Algebra
Probability and Information Theory
Numerical Computation
Machine Learning Basics

✔️Part II: Modern Practical Deep Networks

Deep Feedforward Networks
Regularization for Deep Learning
Optimization for Training Deep Models
Convolutional Networks
Sequence Modeling: Recurrent and Recursive Nets
Practical Methodology
Applications

✔️Part III: Deep Learning Research

Linear Factor Models
Autoencoders
Representation Learning
Structured Probabilistic Models for Deep Learning
Monte Carlo Methods
Confronting the Partition Function
Approximate Inference
Deep Generative Models
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📌Via: @cedeeplearning

https://www.kdnuggets.com/2020/05/deep-learning-free-ebook.html

#deeplearning #AI
#neuralnetworks #ebook #machinelearning

⚪️Basics of Neural Network Programming

✒️ by Andrew Ng

🔹Source: Coursera

Neural Networks and Deep Learning

🔖 Lecture 3 Logistic Regression
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📌Other social media: https://linktr.ee/cedeeplearning

#DeepLearning #NeuralNeworks
#machinelearning #AI #coursera
#free #python #supervised_learning

🔹 LSTM for time series prediction

🖊By Roman Orac

🔻Learn how to develop a LSTM neural network with #PyTorch on trading data to predict future prices by mimicking actual values of the time series data.

In this blog post, I am going to train a Long Short Term Memory Neural Network (LSTM) with PyTorch on Bitcoin trading data and use it to predict the price of unseen trading data. I had quite some difficulties with finding intermediate tutorials with a repeatable example of training an #LSTM for time series prediction, so I’ve put together a #Jupyter notebook to help you to get started.
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📌Via: @cedeeplearning

https://www.kdnuggets.com/2020/04/lstm-time-series-prediction.html

#deeplearning #AI #machinelearning
#neuralnetworks #timeseries

🔹Deep Learning-powering machines with human intelligence

🖊 by Ashish Sukhadeve

🔻Deep learning uses neural networks with many intermediate layers of artificial “neurons” between the input and the output, inspired by the human brain. The technology excels at modeling extremely complicated relationships between these layers to classify and predict things.

🔻Deep learning is making a significant contribution to the business world, and the economy is already beginning to feel the impact. The deep learning market is expected to reach $18.2 billion by 2023 from $3.2 billion in 2018, growing at a CAGR of 41.7%. The confluence of three factors- the rise of big data, the emergence of powerful graphics processing units (GPUs), and increasing adoption of cloud computing is fuelling the rapid growth of deep learning.
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

link: https://www.analyticsinsight.net/deep-learning-powering-machines-with-human-intelligence/

#deeplearning #machinelearning
#neuralnetworks
#business #market

🔹Are facial dataset sufficient to perform sentiment analysis using emotional AI?

🖊by Smriti Srivastava

According to Harvard Business Review, the days when #AI technology will be able to “recognize, process, and simulate” human emotions are not that far away. As the emotional AI has already made itself a unique place in the affective computing market, it has been predicted that its market size could grow to about US$ 41 billion by 2022.

The technology of #sentiment_analysis or emotion analysis caters great insights into rapidly growing customer service issues in an effort to conveniently identify and act on the root cause of issues or even mitigate them before they reach critical mass.
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📌Via: @cedeeplearning

https://www.analyticsinsight.net/facial-datasets-sufficient-perform-sentiment-analysis-using-emotional-ai/

#emotional_ai #deeplearning
#machinelearning #sentiment
#facial_recognition

🔻Speeding Up Deep Learning Inference Using TensorRT

📈 This version starts from a #PyTorch model instead of the #ONNX model, upgrades the sample application to use #TensorRT 7, and replaces the ResNet-50 #classification model with UNet, which is a segmentation model.

📈 NVIDIA TensorRT is an SDK for deep learning inference. TensorRT provides APIs and parsers to import trained models from all major deep learning frameworks. It then generates optimized runtime engines deployable in the datacenter as well as in automotive and embedded environments.

🔹Simple TensorRT example

🔻Convert the pretrained image segmentation PyTorch model into ONNX.
🔻Import the ONNX model into TensorRT.
🔻Apply optimizations and generate an engine.
🔻Perform inference on the GPU.

⭕️ DO NOT MISS OUT THIS ARTICLE
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

link: https://devblogs.nvidia.com/speeding-up-deep-learning-inference-using-tensorrt/

#NVIDIA #deeplearning #neuralnetworks #python
#machinelearning #AI

🔹 A Primer on Neural Network Models
for Natural Language Processing

This tutorial covers input encoding for natural language tasks, feed-forward networks, convolutional networks, recurrent networks and recursive networks, as well as the computation graph abstraction for automatic gradient computation.
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📌Via: @cedeeplearning

#deeplearning #CNN #tutorial
#neuralnetworks #RNN #paper #nlp

🔻 How cognitive technologies are redefining the future of manufacturing?

🖊by Kanti S

As factories and equipment get smarter and armed with new technologies, like IoT, AI, and Cognitive Automation, Industry 4.0 has finally arrived. Industry 4.0 a term coined in Germany to computerize manufacturing, has now launched into a worldwide phenomenon.

🔹The Future is Bright for Cognitive Manufacturing

As the pace of technological advancement keeps increasing, Cognitive Manufacturing is a trend of today redefining commonplace activities that were impossible to comprehend by technology 10 or 15 years ago.
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📌Via: @cedeeplearning

https://www.analyticsinsight.net/how-cognitive-technologies-are-redefining-the-future-of-manufacturing/

#deeplearning
#cognitive #AI
#manufacturing

⚪️Basics of Neural Network Programming

✒️ by prof. Andrew Ng
🔹Source: Coursera

🔖 Lecture 4 Binary Classification

Neural Networks and Deep Learning
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

#DeepLearning #NeuralNeworks
#machinelearning #AI #coursera
#free #python #supervised_learning
#classification #binary

FineGym: A Hierarchical Video Dataset for Fine-grained Action Understanding
—————
@cedeeplearning

[Received highest review score at CVPR 2020] FineGym: A Hierarchical Video Dataset for Fine-grained Action Understanding

Page: https://sdolivia.github.io/FineGym/

Arxiv: https://arxiv.org/abs/2004.06704v1?utm_content=bufferd6581&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer

(the author will be livestreaming at AISC: https://buff.ly/3feLIrk) —> Time: Friday 15-May-2020 04:00

The Chinese University of Hong Kong: http://www.cuhk.edu.hk/english/index.html
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📌Via: @cedeeplearning

🔻 Learning to Smell: Using Deep Learning to Predict the Olfactory Properties of Molecules

🔹Smell is a sense shared by an incredible range of living organisms, and plays a critical role in how they analyze and react to the world. For humans, our sense of smell is tied to our ability to enjoy food and can also trigger vivid memories.

🔹In “Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules”, we leverage graph neural networks (GNNs), a kind of deep neural network designed to operate on graphs as input, to directly predict the odor descriptors for individual molecules, without using any handcrafted rules.
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📌Via: @cedeeplearning

https://ai.googleblog.com/2019/10/learning-to-smell-using-deep-learning.html

#deeplearning #neuralnetworks
#GNN #graph #network #machinelearning

🔻Quantum Supremacy Using a Programmable #Superconducting #Processor

🔹Physicists have been talking about the power of quantum computing for over 30 years, but the questions have always been: will it ever do something useful and is it worth investing in? For such large-scale endeavors it is good engineering practice to formulate decisive short-term goals that demonstrate whether the designs are going in the right direction.

🔹Today we published the results of this quantum supremacy experiment in the Nature article, “Quantum Supremacy Using a Programmable Superconducting Processor”. We developed a new 54-qubit processor, named “Sycamore”, that is comprised of fast, high-fidelity quantum logic gates, in order to perform the benchmark testing.
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📌Via: @cedeeplearning

https://ai.googleblog.com/2019/10/quantum-supremacy-using-programmable.html

#quantomcomputing #deeplearning
#machinelearning #neuralnetworks
#AI #sycamore #hardware

📝 Machine Learning Resume Sample: how to build a strong ML Resume

🔻Tips to make machine learning resume
🔻What are the must-have skills for an AI resume
🔻Common skills that employers look for on an ML Resume
🔻How to master programming languages
🔻Creating your Machine Learning Resume
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📌Via: @cedeeplearning

https://www.mygreatlearning.com/blog/5-must-haves-machine-learning-resume/#tips

#resume #machinelearning
#datascience #skill #AI
#python #programming