📗 Adapted Center and Scale Prediction: More Stable and More Accurate

📕 In order to enjoy the simplicity of anchor-free detectors and the accuracy of two-stage ones simultaneously, they have proposed some adaptations based on a detector, Center and Scale Prediction(CSP). The main contributions of their paper are:

1. Improve the robustness of CSP and make it easier to train.
2. Propose a novel method to predict width, namely compressing width.
3. Achieve the second best performance on CityPersons benchmark, i.e. 9.3% log-average miss rate(MR) on reasonable set, 8.7% MR on partial set and 5.6% MR on bare set, which shows an anchor-free and one-stage detector can still have high accuracy.
4. Explore some capabilities of Switchable Normalization which are not mentioned in its original paper.

Link: http://arxiv.org/abs/2002.09053

Via: @cedeeplearning 📌
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Neural network architectures

Deep neural networks and Deep Learning are powerful and popular algorithms. And a lot of their success lays in the careful design of the neural network architecture.

https://towardsdatascience.com/neural-network-architectures-156e5bad51ba

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📗Progressive Learning and Disentanglement of Hierarchical Representations

📕present a strategy to progressively learn independent hierarchical representations from high- to low-levels of abstractions. The model starts with learning the most abstract representation, and then progressively grow the network architecture to introduce new representations at different levels of abstraction.
arXiv, Apr 3, 2020
Link: http://arxiv.org/abs/2002.10549


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Machine Learning Mind Map

📗 This is an interactive chart. Click on the icons to go to a specific sub-field/section.
Straightforward A-Z explanation of ML algorithms with Python implementation and clearly explained math behind -
Link: thelearningmachine.ai/ml

Via: @cedeeplarning📌
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🔹MIT Deep Learning Basics: Introduction and Overview with TensorFlow

As part of the MIT Deep Learning series of lectures and GitHub tutorials, we are covering the basics of using neural networks to solve problems in computer vision, natural language processing, games, autonomous driving, robotics, and beyond.

📌Via: @cedeeplearning

https://medium.com/tensorflow/mit-deep-learning-basics-introduction-and-overview-with-tensorflow-355bcd26baf0

#deeplearning
#neuralnetworks
#TensorFlow
#machinelearning

🔹Olympics Win Gold Medal For Big Data

Nearly 60 GB of information per second is expected to travel across British Telecom’s networks during the Olympic Games. Unsurprisingly social media has generated reams of content during the games. The estimated 845 million monthly active Facebook users are expected to be responsible for more than 15 terabytes of data each day, while Twitter is expecting over 13,000 tweets per second.

link: https://www.forbes.com/sites/netapp/2012/08/08/olympic-charter-big-data-airplane/#17ce8a38180f

📌Via: @cedeeplearning

📌Other Social Media: https://linktr.ee/cedeeplearning
#bigdata
#machinelearning
#datascience

🔻Some points to visualizing your data

📌Via: @cedeeplearning
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link: https://policyviz.com/2018/08/07/dataviz-cheatsheet/

#visualization
#bigdata
#machinelearning
#datascience

🔻The 8 Data Science Skills

1. Programming Skills
2. Statistics
3. Machine Learning
4. Multivariable Calculus & Linear Algebra
5. Data Wrangling
6. Data Visualization & Communication
7. Software Engineering
8. Data Intuition

📌Via: @cedeeplearning
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link: https://blog.udacity.com/2014/11/data-science-job-skills.html

#datascience
#bigdata
#skill
#machineleraning

📗StyleGAN2 Distillation for Feed-forward Image Manipulation

🎉A new way to distill a particular image manipulation of StyleGAN2 into image-to-image network trained in paired way. The resulting pipeline is an alternative to existing GANs, trained on unpaired data. They provide results of human faces' transformation: gender swap, aging/rejuvenation, style transfer and image morphing. The quality of generation using this method is comparable to StyleGAN2 backpropagation and current state-of-the-art methods in these particular tasks.
By Yandex.MILAB 🎉 2020

📝 arxiv.org/abs/2003.03581

Via: @CEdeeplearning 📌
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🔹Deep Learning vs. Machine Learning Models

One topic we find very interesting and will freely admit consumes much of our free time is machine learning. We devote considerable time to researching meaningful business-level topics related to AI, deep learning and machine learning.

If you’re unsure of the core differences between the two, this brief video by MATLAB gives an excellent quick and digestible overview.

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🔻link: https://youtu.be/-SgkLEuhfbg

#machinelearning
#deeplearning
#matlab

🔹Achieving Digital Economies of Scale Via Machine Learning and Model Sequencing

As you deploy something as complex as machine learning, you’ll often start the initial work of scoping initiative X, exploring data, surfacing various levels of insights or predictions and deploying the solution into the wild.
The types of “work” we’re talking about here could fall in the range of any of the following:

1. A specific set of data exploration protocols
2. An outlier that was discovered and that may apply to subsequent models
3. Specific features engineered for a given reason
4. A given team that properly and adequately ideates
5. Scopes and plans of a given initiative or model
6. Specific meta-data and semantic rules or data points that are cultivated and subsequently documented and disseminated across the proper channels and teams throughout an organization

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📌Via: @cedeeplearning
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Credit: https://www.rocketsource.co/blog/machine-learning-model

🔹The Span of Influence in Machine Learning Models

Each player here is an expert in his own right, he must know what the other influencers in the machine learning model need to succeed. You’ve likely heard us talk about the importance of V-Shaped Teams in the past when discussing the concept of skilling up your team members in areas outside of their immediate expertise. The same concept applies here. Peripheral skills matter a lot because your team cannot successfully build and leverage machine learning models if they’re working in silos.

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Link: https://www.rocketsource.co/blog/machine-learning-models/

#machinelearning
#deeplearning
#datascience

📗 Automating Botnet Detection with Graph Neural Networks

🟢 Botnets are a major source for many network attacks, such as DDoS attacks and spam

(Submitted on 13 Mar 2020)
https://arxiv.org/abs/2003.06344?utm_content=buffer8e745&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer

Via: @cedeeplearning
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📚Confused by numerous resources and road-maps to start machine learning?
then this curated list is just for you! 🎉
you will find a mostly complete road-map of topics and skills you need to learn
In this tutorial, getting started in ML is broken into 5 main steps and each of which has their sub-steps
Link

Via: @cedeeplearning 📌
📗🖇Other social media handles: Instagram and facebook

🔹Researchers composed new protein based on sonification using Deep Learning

Recently, an innovation came into being when researchers in the United States and Taiwan explored how to create new proteins by using machine learning to translate #protein structures into musical scores, presenting an unusual way to translate physics concepts across disparate domains, noted APL #Bioengineering.

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https://www.analyticsinsight.net/researchers-composed-new-protein-based-sonification-using-deep-learning

#deeplearning
#machinelearning
#datascience

🔹Deep Learning technologies impacting computer vision advances

A significant focus of study in the field of computer vision is on systems to recognize and remove highlights from digital pictures. Extracted features context for inference about an image, and often the more extravagant the highlights, the better the derivation.

Until not long ago, facial recognition was an awkward and costly innovation constrained to police research labs. However, as of late, because of advances in #computer_vision #algorithms, #facial_recognition has discovered its way into different computing gadgets.
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link: https://www.analyticsinsight.net/deep-learning-technologies-impacting-computer-vision-advances/

#deeplearning
#neuralnetworks
#machinelearning

🔻Unlocking potentials of NLP to fight against COVID-19 crisis

DAMO’s existing model has already been deployed widely in Alibaba’s ecosystem, powering its customer-service AI chatbot and the search engine on Alibaba’s retail platforms, as well as anonymous healthcare data analysis. The model was used in the text analysis of medical records and epidemiological investigation by CDCs in different cities in China for fighting against #COVID-19.
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📌Via: @cedeeplearning
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https://www.analyticsinsight.net/unlocking-potentials-nlp-fight-covid-19-crisis/

#machinelearning
#deeplearning
#neuralnetworks
#NLP

🔹Explainable Data Science Workflows
🔻Do not miss out this webinar🔻

In this talk you will learn:

1. Best practices for explainable data science

2. How to use Lale for semi-automated data science for portability and replicability

3. How to utilize explainable algorithms and metrics for data science tasks

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📌Via: @cedeeplearning
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link: https://info.datascience.salon/en/explainable-data-science-workflows

#datascience
#machinelearning
#deeplearning
#webinar

📗Foundations of Machine Learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar
MIT Press, Second Edition, 2018.


🔹A detailed treatise on Machine Learning mathematical concepts.

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#machinelearning
#free_books
#datascience
#deeplearning

⚪️ 12 Deep Learning Researchers and Leaders

Our list of deep learning researchers and industry leaders are the people you should follow to stay current with this wildly expanding field in AI. From early practitioners and established academics to entrepreneurs and today’s top corporate influencers, this diverse group of individuals is leading the way into tomorrow’s deep learning landscape.

https://www.kdnuggets.com/2019/09/12-deep-learning-research-leaders.html

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