🔻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
📌Other social media: https://linktr.ee/cedeeplearning

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

🔻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
📌Other social media: https://linktr.ee/cedeeplearning

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

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
Other social media: https://linktr.ee/cedeeplearning

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

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

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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📌Via: @cedeepelarning
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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

📌Via: @cedeeplearning
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“As far as the laws of mathematics refer to reality, they are not certain, as far as they are certain, they do not refer to reality.”📚

Albert Einstein, 1921
About probabilistic mathematics 📚

@cedeeplearning

“Getting started with Object detection” by Sanchit Tanwar
https://link.medium.com/8QO47s1Fm5

✅ Via: @cedeeplearning📌
✅ Other social media: https://news.1rj.ru/str/Cedeeplearning

Create art using GANs!
Novel Generation of Flower Paintings
GAN-derived model to the generation of novel art

Generative Adversarial Networks (GANS) were introduced by Ian Goodfellow et. al. in a 2014 paper. GANs address the lack of relative success of deep generative models compared to deep discriminative models.

Link

Via: @cedeeplearning
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📌11 Data Science careers shaping our future

1. Business Intelligence (BI) Developer
Average Salary: $89,333

2. Data Architect
Average Salary: $137,630

3. Applications Architect
Average Salary: $134,520

4. Infrastructure Architect
Average Salary: $126,353

5. Enterprise Architect
Average Salary: $161,272

6. Data Scientist
Average Salary: $139,840

7. Data Analyst
Average Salary: $83,878

8. Data Engineer
Average Salary: $151,307

9. Machine Learning Scientist
Average Salary: $139,840

10. Machine Learning Engineer
Average Salary: $114,826

11. Statistician
Average Salary: $93,589

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📌Via: @cedeeplerning
📌Social media: https://linktr.ee/cedeeplearning

link: https://www.northeastern.edu/graduate/blog/data-science-careers-shaping-our-future/

#machinelearning
#datascience
#deeplearning
#career
#salary

🔹Deep Learning 101 — Role of Deep Learning in Artificial Intelligence

While deep learning itself is a concept, neural networks are a model for deep learning. The architecture of a neural network is inspired by the way biological neurons interact with each other.

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📌Via: @cedeeplearning
📌Social media: https://linktr.ee/cedeeplearning

https://medium.com/senseai/deep-learning-101-role-of-deep-learning-in-artificial-intelligence-d949d0ffc4f6

#machinelearning
#deeplearning
#datascience
#neuralnetworks

📌Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data

🔻Do not miss out this amazing article🔻

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📌Via: @cedeeplearning

https://becominghuman.ai/cheat-sheets-for-ai-neural-networks-machine-learning-deep-learning-big-data-678c51b4b463