📕 Data Mining Methods for Recommender Systems

✒️ by Xavier Amatriain
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📌Via: @cedeeplearning

#datamining #recommendersystems
#clustering #classification #regression
#machinelearning #datascience

⭕️ Top 10 machine learning startups of 2020

✒️ by Priya Dialani

🌀 As per #Crunchbase, there are 8,705 startups and organizations today depending on AI and machine learning for their essential applications, products, and services. Practically 83% of AI and machine learning startups that Crunchbase tracks, had just three or fewer funding rounds, the most well-known being seed rounds, angel rounds, and early-stage rounds.

1. Alation
2. Graphcore
3. AI.reverie
4. DataRobot
5. Anodot
6. Viz.ai
7. FogHorn
8. Jus Mundi
9. Rosetta.ai
10. Folio3
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📌Via: @cedeeplearning

link: https://www.analyticsinsight.net/top-10-machine-learning-startups-of-2020/

#machinelearning #AI
#datascience #starutp
#technology #hightech
#deeplearning #neuralnetworks

⭕️ Top 6 Open Source Pre-trained Models for Text Classification you should use

1. XLNet
2. ERNIE
3. Text-to-Text Transfer Transformer (T5)
4. Binary - Partitioning Transformation (BPT)
5. Neural Attentive Bag-of-Entities Model for Text Classification (NABoE)
6. Rethinking Complex Neural Network Architectures for Document Classification
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📌Via: @cedeeplearning


https://www.analyticsvidhya.com/blog/2020/03/6-pretrained-models-text-classification/

#classification #machinelearning
#datascience #model #training
#deeplearning #dataset #neuralnetworks
#NLP #math #AI

🔹🔹 A Holistic Framework for Managing Data Analytics Projects

Agile project management for Data Science development continues to be an effective framework that enables flexibility and productivity in a field that can experience continuous changes in data and evolving stakeholder expectations. Learn more about the leading approaches for developing Data Science models, and apply them to your next project.

🔻The Data Science Delivery Process

Data science initiatives are project-oriented, so they have a defined start and end. The Cross-Industry Standard Process for Data Mining (CRISP-DM) is a high-level, extensible process that is an effective framework for data science projects.

Although the steps are shown in the general order in which they are executed, it is important to note that CRISP-DM, like the Agile software development process, is an iterative process framework. Each step can be revisited as many times as needed to refine problem understanding and results.
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📌Via: @cedeeplearning

https://www.kdnuggets.com/2020/05/framework-managing-data-analytics-projects.html

#Agile #CRISP_DM #Data_Analytics #Data_Management #Data_Mining #datascience #Decision_Management, #Development #Software Engineering

👆🏻👆🏻 A Holistic Framework for Managing Data Analytics Projects

🔻 The six CRISP-DM steps are:

1. Business Understanding
2. Data Understanding
3. Data Preparation
4. Modeling
5. Evaluation
6. Deployment
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

link: https://www.kdnuggets.com/2020/05/framework-managing-data-analytics-projects.html

#data_management #datamining
#datascience #machinelearning
#preprocessing #agile #project

🔹🔹 Autonomous vehicle landscape 2020: The leaders of self-driving cars race

Self-Driving Car is yet to take a leap from sci-fi to real-world application. With rising debates and discussions at scale regarding the rollout of the autonomous vehicle, people are skeptical about its service towards them. However, far-far away from ordinary man’s thoughts, in the land of innovative technologies and amid top-notch leaders of the race of innovation, self-driving cars are no more a far-off star.

⚪️ Moreover, according to Bloomberg, here the top 5 leaders of autonomous vehicles landscape in 2020:

🔹 Waymo
Investment: US$3 billion

🔹 Cruise
Investment: US$9+ billion

🔹 Argo AI
Investment: US$2.6 billion (VW); US$1 billion (Ford)

🔹 Aurora
Investment: US$700+ million

🔹 Aptiv
Investment: Undisclosed
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📌Via: @cdedeeplearning

https://www.analyticsinsight.net/autonomous-vehicle-landscape-2020-leaders-self-driving-cars-race/

#deeplearning #neuralnetworks
#machinelearning
#self_driving_cars
#datascience

⚪️ Visualizing the world beyond the frame

🔹Researchers test how far artificial intelligence models can go in dreaming up varied poses and colors of objects and animals in photos.

🔹To give computer vision models a fuller, more imaginative view of the world, researchers have tried feeding them more varied images. Some have tried shooting objects from odd angles, and in unusual positions, to better convey their real-world complexity. Others have asked the models to generate pictures of their own, using a form of artificial intelligence called GANs, or generative adversarial networks. In both cases, the aim is to fill in the gaps of image datasets to better reflect the three-dimensional world and make face- and object-recognition models less biased.
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

link: http://news.mit.edu/2020/visualizing-the-world-beyond-the-frame-0506

#deeplearning #GANs #math
#machinelearning #visualization
#AI #MIT #datascience

⭕️ A foolproof way to shrink deep learning models

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

🖋By Kim Martineau

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.
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📌Via: @cedeeplearning

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

#deeplearning #AI #model
#MIT #machinelearning
#datascience #neuralnetworks
#algorithm #research

🔋 Machine-learning tool could help develop tougher materials

Engineers develop a rapid screening system to test fracture resistance in billions of potential materials.

🖊 By David L. Chandler

For engineers developing new materials or protective coatings, there are billions of different possibilities to sort through. Lab tests or even detailed computer simulations to determine their exact properties, such as toughness, can take hours, days, or more for each variation. Now, a new artificial intelligence-based approach developed at MIT could reduce that to a matter of milliseconds, making it practical to screen vast arrays of candidate materials.
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📌Via: @cedeeplearning

http://news.mit.edu/2020/machine-learning-develop-materials-0520

#machinelearning #deeplearning
#neuralnetworks #material #AI
#datascience #MIT #engineering

❌ Deep learning is a blessing to police for crime investigations

Deep learning architectures these days are applied to computer vision, speech recognition, machine translation, bioinformatics, drug design, crime inspections and various other fields. Deep learning uses deep neural networks based on which actions are triggered and have produced results comparable to human experts. When compared to traditional machine learning algorithms which are linear, deep learning algorithms are hierarchical. These are based on increasing complexity and abstraction. Now, these are helpful in police investigations in the way these processes available information.

In the police investigations, deep learning helps through the video analysis. Videos gathered from multiple sources are feed into the deep learning systems. Through the software, we can identify and differentiate various targets appearing on the footage.
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📌 Via: @cedeeplearning

https://www.analyticsinsight.net/deep-learning-is-a-blessing-to-police-for-investigations/

#deeplearning #machinelearning
#neuralnetworks #videodetection
#analysis #AI #math #datascience
#artificial_intelligence

⚪️ Metis Webinar: Deep Learning Approaches to Forecasting

🔹Metis Corporate Training is offering Deep Learning Approaches to Forecasting and Planning, a free webinar focusing on the intuition behind various deep learning approaches, and exploring how business leaders, data science managers, and decision makers can tackle highly complex models by asking the right questions, and evaluating the models with familiar tools.
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

link: https://www.kdnuggets.com/2020/06/metis-webinar-deep-learning-approaches-forecasting.html

#deeplearning #forecasting #metis #webinar #machinelearning #neuralnetworks #free #datascience

🔹 How to Think Like a Data Scientist

🖊By Jo Stichbury

🔻So what does it take to become a data scientist? For some pointers on the skills for success, I interviewed Ben Chu, who is a Senior Data Scientist at Refinitiv Labs.

🔻Be curious
🔻Be scientific
🔻Be creative
🔻Learn how to code
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📌Via: @cedeeplearning

https://www.kdnuggets.com/2020/05/think-like-data-scientist-data-analyst.html

#datascience #machinelearning
#tutorial #roadmap
#python #math #statistics #neuralnetworks

🔹 Study by - LinkedIn Learning.
some important skills needed by companies for 2020
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📌Via: @cedeeplearning
📌Other social media:https://linktr.ee/cedeeplearning

#skill #python #machinelearning #computerscience #datascience
#tutorial #softskills #hardskills

⭕️ Blockchain Developer program with no upfront payment

📌 Via: @cedeeplearning

#blockchain #machinelearning
#deeplearning #datascience
#job #salary #skill

📕 GPT-3: Language Models are Few-Shot Learners

⚪️ Github: https://github.com/openai/gpt-3

🔹Paper: https://arxiv.org/abs/2005.14165v1
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📌 Via: @cedeeplearning

#machinelearning #math
#deeplearning #neuralnetworks
#datascience #paper #github

⭕️ Top 12 R packages for ML in 2020

🔹do not miss out this nice article!
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📌Via: @cedeeplearning

https://analyticsindiamag.com/top-12-r-packages-for-machine-learning-in-2020/

#machinelearning #AI
#r #R_language #math
#neuralnetworks #skill
#deeplearning #datascience

⭕️ How to Avoid Data Leakage When Performing Data Preparation

🔹A naive approach to preparing data applies the transform on the entire dataset before evaluating the performance of the model. This results in a problem referred to as data leakage, where knowledge of the hold-out test set leaks into the dataset used to train the model. This can result in an incorrect estimate of model performance when making predictions on new data.
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📌 Via: @cedeeplearnig

https://machinelearningmastery.com/data-preparation-without-data-leakage/

#machinelearning #AI
#neuralnetworks #deeplearning
#datascience #preprocessing
#datamining

🔹The 5 Basic Statistics Concepts Data Scientists Need to Know

Statistics can be a powerful tool when performing the art of Data Science (DS). From a high-level view, statistics is the use of mathematics to perform technical analysis of data. A basic visualisation such as a bar chart might give you some high-level information, but with statistics we get to operate on the data in a much more information-driven and targeted way. The math involved helps us form concrete conclusions about our data rather than just guesstimating.
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📌Via: @cedeeplearning

link: https://towardsdatascience.com/the-5-basic-statistics-concepts-data-scientists-need-to-know-2c96740377ae

#statistics #datascience
#machinelearning
#tutorial #AI #python
#deeplearning

📕 deep learning in computer vision
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📌Via: @cedeeplearning

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

🔹 “Three must know SQL questions to pass your data science interview”
by Jay Feng

Link: https://link.medium.com/dcyr5YtkI7

📌 Via: @cedeeplearning

#sql #datascience #interview #machinelearning
#resume