🔻10 Machine Learning Future Trends to Watch in 2020

1. Machine Learning Embedded in Most Applications

2. Trained Data as a Service

3. Continuous Retraining of Models

4. Machine Learning as a Service

5. Maturation of NLP

6. Machine Learning Automation

7. Specialized Hardware for Machine Learning

8. Automate Algorithm Selection and Testing Algorithms

9. Transparency and Trust

10. Machine Learning as an End-to-End Process

📌Via: @cedeeplearning

link: https://addiai.com/machine-learning-future-trends/

#trend
#machinelearning
#deeplearning
#datascience

🔻Top Applications of Data Science in 2019

Data Science has huge applications in various industries like banking, finance, manufacturing, transport, e-commerce, education, etc. Here we will see how data science has transformed the world today. We will see how it has been revolutionizing the way we perceive data.

📌Via: @cedeeplearning

link: https://addiai.com/data-science-applications/

#datascience
#machinearning
#application
#deeplearning

🔻Predictions for Deep Learning in 2017

The first hugely successful consumer application of deep learning will come to market, a dominant #open-source deep-learning tool and library will take the developer community by storm, and more Deep Learning predictions.

Deep learning is all the rage as we move into 2017. Grounded in #multilayer #neural_networks, this technology is the foundation of artificial intelligence, #cognitive computing, and #real-time streaming #analytics in many of the most disruptive new #applications.
For data scientists, #deep_learning will be a top professional focus going forward. Here are my #predictions for the chief #trends in deep learning in the coming year: (🔻click on the link to the rest)

link: https://www.kdnuggets.com/2016/12/ibm-predictions-deep-learning-2017.html

📌Via: @cedeeplearning

🔻50 Must-Read Free Books For Every Data Scientist in 2020

1. The Element of Data Analytic Style1. The Element of Data Analytic Style

2. Foundations of Data Science

3. Mining of Massive Datasets

4. Python Data Science Handbook

5. Hands-on Machine Learning and Big Data

6. Think Stats

7. Think Bayes

8. Introduction to Linear Dynamical Systems

9. Convex Optimization

10. Essentials of Metaheuristics
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link: https://www.kdnuggets.com/2020/03/50-must-read-free-books-every-data-scientist-2020.html

📌Via: @cedeeplearning

#free_resources
#datascience
#machinelearning
#free
#python

🔻Does Deep Learning Come from the Devil?

Deep learning has revolutionized #computer_vision and natural language processing (#NLP). Yet the #mathematics explaining its success remains elusive. At the Yandex conference on machine learning prospects and applications, Vladimir Vapnik offered a critical perspective.

🔹we suggest you to tap the link🔹

link: https://www.kdnuggets.com/2015/10/deep-learning-vapnik-einstein-devil-yandex-conference.html

📌Via: @cedeeplearning

#deeplearning
#machinelearning
#neuralnetworks

🔻Top 10 Deep Learning Projects on #Github

The top 10 #deep_learning projects on Github include a number of #libraries, #frameworks, and education resources. Have a look at the tools others are using, and the resources they are learning from.
1. Caffe
2. Data Science IPython Notebooks
3. ConvNetJS
4. Keras
5. MXNet
6. Qix
7. Deeplearning4j
8. Machine Learning Tutorials
9. DeepLearnToolbox
10. LISA Lab Deep Learning Tutorials

link: https://www.kdnuggets.com/2016/01/top-10-deep-learning-github.html

📌Via: @cedeeplearning

⚪️Bringing deep learning to life

MIT duo uses music, videos, and real-world examples to teach students the foundations of artificial intelligence.

📌Via: @cedeeplearning

https://youtu.be/l82PxsKHxYc

🔹Machine learning picks out hidden vibrations from earthquake data

Technique may help scientists more accurately map vast underground geologic structures.

Over the last century, scientists have developed methods to map the structures within the Earth’s crust, in order to identify resources such as oil reserves, geothermal sources, and, more recently, reservoirs where excess carbon dioxide could potentially be sequestered. They do so by tracking seismic waves that are produced naturally by earthquakes or artificially via explosives or underwater air guns.

link: http://news.mit.edu/2020/machine-learning-picks-out-hidden-vibrations-earthquake-data-0228

📌Via: @cedeeplearning

#deeplearning
#neuralnetworks
#machinelearning

🔻Newly discovered enzyme “square dance” helps generate #DNA building blocks

MIT #biochemists can trap and visualize an enzyme as it becomes active — an important development that may aid in future #drug development.

How do you capture a cellular process that transpires in the blink of an eye? Biochemists at #MIT have devised a way to trap and #visualize a vital enzyme at the moment it becomes active — informing drug development and revealing how biological systems store and transfer energy.

link: http://news.mit.edu/2020/enzyme-square-dance-helps-generate-dna-building-blocks-0330

📌Via: @cedeeplearning

#deeplearning
#neuralnetworks
#python
#statistics
#bioinformatics

📗 The challenge of markerless human motion tracking is the high dimensionality of the search space. Thus, efficient exploration in the search space is of great significance. In this paper, a motion capturing algorithm is proposed for upper body motion tracking. The proposed system tracks human motion based on monocular silhouette-matching, and it is built on the top of a hierarchical particle filter, within which a novel deterministic resampling strategy (#DRS) is applied
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📌Via: @cedeeplearning

📌Other social media: https://linktr.ee/cedeeplearning
Link: http://arxiv.org/abs/2002.09554

A Multi-Channel Neural Graphical Event Model with Negative Evidence

Event datasets are sequences of events of various types occurring irregularly over the time-line, and they are increasingly prevalent in numerous domains. A novel multi-channel RNN that optimally reinforces the negative evidence of no observable events with the introduction of fake event epochs within each consecutive inter-event interval.
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Via: @cedeeplearning
Other social media: https://linktr.ee/cedeeplearning
Link: http://arxiv.org/abs/2002.09575

🔹Deep Learning Allows for Cell Analysis Without Labeling

A new microscopy program requires no fluorescent markers to identify cell type, nuclei, and other characteristics. Micrographs of fluorescently labeled cells are undoubtedly beautiful, but they require invasive and sometimes disruptive or deadly protocols to get their glow. To avoid such perturbations, researchers have developed a computer program that can distinguish between cell types and identify subcellular structures, among other features—all without the fluorescent probes our human eyes rely on.

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link: https://www.the-scientist.com/the-nutshell/deep-learning-allows-for-cell-analysis-without-labeling-30252

📌Via: @cedeeplearning

🔻Some quick tips for #TensorFlow

some quick tips, mostly focused on performance, that reveal common pitfalls and may boost your model and #training performance to new levels. We'll start with preprocessing and your input pipeline, visit graph construction and move on to debugging and performance #optimizations.
1. Preprocessing and input pipelines
Keep #preprocessing clean and lean
2. Watch your queues
3. Graph construction and training
Finalize your graph
4. Profile your #graph
5. Watch your memory
6. #Debugging
Print is your friend
7. Set an operation execution timeout
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link: https://www.deeplearningweekly.com/blog/tensorflow-quick-tips/

📌Via: @cedeeplearning

#deeplearning
#neuralnetworks
#machinelearning

🔹Finding a good read among billions of choices

As natural language processing techniques improve, suggestions are getting speedier and more relevant. With the MIT-IBM Watson AI Lab and his Geometric Data Processing Group at MIT, Solomon recently presented a new technique for cutting through massive amounts of text at the Conference on Neural Information Processing Systems (NeurIPS). Their method combines three popular text-analysis tools — topic modeling, word embeddings, and optimal transport — to deliver better, faster results than competing methods on a popular benchmark for classifying documents. If an algorithm knows what you liked in the past, it can scan the millions of possibilities for something similar. As natural language processing techniques improve, those “you might also like” suggestions are getting speedier and more relevant.
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link: http://news.mit.edu/2019/finding-good-read-among-billions-of-choices-1220

📌Via: @cedeeplearning

#deepelarning
#NLP
#neuralnetworks

🔹Predicting people's driving personalities

System from #MIT CSAIL sizes up drivers as selfish or selfless. Could this help self-driving cars navigate in traffic?
#Self_driving cars are coming. But for all their fancy sensors and intricate data-crunching abilities, even the most #cutting_edge cars lack something that (almost) every 16-year-old with a learner’s permit has: social awareness.

While autonomous technologies have improved substantially, they still ultimately view the drivers around them as obstacles made up of ones and zeros, rather than human beings with specific intentions, motivations, and personalities.
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link: http://news.mit.edu/2019/predicting-driving-personalities-1118

📌Via: @cedeeplearning

#deeplearning
#neuralnetworks
#machinelearning

🔹Deep learning with point clouds

Research aims to make it easier for #self_driving cars, robotics, and other applications to understand the 3D world.
“In #computer_vision and machine learning today, 90 percent of the advances deal only with two-dimensional images,” says MIT Professor Justin Solomon, who was senior author of the new series of papers spearheaded by PhD student Yue Wang. “Our work aims to address a fundamental need to better represent the 3D world, with application not just in autonomous driving, but any field that requires understanding 3D shapes.”
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link: http://news.mit.edu/2019/deep-learning-point-clouds-1021

📌Via: @cedeeplearning

#deeplearning
#machinelearning
#neuralnetworks

🔹What Are The Levels Of Autonomy For #Self_Driving Vehicles?

To get the right understanding of driverless cars, it’s worth understanding that there are various autonomy levels available on the market. The infographic below explains the features of each of these levels. The levels were created in 2016 by SAE International, a society of automotive engineers, which has since become the industry standard when referring to #autonomous_vehicles. We’ve also seen these levels described with other robotic systems when discussing levels of autonomy.
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link: https://www.prosyscom.tech/innovation-future/what-are-the-levels-of-autonomy-for-self-driving-vehicles/

📌Via: @cedeeplearning

#deeplearning
#neuralnetworks
#machinelearning