Wormhole or Generative Adversarial Networks?!? :)
#gans #deeplearning #ai #machinelearning #generativeadversarialnetworks #artificialintelligence #data #wormhole #galaxy
🔭 @DeepGravity
#gans #deeplearning #ai #machinelearning #generativeadversarialnetworks #artificialintelligence #data #wormhole #galaxy
🔭 @DeepGravity
Everything a Data Scientist Should Know About Data Management
For full-stack data science mastery, you must understand data management along with all the bells and whistles of machine learning. This high-level overview is a road map for the history and current state of the expansive options for data storage and infrastructure solutions.
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#Data
🔭 @DeepGravity
For full-stack data science mastery, you must understand data management along with all the bells and whistles of machine learning. This high-level overview is a road map for the history and current state of the expansive options for data storage and infrastructure solutions.
Link to the article
#Data
🔭 @DeepGravity
Researchers from #Microsoft have created #Icebreaker, a #DeepGenerativeModel with a new element-wise method of acquiring data that uses AI to help decision making and minimize #data requirements. Learn how less costly information can be collected
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🔭 @DeepGravity
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🔭 @DeepGravity
Microsoft Research
Icebreaker trains machine learning models with low data cost
Microsoft researchers have created Icebreaker, a deep generative model with a new element-wise information acquisition method that uses AI to aid decision making and minimize data requirements. Learn how it helps get information w/ less cost.
Multi-Object Portion Tracking in 4D Fluorescence Microscopy Imagery with #Deep Feature Maps
3D fluorescence microscopy of living organisms has increasingly become an essential and powerful tool in biomedical research and diagnosis. An exploding amount of imaging #data has been collected, whereas efficient and effective computational tools to extract information from them are still lagging behind. This is largely due to the challenges in analyzing biological data. Interesting biological structures are not only small, but are often morphologically irregular and highly dynamic. Although tracking cells in live organisms has been studied for years, existing tracking methods for cells are not effective in tracking subcellular structures, such as protein complexes, which feature in continuous morphological changes including split and merge, in addition to fast migration and complex motion. In this paper, we first define the problem of multi-object portion tracking to model the protein object tracking process. A multi-object tracking method with portion matching is proposed based on 3D segmentation results. The proposed method distills deep feature maps from deep networks, then recognizes and matches object portions using an extended search. Experimental results confirm that the proposed method achieves 2.96 consistent tracking accuracy and 35.48 than the state-of-art methods.
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🔭 @DeepGravity
3D fluorescence microscopy of living organisms has increasingly become an essential and powerful tool in biomedical research and diagnosis. An exploding amount of imaging #data has been collected, whereas efficient and effective computational tools to extract information from them are still lagging behind. This is largely due to the challenges in analyzing biological data. Interesting biological structures are not only small, but are often morphologically irregular and highly dynamic. Although tracking cells in live organisms has been studied for years, existing tracking methods for cells are not effective in tracking subcellular structures, such as protein complexes, which feature in continuous morphological changes including split and merge, in addition to fast migration and complex motion. In this paper, we first define the problem of multi-object portion tracking to model the protein object tracking process. A multi-object tracking method with portion matching is proposed based on 3D segmentation results. The proposed method distills deep feature maps from deep networks, then recognizes and matches object portions using an extended search. Experimental results confirm that the proposed method achieves 2.96 consistent tracking accuracy and 35.48 than the state-of-art methods.
Link
🔭 @DeepGravity
#DecisionTree vs #RandomForest vs #GradientBoostingMachines: Explained Simply
Decision Trees, Random Forests and Boosting are among the top 16 #data science and machine learning tools used by data scientists. The three methods are similar, with a significant amount of overlap. In a nutshell:
* A decision tree is a simple, decision making-diagram.
* Random forests are a large number of trees, combined (using averages or "majority rules") at the end of the process.
* Gradient boosting machines also combine decision trees, but start the combining process at the beginning, instead of at the end.
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🔭 @DeepGravity
Decision Trees, Random Forests and Boosting are among the top 16 #data science and machine learning tools used by data scientists. The three methods are similar, with a significant amount of overlap. In a nutshell:
* A decision tree is a simple, decision making-diagram.
* Random forests are a large number of trees, combined (using averages or "majority rules") at the end of the process.
* Gradient boosting machines also combine decision trees, but start the combining process at the beginning, instead of at the end.
Link
🔭 @DeepGravity
Best #Data #Visualization Techniques for small and large data
Data visualization is used in many areas to model complex events and visualize phenomena that cannot be observed directly, such as weather patterns, medical conditions or mathematical relationships. Here we review basic data visualization tools and techniques.
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🔭 @DeepGravity
Data visualization is used in many areas to model complex events and visualize phenomena that cannot be observed directly, such as weather patterns, medical conditions or mathematical relationships. Here we review basic data visualization tools and techniques.
Link
🔭 @DeepGravity
KDnuggets
Best Data Visualization Techniques for small and large data - KDnuggets
Data visualization is used in many areas to model complex events and visualize phenomena that cannot be observed directly, such as weather patterns, medical conditions or mathematical relationships. Here we review basic data visualization tools and techniques.
5 #Financial Services Tech Trends to Watch in 2020
1. The Role of #ArtificialIntelligence in Finance Will Expand
2. Financial Services Firms Will Grow Their Use of #Data Analytics
3. #Blockchain Will Be a Key Security Solution
4. More #Bank Branches Will Undergo Digital Transformations
5. Automation Will Take Over More Financial Services
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🔭 @DeepGravity
1. The Role of #ArtificialIntelligence in Finance Will Expand
2. Financial Services Firms Will Grow Their Use of #Data Analytics
3. #Blockchain Will Be a Key Security Solution
4. More #Bank Branches Will Undergo Digital Transformations
5. Automation Will Take Over More Financial Services
Link
🔭 @DeepGravity
Technology Solutions That Drive Business
5 Financial Services Tech Trends to Watch in 2020
AI, blockchain and automation are among the trends poised to alter the financial services industry.