Machine Learning refers to a set of tools for modeling and understanding complex datasets.
With the explosion of “Big Data” problems, machine learning has become a very hot field in many scientific areas as well as bioinformatics, cancer research, and other biology disciplines. People with statistical learning skills are in high demand.
Combining left and right palmprint images for more accurate personal identifi...LogicMindtech Nologies
IMAGE PROCESSING Projects for M. Tech, IMAGE PROCESSING Projects in Vijayanagar, IMAGE PROCESSING Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, IMAGE PROCESSING IEEE projects in Bangalore, IEEE 2015 IMAGE PROCESSING Projects, MATLAB Image Processing Projects, MATLAB Image Processing Projects in Bangalore, MATLAB Image Processing Projects in Vijayangar
Combining left and right palmprint images for more accurate personal identifi...Shakas Technologies
Multibiometrics can provide higher identificationaccuracy than single biometrics, so it is more suitable forsome real-world personal identification applications that needhigh-standard security. Among various biometrics technologies,palmprint identification has received much attention because ofits good performance.
Combining left and right palmprint images forjpstudcorner
To get this project in ONLINE or through TRAINING Sessions,
Contact:JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83. Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry -9. Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: jpinfotechprojects@gmail.com, web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com
Friday, October 15th, 2021, Sapporo, Hokkaido, Japan.
Hokkaido University ICReDD - Faculty of Medicine Joint Symposium
https://www.icredd.hokudai.ac.jp/event/5993
ICReDD (Institute for Chemical Reaction Design and Discovery)
https://www.icredd.hokudai.ac.jp
Combining left and right palmprint images for more accurate personal identifi...LogicMindtech Nologies
IMAGE PROCESSING Projects for M. Tech, IMAGE PROCESSING Projects in Vijayanagar, IMAGE PROCESSING Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, IMAGE PROCESSING IEEE projects in Bangalore, IEEE 2015 IMAGE PROCESSING Projects, MATLAB Image Processing Projects, MATLAB Image Processing Projects in Bangalore, MATLAB Image Processing Projects in Vijayangar
Combining left and right palmprint images for more accurate personal identifi...Shakas Technologies
Multibiometrics can provide higher identificationaccuracy than single biometrics, so it is more suitable forsome real-world personal identification applications that needhigh-standard security. Among various biometrics technologies,palmprint identification has received much attention because ofits good performance.
Combining left and right palmprint images forjpstudcorner
To get this project in ONLINE or through TRAINING Sessions,
Contact:JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83. Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry -9. Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: jpinfotechprojects@gmail.com, web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com
Friday, October 15th, 2021, Sapporo, Hokkaido, Japan.
Hokkaido University ICReDD - Faculty of Medicine Joint Symposium
https://www.icredd.hokudai.ac.jp/event/5993
ICReDD (Institute for Chemical Reaction Design and Discovery)
https://www.icredd.hokudai.ac.jp
Informs2020 using machine learning to identify the factors of people's mobi...Alex Gilgur
Mobility is an important metric in modeling of community population dynamics and community resilience. It is directly associated with the inorganic changes in a community during and after a disruption (e.g., city gentrification, refugee migration from a war zone, flash mobs in an online community, etc.). Mobility is driven by socioeconomic, demographic, geographical, psychological, and legal parameters. Not all of these parameters are mutually independent (orthogonal). For proper modeling, it is important to avoid collinearity, as otherwise the model will not generalize well. We discuss how machine learning can be used to avoid it by identifying the mutually orthogonal metrics (factors)
Deep Conditional Adversarial learning for polyp Segmentationmultimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper22.pdf
Debapriya Banik and Debotosh Bhattacharjee : Deep Conditional Adversarial learning for polyp Segmentation. Proc. of MediaEval 2020, 14-15 December 2020, Online.
This approach has addressed the Medico automatic polyp segmentation challenge which is a part of Mediaeval 2020. We have proposed a deep conditional adversarial learning based network for the automatic polyp segmentation task. The network comprises of two interdependent models namely a generator and a discriminator. The generator network is a FCN employed for the prediction of the polyp mask while the discriminator enforces the segmentation to be as similar as the real segmented mask (ground truth). Our proposed model achieved a comparative result on the test dataset provided by the organizers of the challenge.
It's a well-known fact that the best explanation of a simple model is the model itself. But often we use complex models, such as ensemble methods or deep networks, so we cannot use the original model as its own best explanation because it is not easy to understand.
In the context of this topic, we will discuss how methods for interpreting model predictions work and will try to understand practical value of these methods.
Example 33.2 Principal Factor Analysis This example uses t.docxSANSKAR20
Example 33.2 Principal Factor Analysis
This example uses the data presented in Example 33.1 and performs a principal factor analysis
with squared multiple correlations for the prior communality estimates. Unlike Example 33.1,
which analyzes the principal components (with default PRIORS=ONE), the current analysis is
based on a common factor model. To use a common factor model, you specify PRIORS=SMC in
the PROC FACTOR statement, as shown in the following:
ods graphics on;
proc factor data=SocioEconomics
priors=smc msa residual
rotate=promax reorder
outstat=fact_all
plots=(scree initloadings preloadings loadings);run;
ods graphics off;
In the PROC FACTOR statement, you include several other options to help you analyze the
results. To help determine whether the common factor model is appropriate, you request the
Kaiser’s measure of sampling adequacy with the MSA option. You specify the RESIDUALS
option to compute the residual correlations and partial correlations.
The ROTATE= and REORDER options are specified to enhance factor interpretability. The
ROTATE=PROMAX option produces an orthogonal varimax prerotation (default) followed by
an oblique Procrustes rotation, and the REORDER option reorders the variables according to
their largest factor loadings. An OUTSTAT= data set is created by PROC FACTOR and
displayed in Output 33.2.15.
PROC FACTOR can produce high-quality graphs that are very useful for interpreting the factor
solutions. To request these graphs, you must first enable ODS Graphics by specifying the ODS
GRAPHICS ON statement, as shown in the preceding statements. All ODS graphs in PROC
FACTOR are requested with the PLOTS= option. In this example, you request a scree plot
(SCREE) and loading plots for the factor matrix during the following three stages: initial
unrotated solution (INITLOADINGS), prerotated (varimax) solution (PRELOADINGS), and
promax-rotated solution (LOADINGS). The scree plot helps you determine the number of
factors, and the loading plots help you visualize the patterns of factor loadings during various
stages of analyses.
Principal Factor Analysis: Kaiser’s MSA and Factor Extraction Results
Output 33.2.1 displays the results of the partial correlations and Kaiser’s measure of sampling
adequacy.
Output 33.2.1 Principal Factor Analysis: Partial Correlations and Kaiser’s MSA
Partial Correlations Controlling all other Variables
Population School Employment Services HouseValue
http://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/statug_factor_sect028.htm
http://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/statug_factor_sect028.htm
http://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/statug_factor_sect006.htm#statug.factor.factorpriorsop
http://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/statug_factor_sect006.htm#statug.factor.factorpriorsop
http://support. ...
A Hybrid Formulation between Differential Evolution and Simulated Annealing A...TELKOMNIKA JOURNAL
The aim of this paper is to solve the optimal reactive power dispatch (ORPD) problem.
Metaheuristic algorithms have been extensively used to solve optimization problems in a reasonable time
without requiring in-depth knowledge of the treated problem. The perform ance of a metaheuristic requires
a compromise between exploitation and exploration of the search space. However, it is rarely to have the
two characteristics in the same search method, where the current emergence of hybrid methods. This
paper presents a hybrid formulation between two different metaheuristics: differential evolution (based on a
population of solution) and simulated annealing (based on a unique solution) to solve ORPD. The first one
is characterized with the high capacity of exploration, while the second has a good exploitation of the
search space. For the control variables, a mixed representation (continuous/discrete), is proposed. The
robustness of the method is tested on the IEEE 30 bus test system.
Responsible AI in Industry: Practical Challenges and Lessons LearnedKrishnaram Kenthapadi
How do we develop machine learning models and systems taking fairness, accuracy, explainability, and transparency into account? How do we protect the privacy of users when building large-scale AI based systems? Model fairness and explainability and protection of user privacy are considered prerequisites for building trust and adoption of AI systems in high stakes domains such as hiring, lending, and healthcare. We will first motivate the need for adopting a “fairness, explainability, and privacy by design” approach when developing AI/ML models and systems for different consumer and enterprise applications from the societal, regulatory, customer, end-user, and model developer perspectives. We will then focus on the application of responsible AI techniques in practice through industry case studies. We will discuss the sociotechnical dimensions and practical challenges, and conclude with the key takeaways and open challenges.
An intelligent mammogram diagnosis system can be very helpful for radiologist in detecting the abnormalities earlier than typical screening techniques. This paper investigates a new classification approach for detection of breast abnormalities in digital mammograms using League Championship Algorithm Optimized Ensembled Fully Complex valued Relaxation Network (LCA-FCRN). The proposed algorithm is based on extracting curvelet fractal texture features from the mammograms and classifying the suspicious regions by applying a pattern classifier. The whole system includes steps for pre-processing, feature extraction, feature selection and classification to classify whether the given input mammogram image is normal or abnormal. The method is applied to MIAS database of 322 film mammograms. The performance of the CAD system is analysed using Receiver Operating Characteristic (ROC) curve. This curve indicates the trade-offs between sensitivity and specificity that is available from a diagnostic system, and thus describes the inherent discrimination capacity of the proposed system. The result shows that the area under the ROC curve of the proposed algorithm is 0.985 with a sensitivity of 98.1% and specificity of 92.105%. Experimental results demonstrate that the proposed method can form an effective CAD system, and achieve good classification accuracy.
IMPLEMENTATION OF MACHINE LEARNING IN E-COMMERCE & BEYONDRabi Das
Presentation for the webinar held on 23rd May 2020, conducted by The IoT Academy for FDP program in collaboration with E&ICT Avademy, IIT Guwahati and delivered by Mr. Shree Kant Das, Growth and Digital Strategy Manager from noon.com.
This presentation discusses the following topics:What is Genetic Algorithms?
Introduction to Genetic Algorithm
Classes of Search Techniques
Components of a GA
Components of a GA
Simple Genetic Algorithm
GA Cycle of Reproduction
Population
Reproduction
Chromosome Modification: Mutation, Crossover, Evaluation, Deletion
Example
GA Technology
Issues for GA Practitioners
Benefits of Genetic Algorithms
GA Application Types
User model is description of users’ information and characteristics in abstract level. User model is very important to adaptive software which aims to support user as much as possible. The process to construct user model is called user modeling. Within learning context where users are learners, the research proposes a so-called Triangular Learner Model (TLM) which is composed of three essential learners’ properties such as knowledge, learning style, and learning history. TLM is the user model that supports built-in inference mechanism. So, the strong point of TLM is to reason out new information from users, based on mathematical tools. This paper focuses on fundamental algorithms and mathematical tools to construct three basic components of TLM such as knowledge sub-model, learning style sub-model, and learning history sub-model. In general, the paper is a summary of results from research on TLM. Algorithms and formulas are described by the succinct way.
Title: Detecting Potential Biases in Sequential Hand Gesture Recognition
This slide deck showcases my master's thesis, delving into the exploration of potential biases in sequential hand gesture recognition. The implemented model, utilizing CNN with VGG16 architecture, achieved an impressive 99.97% accuracy. The analytical framework was constructed using the iNNvestigate toolbox, employing Layerwise Relevance Propagation (LRP). To further interpret the LRP results, I incorporated agglomerative clustering through the Clustimage library.
For more in-depth information, feel free to connect with me on LinkedIn.
Shamim Miroliaei
Today it is possible to obtain genome-wide transcriptome data from single cells using high-throughput sequencing (scRNA-seq). The main advantage of scRNA-seq is that the cellular resolution and the genome wide scope makes it possible to address issues that are intractable using other methods, e.g. bulk RNA-seq or single-cell RT-qPCR. However, to analyze scRNA-seq data, novel methods are required and some of the underlying assumptions for the methods developed for bulk RNA-seq experiments are no longer valid.
More Related Content
Similar to Introduction to Applied Machine Learning
Informs2020 using machine learning to identify the factors of people's mobi...Alex Gilgur
Mobility is an important metric in modeling of community population dynamics and community resilience. It is directly associated with the inorganic changes in a community during and after a disruption (e.g., city gentrification, refugee migration from a war zone, flash mobs in an online community, etc.). Mobility is driven by socioeconomic, demographic, geographical, psychological, and legal parameters. Not all of these parameters are mutually independent (orthogonal). For proper modeling, it is important to avoid collinearity, as otherwise the model will not generalize well. We discuss how machine learning can be used to avoid it by identifying the mutually orthogonal metrics (factors)
Deep Conditional Adversarial learning for polyp Segmentationmultimediaeval
Paper: http://ceur-ws.org/Vol-2882/paper22.pdf
Debapriya Banik and Debotosh Bhattacharjee : Deep Conditional Adversarial learning for polyp Segmentation. Proc. of MediaEval 2020, 14-15 December 2020, Online.
This approach has addressed the Medico automatic polyp segmentation challenge which is a part of Mediaeval 2020. We have proposed a deep conditional adversarial learning based network for the automatic polyp segmentation task. The network comprises of two interdependent models namely a generator and a discriminator. The generator network is a FCN employed for the prediction of the polyp mask while the discriminator enforces the segmentation to be as similar as the real segmented mask (ground truth). Our proposed model achieved a comparative result on the test dataset provided by the organizers of the challenge.
It's a well-known fact that the best explanation of a simple model is the model itself. But often we use complex models, such as ensemble methods or deep networks, so we cannot use the original model as its own best explanation because it is not easy to understand.
In the context of this topic, we will discuss how methods for interpreting model predictions work and will try to understand practical value of these methods.
Example 33.2 Principal Factor Analysis This example uses t.docxSANSKAR20
Example 33.2 Principal Factor Analysis
This example uses the data presented in Example 33.1 and performs a principal factor analysis
with squared multiple correlations for the prior communality estimates. Unlike Example 33.1,
which analyzes the principal components (with default PRIORS=ONE), the current analysis is
based on a common factor model. To use a common factor model, you specify PRIORS=SMC in
the PROC FACTOR statement, as shown in the following:
ods graphics on;
proc factor data=SocioEconomics
priors=smc msa residual
rotate=promax reorder
outstat=fact_all
plots=(scree initloadings preloadings loadings);run;
ods graphics off;
In the PROC FACTOR statement, you include several other options to help you analyze the
results. To help determine whether the common factor model is appropriate, you request the
Kaiser’s measure of sampling adequacy with the MSA option. You specify the RESIDUALS
option to compute the residual correlations and partial correlations.
The ROTATE= and REORDER options are specified to enhance factor interpretability. The
ROTATE=PROMAX option produces an orthogonal varimax prerotation (default) followed by
an oblique Procrustes rotation, and the REORDER option reorders the variables according to
their largest factor loadings. An OUTSTAT= data set is created by PROC FACTOR and
displayed in Output 33.2.15.
PROC FACTOR can produce high-quality graphs that are very useful for interpreting the factor
solutions. To request these graphs, you must first enable ODS Graphics by specifying the ODS
GRAPHICS ON statement, as shown in the preceding statements. All ODS graphs in PROC
FACTOR are requested with the PLOTS= option. In this example, you request a scree plot
(SCREE) and loading plots for the factor matrix during the following three stages: initial
unrotated solution (INITLOADINGS), prerotated (varimax) solution (PRELOADINGS), and
promax-rotated solution (LOADINGS). The scree plot helps you determine the number of
factors, and the loading plots help you visualize the patterns of factor loadings during various
stages of analyses.
Principal Factor Analysis: Kaiser’s MSA and Factor Extraction Results
Output 33.2.1 displays the results of the partial correlations and Kaiser’s measure of sampling
adequacy.
Output 33.2.1 Principal Factor Analysis: Partial Correlations and Kaiser’s MSA
Partial Correlations Controlling all other Variables
Population School Employment Services HouseValue
http://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/statug_factor_sect028.htm
http://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/statug_factor_sect028.htm
http://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/statug_factor_sect006.htm#statug.factor.factorpriorsop
http://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/statug_factor_sect006.htm#statug.factor.factorpriorsop
http://support. ...
A Hybrid Formulation between Differential Evolution and Simulated Annealing A...TELKOMNIKA JOURNAL
The aim of this paper is to solve the optimal reactive power dispatch (ORPD) problem.
Metaheuristic algorithms have been extensively used to solve optimization problems in a reasonable time
without requiring in-depth knowledge of the treated problem. The perform ance of a metaheuristic requires
a compromise between exploitation and exploration of the search space. However, it is rarely to have the
two characteristics in the same search method, where the current emergence of hybrid methods. This
paper presents a hybrid formulation between two different metaheuristics: differential evolution (based on a
population of solution) and simulated annealing (based on a unique solution) to solve ORPD. The first one
is characterized with the high capacity of exploration, while the second has a good exploitation of the
search space. For the control variables, a mixed representation (continuous/discrete), is proposed. The
robustness of the method is tested on the IEEE 30 bus test system.
Responsible AI in Industry: Practical Challenges and Lessons LearnedKrishnaram Kenthapadi
How do we develop machine learning models and systems taking fairness, accuracy, explainability, and transparency into account? How do we protect the privacy of users when building large-scale AI based systems? Model fairness and explainability and protection of user privacy are considered prerequisites for building trust and adoption of AI systems in high stakes domains such as hiring, lending, and healthcare. We will first motivate the need for adopting a “fairness, explainability, and privacy by design” approach when developing AI/ML models and systems for different consumer and enterprise applications from the societal, regulatory, customer, end-user, and model developer perspectives. We will then focus on the application of responsible AI techniques in practice through industry case studies. We will discuss the sociotechnical dimensions and practical challenges, and conclude with the key takeaways and open challenges.
An intelligent mammogram diagnosis system can be very helpful for radiologist in detecting the abnormalities earlier than typical screening techniques. This paper investigates a new classification approach for detection of breast abnormalities in digital mammograms using League Championship Algorithm Optimized Ensembled Fully Complex valued Relaxation Network (LCA-FCRN). The proposed algorithm is based on extracting curvelet fractal texture features from the mammograms and classifying the suspicious regions by applying a pattern classifier. The whole system includes steps for pre-processing, feature extraction, feature selection and classification to classify whether the given input mammogram image is normal or abnormal. The method is applied to MIAS database of 322 film mammograms. The performance of the CAD system is analysed using Receiver Operating Characteristic (ROC) curve. This curve indicates the trade-offs between sensitivity and specificity that is available from a diagnostic system, and thus describes the inherent discrimination capacity of the proposed system. The result shows that the area under the ROC curve of the proposed algorithm is 0.985 with a sensitivity of 98.1% and specificity of 92.105%. Experimental results demonstrate that the proposed method can form an effective CAD system, and achieve good classification accuracy.
IMPLEMENTATION OF MACHINE LEARNING IN E-COMMERCE & BEYONDRabi Das
Presentation for the webinar held on 23rd May 2020, conducted by The IoT Academy for FDP program in collaboration with E&ICT Avademy, IIT Guwahati and delivered by Mr. Shree Kant Das, Growth and Digital Strategy Manager from noon.com.
This presentation discusses the following topics:What is Genetic Algorithms?
Introduction to Genetic Algorithm
Classes of Search Techniques
Components of a GA
Components of a GA
Simple Genetic Algorithm
GA Cycle of Reproduction
Population
Reproduction
Chromosome Modification: Mutation, Crossover, Evaluation, Deletion
Example
GA Technology
Issues for GA Practitioners
Benefits of Genetic Algorithms
GA Application Types
User model is description of users’ information and characteristics in abstract level. User model is very important to adaptive software which aims to support user as much as possible. The process to construct user model is called user modeling. Within learning context where users are learners, the research proposes a so-called Triangular Learner Model (TLM) which is composed of three essential learners’ properties such as knowledge, learning style, and learning history. TLM is the user model that supports built-in inference mechanism. So, the strong point of TLM is to reason out new information from users, based on mathematical tools. This paper focuses on fundamental algorithms and mathematical tools to construct three basic components of TLM such as knowledge sub-model, learning style sub-model, and learning history sub-model. In general, the paper is a summary of results from research on TLM. Algorithms and formulas are described by the succinct way.
Title: Detecting Potential Biases in Sequential Hand Gesture Recognition
This slide deck showcases my master's thesis, delving into the exploration of potential biases in sequential hand gesture recognition. The implemented model, utilizing CNN with VGG16 architecture, achieved an impressive 99.97% accuracy. The analytical framework was constructed using the iNNvestigate toolbox, employing Layerwise Relevance Propagation (LRP). To further interpret the LRP results, I incorporated agglomerative clustering through the Clustimage library.
For more in-depth information, feel free to connect with me on LinkedIn.
Shamim Miroliaei
Similar to Introduction to Applied Machine Learning (20)
Today it is possible to obtain genome-wide transcriptome data from single cells using high-throughput sequencing (scRNA-seq). The main advantage of scRNA-seq is that the cellular resolution and the genome wide scope makes it possible to address issues that are intractable using other methods, e.g. bulk RNA-seq or single-cell RT-qPCR. However, to analyze scRNA-seq data, novel methods are required and some of the underlying assumptions for the methods developed for bulk RNA-seq experiments are no longer valid.
OSPREY is a suite of programs for computational structure-based protein design. OSPREY is developed in the lab of Prof. Bruce Donald at Duke University.
OSPREY 3.0 offers a unique package of advantages over other design software, including provable design algorithms that account for continuous flexibility during design and model conformational.
OSPREY has been used for an impressive number of empirically successful designs, ranging from enzyme design to antibody design to prediction of antibiotic resistance mutations.
OSPREY 3.0 is available at http://www.cs.duke.edu/donaldlab/osprey.php as free and open-source software.
Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets.
more details:
https://youtu.be/IF_AR7iHMY8
References:
https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/
https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-9-559
More detail: https://www.civilica.com/Paper-CITCOMP02-CITCOMP02_141=%D9%BE%D8%B1%D8%AF%D8%A7%D8%B2%D8%B4-%DA%AF%D9%81%D8%AA%D8%A7%D8%B1-%D9%88-%D8%A7%D9%84%D9%82%D8%A7%DB%8C-%D8%B2%D8%A8%D8%A7%D9%86-%DA%AF%D9%81%D8%AA%D8%A7%D8%B1%DB%8C.html
Differential expression analysis means taking the normalized read count data and performing statistical analysis to discover quantitative changes in expression levels between experimental groups.
for more details:
https://www.youtube.com/watch?v=__vrYM0D-SM
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
1. 1 of 34
ML - 2021
Biological Sciences faculty
Biophysics Department
Introduction to Applied Machine Learning
Presented By
Alireza Doustmohammadi
Graduate Student in Bioinformatics
January 2021
5. Why do we need to prediction?
5 of 34
ML - 2021
6. Why do we need to prediction?
6 of 34
Welcome To de Era of
Big Data …..
ML - 2021
7. Why do we need to prediction?
7 of 34
ML - 2021
8. Why do we need to prediction?
8 of 34
[https://www.ncbi.nlm.nih.gov/genbank/statistics/]
ML - 2021
9. Why do we need to prediction?
9 of 34
[https://www.rcsb.org/stats/summary]
Molecular Type X-ray NMR EM Multiple methods Neutron Other Total
Protein (only) 135896 34576 4544 165 67 34 152280
Protein/NA 7177 269 1603 3 0 0 9052
Nucleic acid (only) 2158 1340 53 7 2 1 3561
Other 149 31 3 0 0 0 183
Total 153400 13453 6814 181 69 37 173754
PDB Data Distribution by Experimental Method and Molecular Type:
ML - 2021
10. Why do we need to prediction?
10 of 34
Basic Concepts &
Nomenclatures
ML - 2021
28. Algorithm (Model Selection): over fitting
28 of 34
ML - 2021
“All models are wrong,
but some are useful.”
George Box, British Statistician
1919-213
30. Algorithm (Model Selection): over fitting
30 of 34
Increasing the size of the data set may reduce the over-fitting
ML - 2021
31. Algorithm (Model Selection): over fitting
31 of 34
Bias – variance Trade off
ML - 2021
Increase Flexibility:
▪ Bias tends to initially decrease
faster than variance increases
▪ At some point has little impact on
the bias but starts to significantly
increase the variance.
