Connect with us

Big Data

The Machine & Deep Learning Compendium Open Book

Published

on

The Machine & Deep Learning Compendium Open Book

After years in the making, this extensive and comprehensive ebook resource is now available and open for data scientists and ML engineers. Learn from and contribute to this tome of valuable information to support all your work in data science from engineering to strategy to management.


By Ori Cohen, AI/ML/DL Expert, Researcher, Data Scientist.

Partial Topic List From The Machine & Deep Learning Compendium.

Nearly a year ago, I announced the Machine & Deep Learning Compendium, a Google document that I have been writing for the last 4 years. The ML Compendium contains over 500 topics, and it is over 400 pages long.

Today, I’m announcing that the Compendium is fully open. It is now a project on GitBook and GitHub (please star it!). I believe in knowledge sharing, and the Compendium will always be free to everyone.

I see this compendium as a gateway, as a frequently visited resource for people of various proficiency levels, for industry data scientists, and academics. The compendium will save you countless hours googling and sifting through articles that may not give you any value.

The Compendium includes around 500 topics that contain various summaries, links, and articles that I have read on numerous topics that I found interesting or that I had needed to learn. It includes the majority of modern machine learning algorithms, statistics, feature selection and engineering techniques, deep-learning, NLP, audio, deep and classic vision, time series, anomaly detection, graphs, experiment management, and much more. In addition, strategic topics, such as data science management and team building, are highlighted as well as other essential topics, such as product management, product design, and a technology stack from a data science perspective.

Please keep in mind that this is a perpetual work in progress on a variety of topics. If you feel that something should be changed, you can now easily contribute using GitBook, GitHub, or contact me.

GitBook

The ML Compendium is a project on GitBook, which means that you can contribute as a GitBook writer. Writing and editing content using the internal editor is easy and intuitive, especially compared to the more advanced option of contributing via GitHub pull requests.

You can visit the mlcompendium.com website or directly access the compendium “book”. As seen in Figure 1, on the left you have the main topics and on the right the sub-topics which are in each main topic, not to mention that the search feature is more advanced, especially compared to the old method of using CTRL-F inside the original document.

Figure 1: The Machine & Deep Learning Compendium with the GitBook UI.

The following are two topics that may interest you, the natural language processing (NLP) page, as seen in Figure 2, and the deep neural nets (DNN) page, as seen in Figure 3.

Figure 2: Natural Language Processing.

Figure 3: Deep Neural Nets.

GitHub

Alternatively, you can use GitHub (Figure 4) if you want to contribute content, please place the content within the proper topic, then create a pull request to a new branch. Finally, don’t forget to ‘Star’ the project if you like it.

The following is a simple set of instructions for contributing using GitHub:

  1. git clone https://github.com/orico/www.mlcompendium.com.git
  2. git branch mybranch
  3. git switch mybranch
  4. add your content
  5. git add the-edited-file
  6. git commit -m “my content”
  7. git push
  8. create a PR by visiting this link: https://github.com/orico/stateofmlops/pull/new/mybranch

Figure 4: The mlcompendium.com GitHub project.

.

Original. Reposted with permission.

Bio: Dr. Ori Cohen has a Ph.D. in Computer Science with a focus on machine learning. He is the author of the ML & DL Compendium and the StateOfMLOps.com. He is a lead data scientist at New Relic TLV, doing machine and deep learning research in the field of AIOps & MLOps.

Related:


PlatoAi. Web3 Reimagined. Data Intelligence Amplified.
Click here to access.

Source: https://www.kdnuggets.com/2021/09/machine-deep-learning-open-book.html

Big Data

If you did not already know

Published

on

DataOps google
DataOps is an automated, process-oriented methodology, used by analytic and data teams, to improve the quality and reduce the cycle time of data analytics. While DataOps began as a set of best practices, it has now matured to become a new and independent approach to data analytics. DataOps applies to the entire data lifecycle from data preparation to reporting, and recognizes the interconnected nature of the data analytics team and information technology operations. From a process and methodology perspective, DataOps applies Agile software development, DevOps software development practices and the statistical process control used in lean manufacturing, to data analytics. In DataOps, development of new analytics is streamlined using Agile software development, an iterative project management methodology that replaces the traditional Waterfall sequential methodology. Studies show that software development projects complete significantly faster and with far fewer defects when Agile Development is used. The Agile methodology is particularly effective in environments where requirements are quickly evolving – a situation well known to data analytics professionals. DevOps focuses on continuous delivery by leveraging on-demand IT resources and by automating test and deployment of analytics. This merging of software development and IT operations has improved velocity, quality, predictability and scale of software engineering and deployment. Borrowing methods from DevOps, DataOps seeks to bring these same improvements to data analytics. Like lean manufacturing, DataOps utilizes statistical process control (SPC) to monitor and control the data analytics pipeline. With SPC in place, the data flowing through an operational system is constantly monitored and verified to be working. If an anomaly occurs, the data analytics team can be notified through an automated alert. DataOps is not tied to a particular technology, architecture, tool, language or framework. Tools that support DataOps promote collaboration, orchestration, agility, quality, security, access and ease of use. …

CoSegNet google
We introduce CoSegNet, a deep neural network architecture for co-segmentation of a set of 3D shapes represented as point clouds. CoSegNet takes as input a set of unsegmented shapes, proposes per-shape parts, and then jointly optimizes the part labelings across the set subjected to a novel group consistency loss expressed via matrix rank estimates. The proposals are refined in each iteration by an auxiliary network that acts as a weak regularizing prior, pre-trained to denoise noisy, unlabeled parts from a large collection of segmented 3D shapes, where the part compositions within the same object category can be highly inconsistent. The output is a consistent part labeling for the input set, with each shape segmented into up to K (a user-specified hyperparameter) parts. The overall pipeline is thus weakly supervised, producing consistent segmentations tailored to the test set, without consistent ground-truth segmentations. We show qualitative and quantitative results from CoSegNet and evaluate it via ablation studies and comparisons to state-of-the-art co-segmentation methods. …

Stochastic Computation Graph (SCG) google
Stochastic computation graphs are directed acyclic graphs that encode the dependency structure of computation to be performed. The graphical notation generalizes directed graphical models. …

Smooth Density Spatial Quantile Regression google
We derive the properties and demonstrate the desirability of a model-based method for estimating the spatially-varying effects of covariates on the quantile function. By modeling the quantile function as a combination of I-spline basis functions and Pareto tail distributions, we allow for flexible parametric modeling of the extremes while preserving non-parametric flexibility in the center of the distribution. We further establish that the model guarantees the desired degree of differentiability in the density function and enables the estimation of non-stationary covariance functions dependent on the predictors. We demonstrate through a simulation study that the proposed method produces more efficient estimates of the effects of predictors than other methods, particularly in distributions with heavy tails. To illustrate the utility of the model we apply it to measurements of benzene collected around an oil refinery to determine the effect of an emission source within the refinery on the distribution of the fence line measurements. …

PlatoAi. Web3 Reimagined. Data Intelligence Amplified.
Click here to access.

Source: https://analytixon.com/2021/10/24/if-you-did-not-already-know-1540/

Continue Reading

Big Data

If you did not already know

Published

on

DataOps google
DataOps is an automated, process-oriented methodology, used by analytic and data teams, to improve the quality and reduce the cycle time of data analytics. While DataOps began as a set of best practices, it has now matured to become a new and independent approach to data analytics. DataOps applies to the entire data lifecycle from data preparation to reporting, and recognizes the interconnected nature of the data analytics team and information technology operations. From a process and methodology perspective, DataOps applies Agile software development, DevOps software development practices and the statistical process control used in lean manufacturing, to data analytics. In DataOps, development of new analytics is streamlined using Agile software development, an iterative project management methodology that replaces the traditional Waterfall sequential methodology. Studies show that software development projects complete significantly faster and with far fewer defects when Agile Development is used. The Agile methodology is particularly effective in environments where requirements are quickly evolving – a situation well known to data analytics professionals. DevOps focuses on continuous delivery by leveraging on-demand IT resources and by automating test and deployment of analytics. This merging of software development and IT operations has improved velocity, quality, predictability and scale of software engineering and deployment. Borrowing methods from DevOps, DataOps seeks to bring these same improvements to data analytics. Like lean manufacturing, DataOps utilizes statistical process control (SPC) to monitor and control the data analytics pipeline. With SPC in place, the data flowing through an operational system is constantly monitored and verified to be working. If an anomaly occurs, the data analytics team can be notified through an automated alert. DataOps is not tied to a particular technology, architecture, tool, language or framework. Tools that support DataOps promote collaboration, orchestration, agility, quality, security, access and ease of use. …

CoSegNet google
We introduce CoSegNet, a deep neural network architecture for co-segmentation of a set of 3D shapes represented as point clouds. CoSegNet takes as input a set of unsegmented shapes, proposes per-shape parts, and then jointly optimizes the part labelings across the set subjected to a novel group consistency loss expressed via matrix rank estimates. The proposals are refined in each iteration by an auxiliary network that acts as a weak regularizing prior, pre-trained to denoise noisy, unlabeled parts from a large collection of segmented 3D shapes, where the part compositions within the same object category can be highly inconsistent. The output is a consistent part labeling for the input set, with each shape segmented into up to K (a user-specified hyperparameter) parts. The overall pipeline is thus weakly supervised, producing consistent segmentations tailored to the test set, without consistent ground-truth segmentations. We show qualitative and quantitative results from CoSegNet and evaluate it via ablation studies and comparisons to state-of-the-art co-segmentation methods. …

Stochastic Computation Graph (SCG) google
Stochastic computation graphs are directed acyclic graphs that encode the dependency structure of computation to be performed. The graphical notation generalizes directed graphical models. …

Smooth Density Spatial Quantile Regression google
We derive the properties and demonstrate the desirability of a model-based method for estimating the spatially-varying effects of covariates on the quantile function. By modeling the quantile function as a combination of I-spline basis functions and Pareto tail distributions, we allow for flexible parametric modeling of the extremes while preserving non-parametric flexibility in the center of the distribution. We further establish that the model guarantees the desired degree of differentiability in the density function and enables the estimation of non-stationary covariance functions dependent on the predictors. We demonstrate through a simulation study that the proposed method produces more efficient estimates of the effects of predictors than other methods, particularly in distributions with heavy tails. To illustrate the utility of the model we apply it to measurements of benzene collected around an oil refinery to determine the effect of an emission source within the refinery on the distribution of the fence line measurements. …

PlatoAi. Web3 Reimagined. Data Intelligence Amplified.
Click here to access.

Source: https://analytixon.com/2021/10/24/if-you-did-not-already-know-1540/

Continue Reading

Big Data

If you did not already know

Published

on

Correntropy google
Correntropy is a nonlinear similarity measure between two random variables.
Learning with the Maximum Correntropy Criterion Induced Losses for Regression


Patient Event Graph (PatientEG) google
Medical activities, such as diagnoses, medicine treatments, and laboratory tests, as well as temporal relations between these activities are the basic concepts in clinical research. However, existing relational data model on electronic medical records (EMRs) lacks explicit and accurate semantic definitions of these concepts. It leads to the inconvenience of query construction and the inefficiency of query execution where multi-table join queries are frequently required. In this paper, we propose a patient event graph (PatientEG) model to capture the characteristics of EMRs. We respectively define five types of medical entities, five types of medical events and five types of temporal relations. Based on the proposed model, we also construct a PatientEG dataset with 191,294 events, 3,429 distinct entities, and 545,993 temporal relations using EMRs from Shanghai Shuguang hospital. To help to normalize entity values which contain synonyms, hyponymies, and abbreviations, we link them with the Chinese biomedical knowledge graph. With the help of PatientEG dataset, we are able to conveniently perform complex queries for clinical research such as auxiliary diagnosis and therapeutic effectiveness analysis. In addition, we provide a SPARQL endpoint to access PatientEG dataset and the dataset is also publicly available online. Also, we list several illustrative SPARQL queries on our website. …

LogitBoost Autoregressive Networks google
Multivariate binary distributions can be decomposed into products of univariate conditional distributions. Recently popular approaches have modeled these conditionals through neural networks with sophisticated weight-sharing structures. It is shown that state-of-the-art performance on several standard benchmark datasets can actually be achieved by training separate probability estimators for each dimension. In that case, model training can be trivially parallelized over data dimensions. On the other hand, complexity control has to be performed for each learned conditional distribution. Three possible methods are considered and experimentally compared. The estimator that is employed for each conditional is LogitBoost. Similarities and differences between the proposed approach and autoregressive models based on neural networks are discussed in detail. …

Discretification google
Discretification’ is the mechanism of making continuous data discrete. If you really grasp the concept, you may be thinking ‘Wait a minute, the type of data we are collecting is discrete in and of itself! Data can EITHER be discrete OR continuous, it can’t be both!’ You would be correct. But what if we manually selected values along that continuous measurement, and declared them to be in a specific category? For instance, if we declare 72.0 degrees and greater to be ‘Hot’, 35.0-71.9 degrees to be ‘Moderate’, and anything lower than 35.0 degrees to be ‘Cold’, we have ‘discretified’ temperature! Our readings that were once continuous now fit into distinct categories. So, where we do we draw the boundaries for these categories? What makes 35.0 degrees ‘Cold’ and 35.1 degrees ‘Moderate’? At is at this juncture that the TRUE decision is being made. The beauty of approaching the challenge in this manner is that it is data-centric, not concept-centric. Let’s walk through our marketing example first without using discretification, then with it. …

PlatoAi. Web3 Reimagined. Data Intelligence Amplified.
Click here to access.

Source: https://analytixon.com/2021/10/23/if-you-did-not-already-know-1539/

Continue Reading

Big Data

Capturing the signal of weak electricigens: a worthy endeavour

Published

on

Recently several non-traditional electroactive microorganisms have been discovered. These can be considered weak electricigens; microorganisms that typically rely on soluble electron acceptors and donors in their lifecycle but are also capable of extracellular electron transfer (EET), resulting in either a low, unreliable, or otherwise unexpected current. These unanticipated electroactive microorganisms represent a new chapter in electromicrobiology and have important medical, environmental, and biotechnological relevance.
PlatoAi. Web3 Reimagined. Data Intelligence Amplified.
Click here to access.

Source: https://www.cell.com/trends/biotechnology/fulltext/S0167-7799(21)00229-8?rss=yes

Continue Reading
Blockchain3 days ago

People’s payment attitude: Why cash Remains the most Common Means of Payment & How Technology and Crypto have more Advantages as a Means of payment

Automotive4 days ago

7 Secrets That Automakers Wish You Don’t Know

Startups3 days ago

The 12 TikTok facts you should know

Supply Chain3 days ago

LPG tubes – what to think about

Energy2 days ago

U Power ties up with Bosch to collaborate on Super Board technology

Gaming4 days ago

New Steam Games You Might Have Missed In August 2021

Blockchain4 days ago

What Is the Best Crypto IRA for Me? Use These 6 Pieces of Criteria to Find Out More

Gaming4 days ago

How do casinos without an account work?

IOT4 days ago

The Benefits of Using IoT SIM Card Technology

Blockchain4 days ago

The Most Profitable Cryptocurrencies on the Market

Gaming4 days ago

Norway will crack down on the unlicensed iGaming market with a new gaming law

Blockchain4 days ago

What does swapping crypto mean?

Energy2 days ago

Piperylene Market Size to Grow by USD 428.50 mn from 2020 to 2024 | Growing Demand for Piperylene-based Adhesives to Boost Growth | Technavio

Energy2 days ago

Notice of Data Security Breach Incident

AR/VR5 days ago

Preview: Little Cities – Delightful City Building on Quest

Blockchain2 days ago

Blockchain & Infrastructure Post-Event Release

Blockchain2 days ago

Week Ahead – Between a rock and a hard place

Cyber Security2 days ago

Ransomware Took a New Twist with US Leading a Law Enforcement Effort to Hack Back

Esports2 days ago

How to get Shiny Zacian and Zamazenta in Pokémon Sword and Shield

Code2 days ago

How does XML to JSON converter work?

Trending