Marketing-for-Engineers, Magnitech Engineers the ultimate defence business Consultant with the experience of more than two decades, provide multi-pronged consultancy service that helps the clientele with result-centric marketing for their products or services
From user ankitmagnitech
Marketing-for-Engineers, Dynamic Web Marketing's Java engineer coding challenge. Read instructions for more details
From user artur5522
Marketing-for-Engineers, An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68] Decision trees Main article: Decision tree learning Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. Support vector machines Main article: Support vector machines Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[69] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Illustration of linear regression on a data set. Regression analysis Main article: Regression analysis Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[70]), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space. Bayesian networks Main article: Bayesian network A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Genetic algorithms Main article: Genetic algorithm A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[71][72] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[73] Training models Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Federated learning Main article: Federated learning Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[74] Applications There are many applications for machine learning, including: Agriculture Anatomy Adaptive websites Affective computing Banking Bioinformatics Brain–machine interfaces Cheminformatics Citizen science Computer networks Computer vision Credit-card fraud detection Data quality DNA sequence classification Economics Financial market analysis[75] General game playing Handwriting recognition Information retrieval Insurance Internet fraud detection Linguistics Machine learning control Machine perception Machine translation Marketing Medical diagnosis Natural language processing Natural language understanding Online advertising Optimization Recommender systems Robot locomotion Search engines Sentiment analysis Sequence mining Software engineering Speech recognition Structural health monitoring Syntactic pattern recognition Telecommunication Theorem proving Time series forecasting User behavior analytics In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[76] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[77] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[78] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[79] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.[80] In 2019 Springer Nature published the first research book created using machine learning.[81] Limitations Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[82][83][84] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[85] In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[86] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.[87][88] Bias Main article: Algorithmic bias Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.[89] Language models learned from data have been shown to contain human-like biases.[90][91] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[92][93] In 2015, Google photos would often tag black people as gorillas,[94] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[95] Similar issues with recognizing non-white people have been found in many other systems.[96] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[97] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[98] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”[99] Model assessments Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[100] In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[101] Ethics Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[102] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[103][104] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[105][106] Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[107] Hardware Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.[108] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[109] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[110][111] Software Software suites containing a variety of machine learning algorithms include the following: Free and open-source so
From user aryia-behroziuan
Marketing-for-Engineers, About Blockchain Pharmaceuticals A DeFi organization, Blockchain Pharmaceuticals is a pharmaceutical company, community, and Fractionalized Intellectual Property NFT marketplace for pharmaceutical, life science, and nutrition scientists and engineers. We provide the technology, marketing, legal, compliance, and FDA regulatory backbone to pair engineering projects with scientists and drug developers, enabling them to innovate together in an entirely new way.
From user blockchainpharmaceuticals
Marketing-for-Engineers, Software Engineer's Affiliate Program Hub: Explore curated affiliate programs tailored for software engineers. Get reviews, marketing strategies, resources, and join a supportive community. Monetize your skills and passion for technology. Note: Research before engaging in partnerships.
From user digital-marketing-engineer
Marketing-for-Engineers, Collection of links and cases study for digital analytics for marketing
From organization dp6
Marketing-for-Engineers, I am an Electrical Engineer and Digital Marketing Executive. I can do SEO for your website. Thank you
From user erakshaykumarjpr
Marketing-for-Engineers, Marketing site for Graham Butler, Drummer, Producer, Engineer
From user gbdrummer
Marketing-for-Engineers, A curated collection of marketing articles & tools to grow your product.
From organization goabstract
Marketing-for-Engineers, Snippet made for Stony Brooks Website. This was made during my internship as a Software Engineer at Stony Brook's Communications & Marketing department. This snippet allows users to dynamically write content and change views in a nice and clean way.
From user hdkid7
Marketing-for-Engineers, A solution for Get Your Guide - Python Backend Engineer - Marketing team challenge
From user iahvector
Marketing-for-Engineers, This repository attempts to provide a solution for Marketing Digital challenge as part of Analytics Engineer Exam.
From user johngodoi
Marketing-for-Engineers, Segmentation and propension modelling for marketing campaign (final project of machine learning engineer nanodegree)
From user lokopobit
Marketing-for-Engineers, Technical 1 for the marketing engineer
From organization loomhq
Marketing-for-Engineers, assignment for applying for Software Engineer (Python/Django Framework) internship at Digital Marketing Lane
From user mahati11
Marketing-for-Engineers, I am looking for passionate engineers/designers/marketing people to start a startup!
From user markitoooo
Marketing-for-Engineers, needing development cooperation for bike/bicycle navigation such as PR, marketing, electrical engineer,...
From organization navigation4bike
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This Agreement will be binding upon and inure to the benefit of the respective successors and assigns of the parties hereto. The following sections of this Agreement will survive any termination thereof: 1, 4, 5, 6 (except the last two sentences), 7, 8, 9, 10, 11, 12, 14, 16, and 17. 17. Google Analytics for Firebase. If You link a Property to Firebase (“Firebase Linkage”) as part of using the Service, the following terms, in addition to Sections 1-16 above, will also apply to You, and will also govern Your use of the Service, including with respect to Your use of Firebase Linkage. Other than as modified below, all other terms will stay the same and continue to apply. In the event of a conflict between this Section 17 and Sections 1-16 above, the terms in Section 17 will govern and control solely with respect to Your use of the Firebase Linkage. The following definition in Section 1 is modified as follows: "Hit" means a collection of interactions that results in data being sent to the Service and processed. Examples of Hits may include page view hits and ecommerce hits. A Hit can be a call to the Service by various libraries, but does not have to be so (e.g., a Hit can be delivered to the Service by other Google Analytics-supported protocols and mechanisms made available by the Service to You). For the sake of clarity, a Hit does not include certain events whose collection reflects interactions with certain Properties capable of supporting multiple data streams, and which may include screen views and custom events (the collection of events, an “Enhanced Packet”). The following sentence is added to the end of Section 7 as follows: If You link a Property to a Firebase project (“Firebase Linkage”) (i) certain data from Your Property, including Customer Data, may be made accessible within or to any other entity or personnel according to permissions set in Firebase and (ii) that Property may have certain Service settings modified by authorized personnel of Firebase (notwithstanding the settings You may have designated for that Property within the Service). Last Updated June 17, 2019 Follow us About Google Marketing Platform Overview For Small Businesses For Enterprise Learning & support Support Blog Analytics Academy Skillshop Google Primer Developers & partners Google Marketing Platform Partners Google Measurement Partners Analytics for developers Tag Manager for developers Surveys for developers Campaign Manager 360 for developers Related products Google Ads Google AdSense Google Ad Manager Google Cloud Firebase More from Google Think with Google Business Solutions Google Workspace PrivacyTermsAbout GoogleGoogle Products Help
From user nestieguilas
Marketing-for-Engineers, Prepared, cleaned, and explored large-scale datasets to engineer features and build a Propensity Purchase Model with Machine Learning for a smartphone company looking to personalize its marketing efforts across user segments.
From user nglan0409
Marketing-for-Engineers, Practical glossary for startup founders and engineers wondering about marketing
From user nicoallgood
Marketing-for-Engineers, An e-commerce website for marketing the products which are developed by engineers using innovative ideas
From user pawansenapati
Marketing-for-Engineers, A group project for marketing research in which a customer survey was engineered in order to address important questions for a local business
From user pcm19b
Marketing-for-Engineers, Rana Moneeb is a web engineer, digital marketing expert and has an extensive understanding of what it takes for a business to completely crush it online. His previous work experience includes author roles in The Hustler's Digest.
From organization rana-moneeb-foundation
Marketing-for-Engineers, Being a part of the data engineering team, are expected to “Develop input features” for the efficient marketing model given the Visitor log data and User Data. As a Data Engineer Creating ETL Pipeline is expected.
From user saikat-kolkata
Marketing-for-Engineers, Assessment for a Quality Engineer on the Red Hat Marketing Operation Date Engineering team
From user westonagreene
Marketing-for-Engineers, Hugo Theme for The Marketing Engineers
From user zebbus
Marketing-for-Engineers, Our team goes above and beyond. Our unique psychology driven approach allows for us to garner results that stand out in today’s crowded market place. Zeus Marketing leverages top psychologists, engineers and leaders in the field of persuasion psychology to produce a ROI for our clients.
From user zeusmarketing
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
Django
The Web framework for perfectionists with deadlines.
Laravel
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
Microsoft
Open source projects and samples from Microsoft.
Google
Google ❤️ Open Source for everyone.
Alibaba
Alibaba Open Source for everyone
D3
Data-Driven Documents codes.
Tencent
China tencent open source team.
