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Data Science & Machine Learning Newsletter

Data Science & Machine Learning Newsletter #105

Do you want to get updates? Please join Data Sci­ence & Machine Learn­ing Newslet­ter Linked Group Effi­cient Neural Archi­tec­ture Search via Para­me­ter Shar­ing “We pro­pose Effi­cient Neural Archi­tec­ture Search (ENAS), a fast and inex­pen­sive approach for auto­matic model design. In ENAS, a con­troller learns to dis­cover neural net­work archi­tec­tures by search­ing for an opti­mal sub­graph within a large com­pu­ta­tional graph.” SMASH: One-Shot Model Archi­tec­ture Search through Hyper­Net­works “We Devamını Oku […]

Data Science & Machine Learning Newsletter #104

Do you want to get updates? Please join Data Sci­ence & Machine Learn­ing Newslet­ter Linked Group Visu­al­iz­ing Incom­plete and Miss­ing Data ”… But a lot of the time (most of the time?), the data you work with is not com­plete. There is miss­ing data. … What do you do when this hap­pens? … Here are some solu­tions to get you headed in the right direc­tion.” A Gen­tle Intro­duc­tion to Vec­tors for Machine Learn­ing “Vec­tors are a foun­da­tional ele­ment of lin­ear alge­bra. Vec­tors Devamını Oku […]

Data Science & Machine Learning Newsletter #103

Do you want to get updates? Please join Data Sci­ence & Machine Learn­ing Newslet­ter Linked Group Mul­ti­ple impu­ta­tion util­is­ing denois­ing autoen­coder for approx­i­mate Bayesian infer­ence “Miss­ing data is a wide­spread prob­lem in machine learn­ing. Bayesian infer­ence is a robust solu­tion to imput­ing miss­ing val­ues, par­tic­u­larly if mul­ti­ple impu­ta­tions are used to model the uncer­tainty regard­ing said val­ues.” Alibaba’s AI Out­guns Humans in Read­ing Test Alibaba has devel­oped Devamını Oku […]

Data Science & Machine Learning Newsletter #102

Do you want to get updates? Please join Data Sci­ence & Machine Learn­ing Newslet­ter Linked Group Gra­di­ent Boost­ing explained “Gra­di­ent boost­ing (GB) is a machine learn­ing algo­rithm devel­oped in the late ‘90s that is still very pop­u­lar. It pro­duces state-of-the-art results for many com­mer­cial (and aca­d­e­mic) appli­ca­tions. This page explains how the gra­di­ent boost­ing algo­rithm works using sev­eral inter­ac­tive visu­al­iza­tions.” AI and Deep Learn­ing in 2017 – A Year in Review Text Devamını Oku […]

Data Science & Machine Learning Newsletter #101

Do you want to get updates? Please join Data Sci­ence & Machine Learn­ing Newslet­ter Linked Group Python mod­ule to per­form under sam­pling and over sam­pling with var­i­ous tech­niques “imbalanced-learn is a python pack­age offer­ing a num­ber of re-sampling tech­niques com­monly used in datasets show­ing strong between-class imbal­ance. It is com­pat­i­ble with scikit-learn and is part of scikit-learn-contrib projects.” On Machine Learn­ing and Pro­gram­ming Lan­guages “While machine Devamını Oku […]

Data Science & Machine Learning Newsletter #100

Do you want to get updates? Please join Data Sci­ence & Machine Learn­ing Newslet­ter Linked Group The 10 Sta­tis­ti­cal Tech­niques Data Sci­en­tists Need to Mas­ter “data sci­en­tist is a per­son who is bet­ter at sta­tis­tics than any pro­gram­mer and bet­ter at pro­gram­ming than any sta­tis­ti­cian.” Unleash a faster Python on your data “Get real per­for­mance results and down­load the free Intel® Dis­tri­b­u­tion for Python that includes every­thing you need for blazing-fast com­put­ing, Devamını Oku […]

Data Science & Machine Learning Newsletter # 99

You want to get updates? Please join Data Sci­ence & Machine Learn­ing Newslet­ter Linked Group Ulti­mate Guide To Sta­tis­ti­cal Sig­nif­i­cance Tests in R Imple­ment and inter­pret the com­monly used sta­tis­ti­cal sig­nif­i­cance tests in R, the pur­pose, when to use and how to inter­pret the result. Word2Vec (skip-gram model) The skip-gram neural net­work model is actu­ally sur­pris­ingly sim­ple in its most basic form. Train a sim­ple neural net­work with a sin­gle hid­den layer to per­form Devamını Oku […]

Data Science & Machine Learning Newsletter # 98

You want to get updates? Please join Data Sci­ence & Machine Learn­ing Newslet­ter Linked Group CAN (Cre­ative Adver­sar­ial Net­work) - Explained GANs (Gen­er­a­tive Adver­sar­ial Net­works), a type of Deep Learn­ing net­works, have been very suc­cess­ful in cre­at­ing non-procedural con­tent. This work explores the pos­si­bil­ity of machine gen­er­ated cre­ative con­tent. 109 Com­monly Asked Data Sci­ence Inter­view Ques­tions For a data sci­ence inter­view, an inter­viewer will ask ques­tions Devamını Oku […]

Data Science & Machine Learning Newsletter # 97

You want to get updates? Please join Data Sci­ence & Machine Learn­ing Newslet­ter Linked Group How to Apply Machine Learn­ing to Event Pro­cess­ing How do you com­bine his­tor­i­cal Big Data with machine learn­ing for real-time ana­lyt­ics? An approach is out­lined with dif­fer­ent soft­ware ven­dors, busi­ness use cases, and best prac­tices. J.P.Morgan’s mas­sive guide to machine learn­ing and big data jobs in finance J.P. Morgan’s quan­ti­ta­tive invest­ing and deriv­a­tives strat­egy team, Devamını Oku […]

Data Science & Machine Learning Newsletter # 96

You want to get updates? Please join Data Sci­ence & Machine Learn­ing Newslet­ter Linked Group Python Plot­ting for Exploratory Analy­sis What method can be used to detect sea­son­al­ity in data? A really good way to find peri­od­ic­ity in any reg­u­lar series of data is to inspect its power spec­trum after remov­ing any over­all trend. Word2vec with tens of bil­lions of items, what could pos­si­bly go wrong? Project related to text sum­ma­riza­tion on Github: https://github.com/miso-belica/sumy https://github.com/abisee/pointer-generator https://github.com/LazoCoder/Article-Summarizer https://github.com/hengluchang/newsum https://github.com/sriniiyer/codenn https://github.com/davidadamojr/TextRank An Devamını Oku […]