Tag: Deep Learning

Feed: Neo4j Graph Database Platform. Author: Jocelyn Hoppa. Editor’s Note: This is a transcription of a presentation given by Hilary Mason at GraphConnect New York in October 2018.Presentation SummaryThe metaphors we use to describe the world directly correspond to the way in which we build technology, particularly in the areas of artificial intelligence and machine learning. Take the example of the technology developed around the U.S. Census, which was based on the way train conductors counted passengers. This simple technology ultimately became the basis for modern computing.One of the challenges facing AI today is the general lack of understanding surrounding ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: William Vorhies. Summary:  Business doesn’t want AI.  Business wants results.  While we were focused inward on our Advanced Analytic Platforms, smart competitors were rolling up AI/ML with other capabilities into “Intelligent Automation” platforms.  This large scale integration of capabilities of which AI/ML is only a part looks a lot like the development of ERPs in the late 90s. Business doesn’t want AI.  Business wants results: increased revenue, increased margin, and decreased cost.  If you’re part of the AI/ML community it’s easy to get wrapped up in how AI/ML can have these ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Divya Singh. Machine Learning has seen a tremendous rise in the last decade, and one of its sub-fields which has contributed largely to its growth is Deep Learning. The large volumes of data and the huge computation power that modern system possess has given Data Scientist, Machine Learning Engineers, and others to achieve ground-breaking results in the Deep Learning and continue to bring in new developments in this field. In this blog post, we would cover the deep learning data sets that you could work with as a Data Scientist but ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Vincent Granville. This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, decision trees, ensembles, correlation, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, cross-validation, model fitting, and many more. To keep receiving these articles, sign up on DSC. 29 Statistical Concepts Explained in Simple English Self-Selection Bias Semantic Differential Scale: Definition, Examples Semi Interquartile Range / Quartile Deviation Sensitivity vs Specificity and Predictive Value Sequential Sampling: Definition, Advantages/Disadvantages Serial Correlation / Autocorrelation: Definition, Tests Shapes of Distributions: Definitions, Examples ... Read More

Feed: R-bloggers. Author: Adrian Antico. Over 20% of Amazon’s North American retail revenue can be attributed to customers who first tried to buy the product at a local store but found it out-of-stock, according to IHL group (a global research and advisory firm specializing in technologies for retail and hospitality. In other words, many companies and local stores suck at forecasting. Accurate demand forecasts are necessary if you’re a retailer who has one of their competitors being Amazon. Want to lose business to Amazon? Then produce sh**ty demand forecasts. It is also actually one of the “low-hanging fruits” of a ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Vincent Granville. Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week. To subscribe, follow this link.   Featured Resources and Technical Contributions  Free Book: Statistics: New Foundations, Toolbox, and ML Recipes  30 Statistical Concepts Explained in Simple English - Part 15  Python Software for Clustering  Deep learning Data Sets for Every Data Scientist  The Divergence Index: A new polarization measure for ordinal categorical variables Comparing Classifiers: Decision Trees, K-NN & Naive Bayes  Unsupervised, k-means Clustering and ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Vincent Granville. This book is intended for busy professionals working with data of any kind: engineers, BI analysts, statisticians, operations research, AI and machine learning professionals, economists, data scientists, biologists, and quants, ranging from beginners to executives. In about 300 pages and 28 chapters it covers many new topics, offering a fresh perspective on the subject, including rules of thumb and recipes that are easy to automate or integrate in black-box systems, as well as new model-free, data-driven foundations to statistical science and predictive analytics. The approach focuses on robust techniques; ... Read More

Feed: All - O'Reilly Media. Author: O'Reilly Media. Student (source: Pixabay) Learn new topics and refine your skills with more than 219 new live online training courses we opened up for June and July on the O'Reilly online learning platform. AI and machine learning AI-driven Future State Cloud Operations, June 7 Deep Learning with PyTorch, June 20 Deep Learning from Scratch, July 2 Introduction to Reinforcement Learning, July 8 Fundamentals of Machine Learning and Data Analytics, July 10-11 Essential Machine Learning and Exploratory Data Analysis with Python and Jupyter Notebook, July 11-12 Artificial Intelligence: An Overview of AI and Machine ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Mark Cramer. How product managers can learn what they need to know about AI to be at peak effectiveness The most frequent question I get about AI from colleagues, product managers and others, is, "What do I need to know about AI and what's the best way to learn it?" I've invested a considerable amount of time taking numerous courses, so I dug into my emails to collect some of the suggestions I've doled out. Is this necessary? First, it's worth addressing the extent to which a product manager even needs ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Andrea Manero-Bastin. This article was written by David March.  Data science has been around since mankind first did experiments and recorded data. It is only since the advent of big and heterogenous data that the term “Data Science” was coined. With such a long and varied history, the field should benefit from the great diversity of perspectives that are brought by practitioners from different fields. My own path started with signal analysis. I was building high speed interferometric photon counting systems, where my “data science” was dominated by signal to noise ... Read More