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Collection of colors: &#10005dist = LearnDistribution[{RGBColor[0.5172966964096541, 0.4435322033449375, 1.], RGBColor[0.3984626930847484, 0.5592892024442906, 1.], RGBColor[0.6149389612362844, 0.5648721294502163, 1.], RGBColor[0.4129156497559272, 0.9146065592632544, 1.], RGBColor[0.7907065846445507, 0.41054133291260947`, 1.], RGBColor[0.4878854162550912, 0.9281119680196579, 1.], RGBColor[0.9884362181280959, 0.49025178842859785`, 1.], RGBColor[0.633242503827218, 0.9880985331612835, 1.], RGBColor[0.9215182482568276, 0.8103084921468551, 1.], RGBColor[0.667469513641223, 0.46420827644204676`, 1.]}]Once we have this “learned distribution”, we can do all sorts of things with it. For example, this generates 20 random samples from it: &#10005RandomVariate[dist,20]But now think about FindAnomalies. What it has to do is to find out which data points are anomalous relative to what’s expected. Or, in other words, given the underlying distribution of the data, it finds what data points are outliers, in the sense that they should occur only with very low probability according to the distribution.And just like for an ordinary numerical distribution, we can compute the PDF for a particular piece of data. Purple is pretty likely given the distribution of colors we’ve learned from our examples: &#10005PDF[dist, RGBColor[ 0.6323870562875563, 0.3525878887878987, 1.0002083564175581`]]But red is really really unlikely: &#10005PDF[dist, RGBColor[1, 0, 0]]For ordinary numerical distributions, there are concepts like CDF that tell us cumulative probabilities, say that we’ll get results that are “further out” than a particular value. For spaces of arbitrary things, there isn’t really a notion of “further out”. But we’ve come up with a function we call RarerProbability, that tells us what the total probability is of generating an example with a smaller PDF than something we give: &#10005RarerProbability[dist, RGBColor[ 0.6323870562875563, 0.3525878887878987, 1.0002083564175581`]] &#10005RarerProbability[dist, RGBColor[1, 0, 0]]Now we’ve got a way to describe anomalies: they’re just data points that have a very small rarer probability. And in fact FindAnomalies has an option AcceptanceThreshold (with default value 0.001) that specifies what should count as “very small”. OK, but let’s see this work on something more complicated than colors. Let’s train an anomaly detector by looking at 1000 examples of handwritten digits: &#10005AnomalyDetection[RandomSample[ResourceData["MNIST"][[All,1]],1000]]Now FindAnomalies can tell us which examples are anomalous: ✕FindAnomalies[AnomalyDetection[RandomSample[ResourceData["MNIST"][[All,1]],1000]], {\!\(\*GraphicsBox[TagBox[RasterBox[CompressedData["1:eJxTTMoPSmNiYGAo5gASQYnljkVFiZXBAkBOaF5xZnpeaopnXklqemqRRRJImQwU/x+84O9URsb6P1ilPk1jAoLzWOUymJiEcchNY2Srm80kcAObHC9z1/8wJm9sUh0sWf+/2DItxyJ1T5Cp9f8tJqbDWOTmMgHlinDK8UpyMVn+xCL3K4iJEei7TdicAgT2jIyFOKT+5zGJ38YhtYiRtR6H1CtuRkNcJlozMa/BIfVYiMkAh9QjAyatF9gkrqo2GjDpPMeq6RzQ0zrPsBv4NI4p+AcuN1ITAABxtMfa"], {{0, 28}, {28, 0}}, {0, 255},ColorFunction->GrayLevel],BoxForm`ImageTag[ "Byte", ColorSpace -> Automatic, Interleaving -> None],Selectable->False],DefaultBaseStyle->"ImageGraphics",ImageSizeRaw->{28, 28},PlotRange->{{0, 28}, {0, 28}}]\), \!\(\*GraphicsBox[TagBox[RasterBox[CompressedData["1:eJxTTMoPSmNiYGAo5gASQYnljkVFiZXBAkBOaF5xZnpeaopnXklqemqRRRJImQwU/x/kgJGJcTUOqV4mFqY12KWKmBiZZI9jlwPqYsEu9ciKgYnRGrsuK6Au68e4dDEw4dbFVIpdFyNeu7D77NEqoC6mXhLt+n8Mt79C5XGGYhhuf4F14bALt7+OyeMKw///LYH+wi7z//9jayYWXHLUBgCB+cHS"], {{0, 28}, {28, 0}}, {0, 255},ColorFunction->GrayLevel],BoxForm`ImageTag[ "Byte", ColorSpace -> Automatic, Interleaving -> None],Selectable->False],DefaultBaseStyle->"ImageGraphics",ImageSizeRaw->{28, 28},PlotRange->{{0, 28}, {0, 28}}]\), \!\(\*GraphicsBox[TagBox[RasterBox[CompressedData["1:eJxTTMoPSmNiYGAo5gASQYnljkVFiZXBAkBOaF5xZnpeaopnXklqemqRRRJImQwU/x/M4I7MI1SBfL2vMOYpxsuocqGMd2DMLehyzoy9MKYTulwWYwuMKY0pdxanXCJCQJrtBqqcOWMXlPVLUhdVCmim76qdm+fNu76wktHr27dvyHLtjChAGFnuZbkTI6NiQIB/ABvDhOXn0Ez9+/37LxAtJPDsPy4gZIZT6gZnC065HYyncMr1IQIWAyQy+q/ELSd7FbfcBNxmir/DKUcVAADomc0b"], {{0, 28}, {28, 0}}, { 0, 255},ColorFunction->GrayLevel],BoxForm`ImageTag[ "Byte", ColorSpace -> Automatic, Interleaving -> None],Selectable->False],DefaultBaseStyle->"ImageGraphics",ImageSizeRaw->{28, 28},PlotRange->{{0, 28}, {0, 28}}]\), \!\(\*GraphicsBox[TagBox[RasterBox[CompressedData["1:eJxTTMoPSmNiYGAo5gASQYnljkVFiZXBAkBOaF5xZnpeaopnXklqemqRRRJImQwUDwZwxxCnVOZfnHJ8T75rYAiKC4Cp2n+rMKRcXm8E099+GaNLsW/7OQ9E2/9ZhqFtwT8HEMV07J8RulT7v1WMINru3xsBNKmw339cQDTroX/FaFLSD/9NBzOU/n1gR5Mz+vdeBESz7P2XwsAujSIn+/zfxZychqO9//7dOHoRzX8xf/5BwN9fi/250ExVCwWC4n8//TE8BwW6/97ikmJI+HcLl5Tc43+TcUhxrP33uxmHnO+/P6W4jDz4bxNOl9AFAAAYpls0"], {{0, 28}, {28, 0}}, {0, 255},ColorFunction->GrayLevel],BoxForm`ImageTag[ "Byte", ColorSpace -> Automatic, Interleaving -> None],Selectable->False],DefaultBaseStyle->"ImageGraphics",ImageSizeRaw->{28, 28},PlotRange->{{0, 28}, {0, 28}}]\), \!\(\*GraphicsBox[TagBox[RasterBox[CompressedData["1:eJxTTMoPSmNiYGAo5gASQYnljkVFiZXBAkBOaF5xZnpeaopnXklqemqRRRJImQwU/x8UgKEep5QDbrn9DAz7SdC2vx6uDYtyGO2AKQU1CtO2/bAAxLStHqYa05FAKTBwwLRtPwMSwHQHg0M9RDu6bRAZCAPd/fX1cLPRtSGZjaENydr9uOTwpR88cvuxuBJJDqd19AAAMwi/NQ=="], {{0, 28}, {28, 0}}, {0, 255},ColorFunction->GrayLevel],BoxForm`ImageTag[ "Byte", ColorSpace -> Automatic, Interleaving -> None],Selectable->False],DefaultBaseStyle->"ImageGraphics",ImageSizeRaw->{28, 28},PlotRange->{{0, 28}, {0, 28}}]\), \!\(\*GraphicsBox[TagBox[RasterBox[CompressedData["1:eJxTTMoPSmNiYGAo5gASQYnljkVFiZXBAkBOaF5xZnpeaopnXklqemqRRRJImQwU/x+M4FkLV9+VK1fmZ2czqn5GlbotwwwETBDiOKrcPCYkuW1oZvZJOMWuBgJrZiaJ92hyPx6DqSkCzMInsTrokAwTk/AybDLH6oH2WWPR9emYPCczE1fjDwyZ9/tVwU6Uj9//BU3qoBIz3A+qaPbVMzExMjHJNU8p0hFgYij9jSy3Sl4t48CBVyDm1UIm5lcoGj8ignAquhwCXHHFDBeYq3CFy9srSUxMTJjhcvbYxn51kB+CMKSmcHGygPwnf/wzhpwbSIts8GrMIAO6gUktp+05DrdTEQAAo1CVcQ=="], {{0, 28}, {28, 0}}, {0, 255},ColorFunction->GrayLevel],BoxForm`ImageTag[ "Byte", ColorSpace -> Automatic, Interleaving -> None],Selectable->False],DefaultBaseStyle->"ImageGraphics",ImageSizeRaw->{28, 28},PlotRange->{{0, 28}, {0, 28}}]\), \!\(\*GraphicsBox[TagBox[RasterBox[CompressedData["1:eJxTTMoPSmNiYGAo5gASQYnljkVFiZXBAkBOaF5xZnpeaopnXklqemqRRRJImQwU/x/s4PcnHBKvNzqZCNX8wRD/8nmfPx8jEAjFo0udtVcAinNouWpp5aPLWTIysoauvIvNpq0cIhpJ2B1xgpvR7zsOKWFGRhxufyLEyLj5H1apz9ZAF0rkfsP01/8f9YwQEP0VTeZGqTojo+Xmfd0yjIy6P1HlUhk52yc+BrG6uTnPosolMDKKN4K93MDPuA1V7m0qMBA5tUr0tRgZ1dAt/L/SGOIUBqU3mA79sn9KhrOzc+0HrD4c3AAAH4+4UQ=="], {{0, 28}, {28, 0}}, {0, 255},ColorFunction->GrayLevel],BoxForm`ImageTag[ "Byte", ColorSpace -> Automatic, Interleaving -> None],Selectable->False],DefaultBaseStyle->"ImageGraphics",ImageSizeRaw->{28, 28},PlotRange->{{0, 28}, {0, 28}}]\), \!\(\*GraphicsBox[TagBox[RasterBox[CompressedData["1:eJxTTMoPSmNiYGAo5gASQYnljkVFiZXBAkBOaF5xZnpeaopnXklqemqRRRJImQwU/x+UoCeKkZGBkZGx4hmmHDsTFBg/wi3H1I0h186hbwkErExM2piGbnsPJN7xYpUDgr9vzUAWYkpcqK3NB9u3BUMqghXqlKq/6FKdMFfqY/qvFu4Fz1foclOYmTSNjAyx27ei/Or//3+WcaHJ/UZi8qHKnbdtug6TckEzcx4Tk+xVMOuzJ1DKFsmY/3v5gZLX/v/fPUkeKCV7AsUZU4FCchYWPCBHStehOnG/EsxvzNpn0N3/ygEiJbcIw2v//7/v7nYUKOq+hkWKugAABiF8Xw=="], {{0, 28}, {28, 0}}, { 0, 255},ColorFunction->GrayLevel],BoxForm`ImageTag[ "Byte", ColorSpace ->

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And in every version since then we’ve added all sorts of state-of-the-art features. In June 2018 we introduced our Neural Net Repository to make it easy to access the latest neural net models from the Wolfram Language—and already there are nearly 100 curated models of many different types in the repository, with new ones being added all the time.So if you need the latest BERT “transformer” neural network (that was added today!), you can get it from NetModel: &#10005NetModel["BERT Trained on BookCorpus and English Wikipedia Data"]You can open this up and see the network that’s involved (and, yes, we’ve updated the display of net graphs for Version 12.0):And you can immediately use the network, here to produce some kind of “meaning features” array: &#10005NetModel["BERT Trained on BookCorpus and English Wikipedia Data"]["What a wonderful network!"] // MatrixPlotIn Version 12.0 we’ve introduced several new layer types—notably AttentionLayer, which lets one set up the latest “transformer” architectures—and we’ve enhanced our “neural net functional programming” capabilities, with things like NetMapThreadOperator, and multiple-sequence NetFoldOperator. In addition to these “inside-the-net” enhancements, Version 12.0 adds all sorts of new NetEncoder and NetDecoder cases, such as BPE tokenization for text in hundreds of languages, and the ability to include custom functions for getting data into and out of neural nets.But some of the most important enhancements in Version 12.0 are more infrastructural. NetTrain now supports multi-GPU training, as well as dealing with mixed-precision arithmetic, and flexible early-stopping criteria. We’re continuing to use the popular MXNet low-level neural net framework (to which we’ve been major contributors)—so we can take advantage of the latest hardware optimizations. 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To your Facebook page. It also helps in building a community as the visitors see others engaging with you on social media. 4. Twitter FeedBring all the witty tweets by your audience to your website with the Twitter Feed.Adding a twitter feed makes your website dynamic and visually pleasing. This can also increase traffic to your twitter account. 5. Pinterest FeedPeople go to Pinterest to get inspiration, and users keep posting captivating content. We see a constant flow of content each time we refresh our Pinterest feed. Bring this visually appealing content to your website, and it will change the entire look and feel and give a new look to your web page.Embedding Pinterest on your website allows you to display your brands interests in the form of Pinterest activities and pins. Sharing visual content can also increase the time spent by a user on your website.6. TikTok VideosAdding TikTok videos with user-generated content on your webpages can serve as social proof to promote products and services off your business, especially if you have a younger generation.Embed Social Media Widget On Your Website Automatically!Take 14-Days Free TrialSignup NowType of Content You Can Embed From Social Media FeedsUser-generated content – You can collect UGC from social media platforms on your website to showcase product used by the real life users.Hashtag: You can fetch hashtag content using social media aggregator and embed hashtag feed right on website. Mention: When you are a brand active on social media, you likely get mentioned now and then. So why not show that off on your website by curating and embedding a social media mentions feed. Handle: You can embed social media content from specific user handles or accounts. This is excellent for showcasing a diverse range of brand-related contentVideos: Embedding videos from social media platforms is a powerful way to engage your website visitors with multimedia content. You can embed videos from platforms like YouTube, Instagram, or TikTok directly onto your website. Take 14-days free trial and increase visual content & decrease bounce rate on website!Step:1 Tagbox account: Sign-up with Tagbox in case you are

Feeds on your WordPress website:Log in to your WordPress website.Select and edit the web page where you wish to display the social feeds gallery. Now. Choose the ‘+’ button, select the custom HTML option, paste your embed code and apply changes to display the social media feeds gallery on the WordPress website.Currently, Wix serves its services to over 110 million users in 190 countries. Adding social media content is easy; you just need to follow these simple steps:After logging into Wix, you will see a ‘+’ button on the left-hand side of the screen in the menu bar; you can add elements to your web pages through this button.After clicking on the plus button, you will come across the complete list of elements.Click ‘More’ on the menu and select HTML iframe from the Embeds.Now enter the social post embed code in the code field, and then click ‘Apply.Weebly allows everyone to create a high-quality website with more than 40 million entrepreneurs using Weebly to grow their businesses.Here is how you can do it effortlessly:Drag and drop your elements on Weebly to create your web pages. In the menu on the left-hand side of your screen, find the “Embed Code element”. Now drag and drop it onto your page where you want to embed a social media feedWhen you Drag and Drop the Embed code, click on the HTML box and choose ‘Edit Custom HTML.’Paste the HTML code to embed Tagbox Social media feed on Weebly Website.Embed Social Media widget automatically with the social media aggregator and UGC platform by Tagbox. Click to try it for freeGoogle Sites is a website builder containing basic development features. You can use it to create a blog, portfolio or an intranet website for your brand. It is very easy to embed a social media feed on a webpage made by Google sites.Following are the steps you must follow to do so:Log in to your Google Site account.Open the website where you want to add the social media feed.Click on the embed button on the site.Click on the “Embed Code Tab” on the pop- up