For Deep Learning & Machine Learning engineers out of all the probabilistic models in the world, Gaussian distribution model simply stands out. Even if you have never worked on an AI project, there is a significant chance that you have come across the Gaussian model.Gaussian distribution model, often identified with its iconic bell shaped curve, also referred as Normal distribution, is so popular mainly because of three reasons.Incredible number of processes in nature and social sciences naturally follows the Gaussian distribution. Even when they don’t, the Gaussian gives the best model approximation for these processes. Some examples include-Central limit theorem states that when we add large number of independent random variables, irrespective of the original distribution of these variables, their normalized sum tends towards a Gaussian distribution. For example, the distribution of total distance covered in an random walk tends towards a Gaussian probability distribution.The theorem’s implications include that large number of scientific and statistical methods that have been developed specifically for Gaussian models can also be applied to wide range of problems that may involve any other types of distributions.The theorem can also been seen as a explanation why many natural phenomena follow Gaussian distribution.Unlike many other distribution that changes their nature on transformation, a Gaussian tends to remain a Gaussian.For every Gaussian model approximation, there may exist a complex multi-parameter distribution that gives better approximation. But still Gaussian is preferred because it makes the math a lot simpler!Gaussian distribution is named after great mathematician and physicist Carl Friedrich Gauss.From a quick cheer to a standing ovation, clap to show how much you enjoyed this story.Finding equilibria among AI, philosophy, and economics.Sharing concepts, ideas, and codes.
It’s very common for machine learning practitioners to have favorite algorithms. It’s a bit irrational, since no algorithm strictly dominates in all applications, the performance of ML algorithms varies wildly depending on the application and the dimensionality of the dataset. And even for a given problem and a given dataset, any single model will likely be beaten by an ensemble of diverse models trained by diverse algorithms anyway. But people have favorites nevertheless. Some like SVMs for the elegance of their formulation or the quality of the available implementations, some like decision rules for their simplicity and interpretability, and some are crazy about neural networks for their flexibility.My favorite out-of-the-box algorithm is (as you might have guessed) the Random Forest, and it’s the second modeling technique I typically try on any given data set (after a linear model).This beautiful visualization from scikit-learn illustrates the modelling capacity of a decision forest:Here’s a paper by Leo Breiman, the inventor of the algorithms describing random forests.Here’s another amazing paper by Rich Caruana et al. evaluating several supervised learning algorithms on many different datasets.From a quick cheer to a standing ovation, clap to show how much you enjoyed this story.Relevance engineer. Machine Learning practitioner and hobbyist. Former entrepreneur.Rants about machine learning and its future
O Grupo de Estudo em Deep Learning de Brasília está planejando o próximo ciclo de encontros do grupo, que deve iniciar-se a partir do meio de junho de 2018. Ainda há tempo para manifestar suas preferências para participar do grupo!Para tal, favor preencher o seguinte questionário para que possamos agregar as preferências de nossa comunidade e selecionar as opções que melhor atenderem a todos: https://goo.gl/forms/H4K77sD1DxW6diIt1Agradecemos se puder divulgar o grupo junto a sua rede de contatos com interesse nos temas de aprendizado automático e Deep Learning, para que possamos iniciar o próximo ciclo já com o máximo de interessados desde o primeiro dia!Seguem abaixo alguns dos resultados iniciais do grupo:Quanto aos resultados inciais do questionário, segue uma síntese das primeiras 50 respostas:Dentre os tópicos de mais interesse destacam-se:1o Deep Learning: 87,5%2o Machine Learning: 78,6%3o Aplicações de Deep Learning em projetos: 69,6%4o Processamento de Linguagem Natural: 51,8%Preferência por curso:1o Machine Learning, da fast.ai: 67,9%2o Deep Learning, parte 2, da fast.ai: 46,4%3o Deep Learning, parte 1, da fast.ai: 44,6%Atenciosamente,Organização do Grupo de Estudo em Deep Learning, de BrasíliaFrom a quick cheer to a standing ovation, clap to show how much you enjoyed this story.Publicações dos membros do Grupo de Estudo em Deep Learning de Brasília
The term data scientist has been used lately to describe a wide variety of skills & roles. In this post I will focus on a particular flavor of data scientist. I will talk about the qualities needed to be a good data scientist-engineer who ships relevance products to users. Some examples of relevance products are:These folks need to be strong at data science and engineering to be successful. Some places call these folks as Machine Learning engineers since most of the work they do involves Machine Learning. More generally, I feel relevance engineer is a good term to describe them.Relevance engineers have a common set of skills that they draw upon to get their jobs done. The list below doesn’t include some of the known, obvious skills. You obviously need to be smart. You obviously need to have (or be able to learn quickly) the required “book” knowledge.But beyond that, there are a bunch of not-so-obvious skills that you can’t learn from a book. Here are some of those, in no particular order:This list is by no means exhaustive, but does capture some of the qualities of the smartest folks I have worked with. Happy to hear what you think.Thanks to Peter Bailey and Andrew Hogue for feedback on the initial revisions.*In this post, feature means a software feature, not a machine learning feature.From a quick cheer to a standing ovation, clap to show how much you enjoyed this story.Engineering Manager doing Machine Learning @ Google. Previously worked on ML and search at Quora, Foursquare and Bing.Sharing concepts, ideas, and codes.
这一阵为了工作上的关系,花了点时间学习了一下LDA算法,说实话,对于我这个学CS而非学数学的人来说,除了集体智慧编程这本书之外基本没怎么看过机器学习的人来说,一开始还真是摸不太到门道,前前后后快要四个月了,算是基本了解了这个算法的实现,记录一下,也供后来人快速入门做个参考。一开始直接就下了Blei的原始的那篇论文来看,但是看了个开头就被Dirichlet分布和几个数学公式打倒,然后因为专心在写项目中的具体的代码,也就先放下了。但是因为发现完全忘记了本科学的概率和统计的内容,只好回头去看大学时候概率论的教材,发现早不知道借给谁了,于是上网买了本,花了几天时间大致回顾了一遍概率论的知识,什么贝叶斯全概率公式,正态分布,二项分布之类的。后来晚上没事儿的时候,去水木的AI版转了转,了解到了Machine Learning的圣经PRML,考虑到反正也是要长期学习了,搞了电子版,同时上淘宝买了个打印胶装的版本。春节里每天晚上看一点儿,扫了一下前两章,再次回顾了一下基本数学知识,然后了解了下贝叶斯学派那种采用共轭先验来建模的方式。于是再次尝试回头去看Blei的那篇论文,发现还是看不太懂,于是又放下了。然后某天Tony让我准备准备给复旦的同学们share一下我们项目中LDA的使用,为了不露怯,又去翻论文,正好看到Science上这篇Topic Models Vs. Unstructured Data的科普性质的文章,翻了一遍之后,再去PRML里看了一遍Graphic Models那一张,觉得对于LDA想解决的问题和方法了解了更清楚了。之后从search engine里搜到这篇文章,然后根据推荐读了一部分的Gibbs Sampling for the Uninitiated。之后忘了怎么又搜到了Mark Steyvers和Tom Griffiths合著的Probabilistic Topic Models,在某个周末往返北京的飞机上读完了,觉得基本上模型训练过程也明白了。再之后就是读了一下这个最简版的LDA Gibbs Sampling的实现,再回过头读了一下PLDA的源码,基本上算是对LDA有了个相对清楚的了解。这样前前后后,也过去了三个月,其实不少时间都是浪费掉的,比如Blei的论文在没有任何相关知识的情况下一开始读了好几次,都没读完而且得到到信息也很有限,如果重新总结一下,我觉得对于我们这些门外汉程序员来说,想了解LDA大概需要这些知识:基本上这样一圈下来,基本概念和算法实现都应该搞定了,当然,数学证明其实没那么容易就搞定,但是对于工程师来说,先把这些搞定就能干活了,这个步骤并不适合各位读博士发论文的同学们,但是这样先看看也比较容易对于这些数学问题的兴趣,不然,成天对这符号和数学公式,没有整块业余时间的我是觉得还是容易退缩放弃的。发现作为工程师来说,还是看代码比较有感觉,看实际应用的实例比较有感觉,看来不能把大部分时间花在PRML上,还是要多对照着代码看。From a quick cheer to a standing ovation, clap to show how much you enjoyed this story.Facebook Messenger & Chatbot, Machine Learning & Big Data生命如此短暂,掌握技艺却要如此长久
Stories are a fundamental human tool that we use to communicate thought. Creating a stories about a image is a difficult task that many struggle with. New machine-learning experiments are enabling us to generate stories based on the content of images. This experiment explores how to generate little romantic stories about images (incl. guest star Taylor Swift).neural-storyteller is a recently published experiment by Ryan Kiros (University of Toronto). It combines recurrent neural networks (RNN), skip-thoughts vectors and other techniques to generate little story about images. Neural-storyteller’s outputs are creative and often comedic. It is open-source.This experiment started by running 5000 randomly selected web-images through neural-storyteller and experimenting with hyper-parameters. neural-storyteller comes with 2 pre-trained models: One trained on 14 million passages of romance novels, the other trained on Taylor Swift Lyrics. Inputs and outputs were manually filtered and recombined into two videos.Using Romantic Novel Model. Voices generated with a Text-to-Speech.Using Taylor Swift Model. Combined with a well known Swift instrumental.neural-storyteller gives us a fascinating glimpse into the future of storytelling. Even though these technologies are not fully mature yet, the art of storytelling is bound to change. In the near future, authors will be training custom models, combining styles across genres and generating text with images & sounds. Exploring this exiting new medium is rewarding!From a quick cheer to a standing ovation, clap to show how much you enjoyed this story.Designer & Code Magician. Working at the intersection of HCI, Machine Learning & Creativity. Building tools for Enlightenment. Narrative Engineering.
The number of meetings that I scheduled in 2012 might seem astronomical. Put in context, it’s less so. I was a startup-founder at the time, and that year my company, Visual Revenue, took Series-A funding, doubled revenue, and started discussing a possible exit.I like the number though! As a startup romantic one could turn it into a nifty Malcolm Gladwell type rule of thumb called the “1,000 meetings rule.” Gladwell’s claim that greatness requires an enormous time sacrifice rings true to me — whether that means investing 10,000 hours into a subject matter to become an expert or conducting a 1,000 meetings per year, is another question.More interesting though is the impact of this 1,019 figure, and a related one: Of those more than one thousand meetings I scheduled, 672 were rescheduled. That was painful. But these numbers were among the early pieces of data that inspired me to start x.ai.* A meeting is defined as an event in my calendar, which is marginally flawed in both directions, given some events would be “Travel to JFK”, which is obviously a task and not a meeting, where others would be “Interview Sales Director Candidates“, which is really 4 meetings in 1.Originally published at x.ai on October 14, 2013.From a quick cheer to a standing ovation, clap to show how much you enjoyed this story.Magically schedule meetings