machine learning baby names

Machine Learning 3 label can be forecasted for a certain example of input data, for example, provided an example, classify it as spam or not (Milosevic and Choo 2017 pg266). In this case, my model had a precision of 65.7% and a recall of 2%. What if a computer program could find the ideal baby name. Sigmoid Activation and Binary Crossentropy — A Less Than Perfect Match? Following the great minds of machine learning can help you discover new things and deepen your knowledge. I figured I would find a bunch of descriptions of people (biographies), block out their names, and build a model that would predict what those (blocked out) names should be. Given a bio, the model will return a set of names, sorted by probability: So in theory I should’ve been a Linda, but at this point, I’m really quite attached to Dale. Bangalore, Karnataka, India About Blog This is a technical blog, to share, encourage and educate everyone to learn new technologies. : My child will be born in New Jersey. My goal was not to build a model that with 100% accuracy could predict a person’s name. On the contrary, I wanted to be named Sailor Moon. Well, it seems the model did learn traditional gender roles when it comes to profession, the only surprise (to me, at least) that “parent” was predicted to have a male name (“Jose”) rather than a female one. But still — wouldn’t it be cool to have the first baby named by an AI? How to name your baby using machine learning 2 months ago . Probably there isn’t, and this is about as scientific as horoscopes. Machine learning is a booming field in computer science. Let’s see if I’m right: “They will be a computer programmer.” — Joseph, “They will be an astronaut.” — Raymond, “They will be a novelist.” — Robert. If you expect a tonne of intricate math, read along. The model labeled Alecs as “alexander” 25% of the time, but by my read, “alec” and “alexander” are awfully close names. Source Code: Emojify Project 4. People tend to assume that ML means machines teaching themselves – but really, ML means machines learning from people. Below we are narrating the 20 best machine learning datasets such a way that you can download the dataset and can develop your machine learning project. There were lots of different ways I could have done this (here’s one example in Tensorflow), but I opted to use AutoML Natural Language, a code-free way to build deep neural networks that analyze text. Deep learning neural networks have shown promising results in problems related to vision, speech and text with varying degrees of success. Maybe it’s a perfect combination of both parents’ names—or maybe it’s a name that’s completely unique. In this post, I’ll show you how I used machine learning to build a baby name generator (or predictor, more accurately) that takes a description of a (future) human and returns a name, i.e. Facial-recognition algorithms are trained to convert images of faces into face embeddings—sequences of say, 16 numbers, which can be compared to find similar faces. Once I had my data sample, I decided to train a model that, given the text of the first paragraph of a Wikipedia biography, would predict the name of the person that bio was about. Finally, I thought I’d test for one last thing. Project idea – The idea behind this ML project is to build a model that will classify how much loan the user can take. He is the creator of the revolutionary “Pocket Sand” defense mechanism, an exterminator, bounty hunter, owner of Daletech, chain smoker, gun fanatic, and paranoid believer of almost all conspiracy theories and urban legends. I wouldn’t want to leave that responsibility to taste or chance or trends. I just finished Exercise-4 of Dr Andrew Ng's most excellent Machine Learning course. But the process of learning can be very onerous, depending on the approach. What can Wikipedia biographies and Deep Neural Networks tell us about what’s in a name? These datasets can either be curated or generated in real time. 20 Best Machine Learning Datasets For developing a machine learning and data science project its important to gather relevant data and create a noise-free and feature enriched dataset. My network took 10-character names as input (shorter names were padded with a special character), ran an LSTM over them, and generated a vector of 64 floating-point numbers that roughly fit a gaussian distribution. Although I wanted to create a name generator, what I really ended up building was a name predictor. Women with androgynous names are potentially more successful. The model seemed especially bad at understanding what names are popular in Asian countries, and tended in those cases just to return the same small set of names (i.e. Because I didn’t want my model to be able to “cheat,” I replaced all instances of the person’s first and last name with a blank line: “___”. Here’s a tiny corner of it (cut off because I had sooo many names in the dataset): So for example, take a look at the row labeled “ahmad.” You’ll see a light blue box labeled “13%”. My past work included research on NLP, Image and Video Processing, Human Computer Interaction and I developed several algorithms in this area while … Once I prepared my dataset, I set out to build a deep learning language model. The point is to use a metric to evaluate, for each line of the corpus data, which location is most likely to be quoted. We live in the future. Word-embedding networksturn words into vectors of numbers whose values map to their semantic meaning in interesting ways. cv_split_column_names was introduced in version 1.6.0. Before you start reading the code, I want to share a little bit about Supervised Learning. Machine Learning is a really common AI technology. 09/30/2020; 12 minutes to read +4; In this article. I just wanted to build a model that understood something about names and how they work. Synonyms for machine learning include artificial intelligence, robotics, AI, development of 'thinking' computer systems, expert system, expert systems, intelligent retrieval, knowledge engineering, natural language processing and neural network. Conclusion: Machine learning in ecommerce is here to stay. 1. What follows is a study of applying machine learning to achieve semblance of human-like logic and semantics for alternative name identification. : My child will be born in New Jersey. Choosing the perfect name for your baby can be fun! To their credit, as an adult, I sure do feel I’ve benefited from pretending to be a man (or not outright denying it) on my resume, on Github, in my email signature, or even here on Medium. If it’s been a while since you’ve read a Wikipedia biography, they usually start something like this: Dale Alvin Gribble is a fictional character in the Fox animated series King of the Hill,[2] voiced by Johnny Hardwick (Stephen Root, who voices Bill, and actor Daniel Stern had both originally auditioned for the role). These algorithms learn from the past data that is inputted, called training data, runs its analysis and uses this analysis to predict future events of any new data within the known classifications. It took this embedding vector and attempted to reconstruct the input name’s characters. Create and manage Azure Machine Learning workspaces. For the sentence “She likes to eat,” the top predicted names were “Frances,” “Dorothy,” and “Nina,” followed by a handful of other female names. Pandemic Modeling The Social Security administration has this neat data by year of what names are most popular for babies born that year in the USA (see social security baby names). Multi-class classification is the classification task with more than two class labels with no normal or abnormal results, such as plant species classification. She will grow up to be a software developer at … By learning about the List of Machine Learning Algorithm you learn furthermore about AI and designing Machine Learning System. For example, if I described someone as a “she,” would the model predict a female name, versus a male name for “he”? Facial-recognition algorithms are trained to convert images of faces into face embeddings—sequences of say, 16 numbers, which can be compared to find similar faces. Machine learning is the science of getting computers to act without being explicitly programmed. This tells me I didn’t have enough global variety in my training dataset. If this sounds interesting read along. Supervised learning algorithms are used when the output is classified or labeled. Its focus is to train algorithms to make predictions and decisions from datasets. In this current technology-driven world, machine learning is a prominent area which makes our machine or electronic device intelligent. Baby Name Generator We trained our AI to create unique baby names based on the … The least popular names (that I still had 50 examples of) were Clark, Logan, Cedric, and a couple more, with 50 counts each. The purpose of this field is to transform a simple machine into a machine with the mind. B. Supervised Machine Learning. Happily, I found just that kind of dataset here, in a Github repo called wikipedia-biography-dataset by David Grangier. The files for this exercise are in the "babynames" directory inside google-python-exercises (download the if you have not already, see Set Up for details). How To Implement Custom Regularization in TensorFlow(Keras), DeepMind Makes History Yet Again By Solving One of the Biggest Challenges in Biology. A Glimpse About Supervised Learning. It is a machine learning category where the output is already defined. Add your code in Neither of these Dales fit my aspirational self image. Stack Exchange network consists of 176 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share … I also expected that this model, reflecting the data it was trained on, would have learned gender bias — that computer programmers are male and nurses are female. In this post, I’ll show you how I used machine learning to build a baby name generator (or predictor, more accurately) that takes a description of a (future) human and returns a name, i.e. The bottleneck forces the network to learn only the most important features of a name, compressing it by stripping superfluous information. Now you definitely shouldn’t put much weight into these predictions, because a. they’re biased and b. they’re about as scientific as a horoscope. I am a Machine Learning Engineer. We’ll be building a classifier able to distinguish between boy and girl names. One way to dig deeper into what a model’s learned is to look at a table called a confusion matrix, which indicates what types of errors a model makes. If you would want to grow the efficiency of your e-commerce operations, you may be interested in checking out this Machine Learning Course.. More and more ecommerce retailers are embracing machine learning and deriving much value from it. Computers drive cars, fight parking tickets and raise children. When I was young, I always hated being named Dale. Word-embedding networks turn words into vectors of numbers whose values map to their semantic meaning in interesting ways. Seems like a good sign. For the sentence “He likes to eat,” the top names were “Gilbert,” “Eugene,” and “Elmer.” So it seems the model understands some concept of gender. Welcome to a short tutorial on a very basic Machine Learning algorithm called Markov Chains. Once the machine has learned, or been taught, it can start to make its own predictions. As we discussed, it has some powerful applications in ecommerce. She will grow up to be a software developer at Google who likes biking and coffee runs. This is mostly because my primary image of what Dales looked like was shaped by Dale Gribble from King of the Hill, and also Dale Earnhardt Jr., the NASCAR driver. Embeddings are an important machine learning technique. I have worked with several Machine learning algorithms. I scraped multiple lists of common alternative spellings for first-names, around 17,500 pairings. This is the first exercise where you get to train a neural network with back propagation … If you want to try this model out yourself, take a look here. Find more similar words at! I also only considered names for which I had at least 50 biographies. To account for this, and because I wanted my name generator to yield names that are popular today, I downloaded the census’s most popular baby names and cut down my Wikipedia dataset to only include people with census-popular names. ), followed by William, David, James, George, and the rest of the biblical-male-name docket. But sexism aside, what if there really is something to nominative determinism — the idea that people tend to take on jobs or lifestyles that fit their names?¹ And if your name does have some impact on the life you lead, what a responsibility it must be to choose a name for a whole human person. Next, I thought I’d test whether it was able to understand how geography played into names. Or, let’s face it, overwhelming. None of this involves any machine learning. Their hipster friends just named their daughter Dale and it was just so cute! But still, fun to think about. Ray Kurtzweil is an … So how well did the name generator model do? Nick Bostrom is a writer and speaker on AI. To account for this massive skew, I downsampled my dataset one more time, randomly selecting 100 biographies for each name. The condition involves sudden and progressive … Data Collection. It can classify the text as "Spam" or "Not Spam (Ham)". ... His work focuses on Machine Learning, Distributed Computing, and Discrete Applied Mathematics. It’s a useful way to debug or do a quick sanity check. I trained an algorithm to generate name embeddings for the 7500 common baby names using a neural network called an autoencoder—a neural network trained to … Next I decided to see if my model understood basic statistical rules about naming. If this post gets 1,000 stars, I will name my first-born child using this code. Our AI-powered baby name generator will find a unique name for your baby. August 12, 2020 - Researchers have created an early warning system that uses machine learning to predict necrotizing enterocolitis (NEC), a life-threatening intestinal disease that affects premature infants.. NEC impacts up to 11,000 premature infants in the US annually, researchers noted, and 15 to 30 percent of babies die from NEC. I’ve been writing about my other adventures in deep learning here~, High-quality slow-motion videos in 5 minutes with Deep Learning, Sparse, Stacked and Variational Autoencoder, Rules-of-thumb for building a Neural Network, Implementing an Autoencoder in TensorFlow 2.0, How to Create a Custom Loss Function | Keras. Start by learning the keys to picking a name and what common pitfalls to avoid.Then browse our inspiration lists or use our Baby Names Finder to search for names by letter, meaning, origin, syllables, popularity, and more. To do so, you can work on your training data, your corpus data, and, the metric that … Machine Learning - Neural Network to Predict Gender from First Name Background. Guess I’m back to square one when it comes to choosing a name for my future progeny…Dale Jr.? Use either cv_split_column_names or cv_splits_indices. NamSor API is focused on inferring gender and cultural origin / ethnicity from names, but as a by-product it does name parsing as well, ie. For more information, see Configure data splits and cross-validation in automated machine learning. When I was young, I always hated being named Dale. This means that, of all the bios of people named Ahmad in our dataset, 13% were labeled “ahmad” by the model. First, some background. This means it should be possible to randomly sample from a gaussian distribution to generate random embeddings that should yield plausible names: Some of them definitely don’t make much sense (“P” or “Hhrsrrrrr”) but I kind of like a couple (“Pruliaa?” “Halden?” “Aradey?”). It is based on the user’s marital status, education, number of dependents, and employments. The tutorial will only assume you have basic knowledge of Java programming. Meanwhile, looking one box over to the right, 25% of bios of peopled named “ahmad” were (incorrectly) labeled “ahmed.” Another 13% of people named Ahmad were incorrectly labeled “alec.”. This is mostly because my primary image of what Dales looked like was shaped by Dale Gribble from King of the Hill, and also Dale Earnhardt Jr., the NASCAR driver. Please like and share! I have to get names from the Social Security Administration for top 100 baby names of 2014 (I've … Press J to jump to the feed. I trained a neural network on a list of 7500 popular American baby names, forcing it to turn each name into a mathematical representation called an embedding. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. I mentioned before there’s a skew in who gets a biography on Wikipedia, so I already expected to have more men than women in my dataset. In this tutorial, I'll talk about the classification problems in machine learning. When I asked my parents about this, their rationale was: A. I have tried looking at a text problem here, where we are trying to predict gender from name of the person. I trained an algorithm to generate name embeddings for the 7500 common baby names using a neural network called an autoencoder—a neural network trained to reconstruct its input after the data has been squeezed through a bottleneck (called a latent vector) that allows a limited amount of data through. In this tutorial, we’re getting started with machine learning. Machine learning (ML) is the study of computer algorithms that improve automatically through experience. So evidently this model has learned something about the way people are named, but not exactly what I’d hoped it would. Machine Learning Teacher Myla RamReddy Data Scientist Review (0 review) $69.00 Buy this course Curriculum Instructor Reviews LP CoursesMachine Learning Machine Learning Introduction 0 Lecture1.1 ML01_01_Machine Learning Introduction and Defination 15 min Lecture1.2 Ml02_01_ETP_Defimation 15 min Lecture1.3 ML03_01_Applications of ML … Why not let machines name our children, too? So the bio above becomes: __ Alvin __ is a fictional character in the Fox animated series…, This is the input data to my model, and its corresponding output label is “Dale.”. Embeddings are an important machine learning technique. Top Machine Learning Influencers – All The Names You Need to Know Posted March 26, 2020. For example,-LG 42CS560 42-Inch 1080p 60Hz LCD HDTV -LG 42 Inch 1080p LCD HDTV These items are the same, yet their product names vary quite a lot. The Machine Learning Algorithm list includes: Linear Regression; Logistic Regression Press question mark to learn the rest of the keyboard shortcuts The most popular name in my dataset was “John,” which corresponded to 10092 Wikipedia bios (shocker! I did not like that my name was “androgynous” — 14 male Dales are born for every one female Dale. I uploaded my dataset into AutoML, which automatically split it into 36,497 training examples, 4,570 validation examples, a 4,570 test examples: To train a model, I navigated to the “Train” tab and clicked “Start Training.” Around four hours later, training was done. However, the product names are not always identical. You automatically put it in a bucket, the girl names bucket or the boy names bucket. This left me with 764 names, majority male. I built the embedding network as a variational autoencoder—a network that encourages the embeddings to have a normal distribution, rather than whatever crazy unpredictable distribution just happens to work best. Plus, the names of people with biographies on Wikipedia will tend to skew older, since many more famous people were born over the past 500 years than over the past 30 years. If you’ve read at all about Model Fairness, you might have heard that it’s easy to accidentally build a biased, racist, sexist, agest, etc. The dataset contains the first paragraph of 728,321 biographies from Wikipedia, as well as various metadata. In this article, we explore machine learning and … But in the case of our name generator model, these metrics aren’t really that telling. I’ve noticed a few interesting properties: When names differ by a simple feature (like an extra “a”, you can subtract out that feature and add it onto other names: You can “multiply” names by constants, which has some strange effects: If you can do simple arithmetic on names, you can also linearly blend them, taking a weighted sum of two name embeddings and generating intermediate names from those. The method of how and when you should be using them. model, especially if your training dataset isn’t reflective of the population you’re building that model for. Neither of these Dales fit my aspirational self-image. In this article, you'll create, view, and delete Azure Machine Learning workspaces for Azure Machine Learning, using the Azure portal or the SDK for Python. Once I had a model that could translate between names and their embeddings, I could generate new names, blend existing names together, do arithmetic on names, and more. Here are some sentences I tested and the model’s predictions: “He was born in New Jersey” — Gilbert, “She was born in New Jersey” — Frances. The machine learning part will inspect what corresponding means. The Google team picks on the example of training a machine learning system to predict the course of a pandemic. Loan Prediction using Machine Learning. Most names are unambiguous (Paul, Jane); some are ambiguous (Pat); some change genders over time (Hillary, Vivian), so you need to know the birth year as well as the name. Names are largely arbitrary, which means no model can make really excellent predictions. I've tried using the Levenshtein distance for measuring the string similarity however this hasn't worked. If you have enough data, it's typically enough to … Gilbert, Frances). Although these are technically incorrect labels, they tell me that the model has probably learned something about naming, because “ahmed” is very close to “ahmad.” Same thing for people named Alec. detecting the first name / last name order as well as the split. If you’ve built models before, you know the go-to metrics for evaluating quality are usually precision and recall (if you’re not familiar with these terms or need a refresher, check out this nice interactive demo my colleague Zack Akil built to explain them!). It’s fascinating to learn from the best scientists. Naturally, there’s a selection bias when it comes to who gets a biography on Wikipedia (according to The Lily, only 15% of bios on Wikipedia are of women, and I assume the same could be said for non-white people).

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