Following the great minds of machine learning can help you discover new things and deepen your knowledge. In this tutorial, I'll talk about the classification problems in machine learning. I just finished Exercise-4 of Dr Andrew Ng's most excellent Machine Learning course. This is the first exercise where you get to train a neural network with back propagation … How to name your baby using machine learning 2 months ago . I did not like that my name was “androgynous” — 14 male Dales are born for every one female Dale. Or, let’s face it, overwhelming. 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. But still — wouldn’t it be cool to have the first baby named by an AI? 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 also only considered names for which I had at least 50 biographies. The model labeled Alecs as “alexander” 25% of the time, but by my read, “alec” and “alexander” are awfully close names. 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!). The bottleneck forces the network to learn only the most important features of a name, compressing it by stripping superfluous information. Machine Learning - Neural Network to Predict Gender from First Name Background. Although I wanted to create a name generator, what I really ended up building was a name predictor. Use either cv_split_column_names or cv_splits_indices. The Machine Learning Algorithm list includes: Linear Regression; Logistic Regression 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. The least popular names (that I still had 50 examples of) were Clark, Logan, Cedric, and a couple more, with 50 counts each. But in the case of our name generator model, these metrics aren’t really that telling. In this case, my model had a precision of 65.7% and a recall of 2%. It’s a useful way to debug or do a quick sanity check. 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?”). Embeddings are an important machine learning technique. Neither of these Dales fit my aspirational self image. What can Wikipedia biographies and Deep Neural Networks tell us about what’s in a name? 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. Its focus is to train algorithms to make predictions and decisions from datasets. Probably there isn’t, and this is about as scientific as horoscopes. First, some background. Of course not — I’d turn to deep learning (duh!). model, especially if your training dataset isn’t reflective of the population you’re building that model for. 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. It is based on the user’s marital status, education, number of dependents, and employments. I just wanted to build a model that understood something about names and how they work. A Glimpse About Supervised Learning. Multi-class classification is the classification task with more than two class labels with no normal or abnormal results, such as plant species classification. As we discussed, it has some powerful applications in ecommerce. 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. None of this involves any machine learning. 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. Sigmoid Activation and Binary Crossentropy — A Less Than Perfect Match? 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. The model took around a 30 minutes running on a GPU to train to a reasonable level of accuracy — as it trains, you can see the model slowly getting better at modeling and reconstructing names: Once we’ve converted words into vectors, we can add, subtract and multiply them. Happily, I found just that kind of dataset here, in a Github repo called wikipedia-biography-dataset by David Grangier. Maybe it’s a perfect combination of both parents’ names—or maybe it’s a name that’s completely unique. In this tutorial, we’re getting started with machine learning. 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 … But still, fun to think about. ), followed by William, David, James, George, and the rest of the biblical-male-name docket. My goal was not to build a model that with 100% accuracy could predict a person’s name. When I was young, I always hated being named Dale. 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. So evidently this model has learned something about the way people are named, but not exactly what I’d hoped it would. 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. Why not let machines name our children, too? She will grow up to be a software developer at Google who likes biking and coffee runs. For example, if I described someone as a “she,” would the model predict a female name, versus a male name for “he”? Guess I’m back to square one when it comes to choosing a name for my future progeny…Dale Jr.? I have tried looking at a text problem here, where we are trying to predict gender from name of the person. It is a machine learning category where the output is already defined. 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. This left me with 764 names, majority male. 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. These datasets can either be curated or generated in real time. : My child will be born in New Jersey. Baby Name Generator We trained our AI to create unique baby names based on the … The most popular name in my dataset was “John,” which corresponded to 10092 Wikipedia bios (shocker! : My child will be born in New Jersey. When I was young, I always hated being named Dale. 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.”. So how well did the name generator model do? How To Implement Custom Regularization in TensorFlow(Keras), DeepMind Makes History Yet Again By Solving One of the Biggest Challenges in Biology. 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. By learning about the List of Machine Learning Algorithm you learn furthermore about AI and designing Machine Learning System. 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. When I asked my parents about this, their rationale was: A. If you want to try this model out yourself, take a look here. Neither of these Dales fit my aspirational self-image. Before you start reading the code, I want to share a little bit about Supervised Learning. 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. 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. It can classify the text as "Spam" or "Not Spam (Ham)". 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 … 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. Once the machine has learned, or been taught, it can start to make its own predictions. Computers drive cars, fight parking tickets and raise children. The dataset contains the first paragraph of 728,321 biographies from Wikipedia, as well as various metadata. Press question mark to learn the rest of the keyboard shortcuts 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. 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. Our AI-powered baby name generator will find a unique name for your baby. 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. 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). Project idea – The idea behind this ML project is to build a model that will classify how much loan the user can take. Women with androgynous names are potentially more successful. The Google team picks on the example of training a machine learning system to predict the course of a pandemic. I am a Machine Learning Engineer. Embeddings are an important machine learning technique. 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 machine learning part will inspect what corresponding means. In this current technology-driven world, machine learning is a prominent area which makes our machine or electronic device intelligent. Machine learning is the science of getting computers to act without being explicitly programmed. 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%”. 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. My past work included research on NLP, Image and Video Processing, Human Computer Interaction and I developed several algorithms in this area while … It took this embedding vector and attempted to reconstruct the input name’s characters. Please like and share! Find more similar words at wordhippo.com! 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 … Pandemic Modeling 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. 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. Source Code: Emojify Project 4. 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. This tells me I didn’t have enough global variety in my training dataset. We live in the future. Stack Exchange network consists of 176 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share … 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. For the sentence “She likes to eat,” the top predicted names were “Frances,” “Dorothy,” and “Nina,” followed by a handful of other female names. 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. 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. What if a computer program could find the ideal baby name. 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.”. The most common example is the Spam Detection method. 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. 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. For more information, see Configure data splits and cross-validation in automated machine learning. Word-embedding networksturn words into vectors of numbers whose values map to their semantic meaning in interesting ways. Choosing the perfect name for your baby can be fun! If this post gets 1,000 stars, I will name my first-born child using this code. 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've tried using the Levenshtein distance for measuring the string similarity however this hasn't worked. Seems like a good sign. To do so, you can work on your training data, your corpus data, and, the metric that … The point is to use a metric to evaluate, for each line of the corpus data, which location is most likely to be quoted. 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. Gilbert, Frances). 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. 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. I wouldn’t want to leave that responsibility to taste or chance or trends. In the “Evaluate” tab, AutoML provides a confusion matrix. The purpose of this field is to transform a simple machine into a machine with the mind. It’s fascinating to learn from the best scientists. Word-embedding networks turn words into vectors of numbers whose values map to their semantic meaning in interesting ways. Top Machine Learning Influencers – All The Names You Need to Know Posted March 26, 2020. This means that, of all the bios of people named Ahmad in our dataset, 13% were labeled “ahmad” by the model. Next I decided to see if my model understood basic statistical rules about naming. We’ll be building a classifier able to distinguish between boy and girl names. Supervised Machine Learning. If this sounds interesting read along. Names are largely arbitrary, which means no model can make really excellent predictions. I was pretty unimpressed with the model’s ability to understand regionally popular names. cv_split_column_names was introduced in version 1.6.0. detecting the first name / last name order as well as the split. Machine Learning is a really common AI technology. Nick Bostrom is a writer and speaker on AI. 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). Data Collection. 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: “___”. ... His work focuses on Machine Learning, Distributed Computing, and Discrete Applied Mathematics. He focuses on Machine Learning and its applications, particularly learning under resource constraints, metric learning, machine learned web search ranking, computer vision, and deep learning. She will grow up to be a software developer at … People tend to assume that ML means machines teaching themselves – but really, ML means machines learning from people. What follows is a study of applying machine learning to achieve semblance of human-like logic and semantics for alternative name identification. 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. 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