Vote, As If That Goofy But Nice Person From High School Is Running

I grew up very disenfranchised. My family emigrated from Hong Kong to Canada when I was five and a half, and it took a long time for us to get used to life in the new country. My father started voting at some point but never discussed it with us. I think my mother voted after many years, and only a few times in her life out of a possible 50 or so. The two of them disagreed along conservative/liberal lines. Mostly we simply felt it had nothing to do with us.

Moreover, we would hear outrageous tidbits on the news about so-and-so, and then extrapolate from that to throw up our hands and claim there was no good option.

One of my high school aquaintances became a Member of Parliament. I got to know him a bit because we got lost on a hike during a field trip. He was someone many people made gentle fun of, as I recall. But the time on that school hike was enough to learn he was thoughtful, considerate, and liked to learn interesting things.

If I were plonked into the next generation, when the person running for office was an old friend of my parents’, if I could see that the people running for office were people like I knew, I think it would have made all the difference whether I voted starting at 18. To really know that some people in government are trying, that many of them are using their resources as best they can, that they make mistakes because mistakes are inevitable but it’s a long game and most mistakes are not only recoverable but essential learning experiences – I think that is what it takes to be able to get out there and vote. That, and having at one point roommates who were anthropology students and volunteered like mad for local elections, influenced me to start voting.

In a word, connection.

For me, the cornerstone of fighting racism and sexism and many isms is to act as if proportional representation of all of us by interest and merit is reality. That means, for example, I should act as if there are amazing geniuses in every town, every neighborhood, no matter where. And good political leaders too. They just have to be found, and could use a little boost.

So if there is no person who, if you got lost with them on a hike and you found out has a good heart, and who is running for office – if you don’t know such a person, assume that they exist, and find them! And vote for them!

To take a moment and be mathematically explicit: why OK to assume they exist? See above paragraph on why and how this mathematician chooses to fight racism and sexism. How can you find them? Talk to people in your neighborhood. Yeah, news and google whatever, but build trust with smart people you know (and why/how to do that? See above paragraph). What if you hate hiking? Replace with other activity of choice. It is not essential to get lost.

Finally, if you need an incentive, what’s in it for you? Hey, if you vote on your best ideas and most deeply held values, it’s a positive action to make the world better for yourself and for everyone else in your constituency.

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Letter to a young artist (from mom)

Dear Young Artist,

I was listening to the Sara Bareilles album based on her music for the Broadway show, “Waitress”, and remembering the movie by Adrienne Shelly, and the wonderful performance by Keri Russell, and suddenly I remembered a lot of shows and movies.

A weird thing happens when you reach my age and watch shows and listen to music. This weird thing will happen to you.

Like a tidal wave, a bunch of shows and music will make sense. The cultural references will be your references, and the philosophical questions will be your questions. It’s because at my age, people who are artists have been working long enough and building their reach far enough that their work comes to me. That feeling of generational recognition happens to me when I watch Pixar movies, or listen to Radiohead, or see The Good Wife, or many other things.

It’s not that I and the writers are locked in the 80s. On the contrary. In the reading nook at your elementary school there is an inscription that says you really can lead more than one life – through books. (Which I generalize here to art). And that is my point. Through this connection, I’ve been touched by more people, and more different people, than I thought would ever happen. We’re not locked. We’re unlocked.

The feeling that came over me when I thought about Keri Russell was overwhelming gratitude. I had seen her in Waitress and in The Americans, two very different vehicles. The Americans is a difficult show to watch; it is incredibly sad. What makes me grateful is I lived a bit of that life, while watching that show. The writers and crew did an amazing job. The show takes place in a time that you didn’t live in but I did. The time of the cold war. Because I could relate to that time, I think I related more to the characters than I otherwise might have.

I think I’ve seen several of these generational waves unfold. When I was young people made shows that reflected their childhoods in the forties and fifties. Then sixties and seventies. Of course when I saw it I got a lot out of it, but I don’t think it’s that same as when your own generational stuff comes in. Maybe it’s not a tidal wave, it’s more like a lot of different ships crossed their own oceans to come to your harbor.

Of course these art forms – music, movies, TV – involve people of different ages, and the connection part is way more than just generation. Generation, for me, turned out to be more powerful than I expected.

I can’t say enough how these ships help me, bolster me, nourish me. I’m proud of you for pushing off on your own boat. The work of giving extra lives to people is terribly necessary and terribly undervalued. Even as a mathematician, I’m convinced that it is only through imagining other lives that any important problem gets solved.



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Prerequisite 1: Machine Learning

To get into any subject, there is a lot of jargon to wade through. I love simplicity but precise terminology makes things much easier. In this post I’m going to establish basic definitions for these terms, which are necessary for deep learning:

  • machine learning
  • model
  • supervised learning
  • prediction
  • cost function
  • function optimization
  • local minimum vs. global minimum

Everyone who reads this post has some idea of learning. What does it mean for a machine to learn?

In the machine learning context, to learn means:

to create the ability to predict from data.

The method used to predict the data, learned or otherwise, is called a model. When a machine learns a model, then machine learning has happened!

A model can be thought of as a function, a math function. If you know about functions, think of the model as a function F where F(data) = prediction.

An example of data, predictionmodel:

Suppose we have a month’s worth of weather data for Redwood City California, and we want to predict weather based on this data. The data is that in 30 days, it rained 3 days. So it rained 10% of the time.

Here is a very simple model: based on these 30 days of data, the probability of rain is 10%. This is a pretty low probability, so I predict no rain for all days. In my function language: the learning process based on historical data gave me a function F:

Learning Process (30 days of data: 27 no rain, 3 rain) = F = no rain

To elaborate on the function F:

F(any time) = no rain

If the weather continues to be the same, my model, the “no rain” model, has an accuracy of 90%. Impressive, isn’t it?

Despite the “no rain” model’s high accuracy (especially in times of drought), can we point out other limitations of this model? We will come back to this question. It is really a good chunk of the work of a scientist – deciding what to do with your model after you’ve computed it. This model might be obviously bad – but many models suffer from exactly the problem that this model does. Care must be taken!

Notice that very little learning took place to create this model. One calculation, the ratio of rainy days to all days, is used as the basis of all predictions. We can maybe say that the “learning” method used in this weather example is just extrapolation, or trending. And the computer didn’t learn it, we did. We decided the formula to use to predict the weather.

How do we get a machine to do the learning? How do we get a computer to build a model?

We give the machine some scaffolding, and program the machine to compute the details – the “walls and floors” of the model.

Since the machine is a computer and computers work with numbers, the scaffolding is a particular class of function, and the computer fits a “best” function of that particular type that matches our observed data.

The method used in almost all of machine learning is this: Estimate the cost of a mistake. Formulate it with variable parameters – use a template formula for your model function that has variable parameters. Minimize that cost with respect to those parameters – find the value of those parameters that gives you the least cost; find the “best” value of those parameters. That gives you the “best” function.

We will discuss this in more detail in another post.

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PSA: What makes a good personal essay?

It’s college essay season again. As hard and annoying as they are to write, I’m a huge fan of the genre of which they are a sub: the personal essay. The personal essay, when well done, exploits tools of both expository and creative writing. Here are some excerpts from my heroes and some notes about why their personal essays are wonderful.

James Baldwin

Notes of a Native Son

On the 29th of July, in 1943, my father died. On the same day, a few hours later, his last child was born. Over a month before this, while all our energies were concentrated in waiting for these events, there had been, in Detroit, one of the bloodiest race riots of the century. A few hours after my father’s funeral, while he lay in state in the undertaker’s chapel, a race riot broke out in Harlem. On the morning of the 3rd of August, we drove my father to the graveyard through a wilderness of smashed plate glass.

James Baldwin is an amazing writer. This paragraph is an example of the power of simplicity. He has an unusual phenomenon to describe, the confluence of three huge events: the death of his father, the birth of a sibling, and a race riot. He just needs to make that clear. Being a master, he brings it all together at the end with enormous force. Note how that simple last sentence serves many purposes all at once: it evokes sight and sound (you can’t help but imagine the glass crunch beneath their wheels), it sets us up for a journey, describes the outer world of chaos, describes his inner broken heart.

If you have an amazing story to tell, start simply. Don’t let the writing overshadow the events. And bring in a concrete detail that makes it alive.

Virginia Woolf

The Duchess of Newcastle

‘… All I desire is fame’, wrote Margaret Cavendish, Duchess of Newcastle. And while she lived her wish was granted. Garish in her dress, eccentric in her habits, chaste in her conduct, coarse in her speech, she succeeded in her lifetime in drawing upon herself the ridicule of the great and the applause of the learned. But the last echoes of that clamour have now all died away; she lives only in the few splendid phrases that Lamb scattered upon her tomb; her poems, her plays, her philosophies, her orations, her discourses – all those folios and quartos in which, she protested, her real life was enshrined – moulder in the gloom of public libraries, or are decanted into tiny thimbles which hold six drops of their profusion. Even the curious student, inspired by the words of Lamb, quails before the mass of her mausoleum, peers in, looks about him, and hurries out again, shutting the door.

Woolf makes good use of variety in her sentences. She starts with a quote, then parallel inverted clauses, then parallel noun phrases. Concrete language grounds all the abstractions: from garish, eccentric, and chaste we go to ridicule, applause, a dying clamour, tomb, mould, decanters, thimbles, drops. And then concrete images of actions: quails, peers, looks about, hurries, shuts.

This first paragraph is inviting. I want to taste the liquid in one of those thimbles. I want to see if I agree with Lamb, or think he’s full of fluff. I want to be braver than the curious student.

Ursula K. LeGuin

I am a Man

I am a man. Now you might think I’ve made some kind of silly mistake about gender, because my name ends in A, or I own three bras, and I’ve been pregnant five times, and other things like that that you might have noticed, little details. But details don’t matter. If we have anything to learn from politicians it’s that details don’t matter. I am a man, and I want you to believe and accept this as a fact, just as I did for many years.

Hang on to your hat, this essay is going to take your for a wry and ironic ride. This is a great example of good use of shock value, sly insertion of philosophy and jokes, and good clear, varied sentence structure to boot. Note her grounding of abstract ideas into concrete imagery – what does it mean to be a woman? Do bras make a woman? What are details and what matters? She poses these questions in an entertaining way.

This is an example of a high-concept essay – using a bold set-up to explore many different kinds of ideas.

Ian Frazier

My First Summer Living in a Van

I had not expected that it would tick. As soon as the sun hit it in the morning—at 6 a.m. or so, in June, in northern Michigan—the metal would start to expand in the heat: tick…tick…tick. My first summer living in my van, in the Pigeon River campground near the town of Vanderbilt, I almost never succeeded in sleeping past dawn. And I was not prepared for how stuffy it got with the windows rolled up against the mosquitoes. I thought the overhead light, which had come loose and dangled by its wires above the rudimentary plywood bed and foam-rubber mattress that I slept on, would be enough light to read by. It wasn’t.

Great example of ‘in medias res’, starting right in with the situation at hand. Ian Frazier is a prolific essayist. He also knows how to tackle abstract ideas using concrete stories and images to connect to the reader and bring the subject home.

George Orwell

Some Thoughts on the Common Toad

Before the swallow, before the daffodil, and not much later than the snowdrop, the common toad salutes the coming of spring after his own fashion, which is to emerge from a hole in the ground, where he has lain buried since the previous autumn, and crawl as rapidly as possible towards the nearest suitable patch of water. Something — some kind of shudder in the earth, or perhaps merely a rise of a few degrees in the temperature — has told him that it is time to wake up: though a few toads appear to sleep the clock round and miss out a year from time to time — at any rate, I have more than once dug them up, alive and apparently well, in the middle of the summer.

This essay connects nature to politics, society, and philosophy in a wonderful way. Note again the sentence variety, concrete imagery, strong verbs. The pacing in this piece is slower than a college essay can be, but if you read it you will still be amazed at all the ideas Orwell manages to pack into the essay.


The examples above all show how great writers use:

  • sentence structure – they are correct, clear, varied; they borrow from spoken language when needed
  • concrete imagery – they don’t overdo it, but they bring it in to show rather than tell
  • ambition – they write about deep things, but do the work to get there. You’d have to read the full essay to see what I mean. They shoot for the fences.

In a college personal essay, keep in mind that it is not only a personal statement but a professional one. It makes a case for you as a student. Tell success stories, not sob stories. Don’t say anything you would not say in person, to someone’s face, in a job interview. Take a stand, of some sort. Whatever stand you take (bold, ironic, bitter, hopeful, witty, futuristic), make every word in your essay hang on that thread. If you read each of the essays above, they hang together.

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The Band Trip To Reno

When I was a junior I played second alto sax in Senior Jazz Band B, and we went to Reno, in February, with Senior Jazz Band A. We were excited, because for the first time on a band trip, we’d stay in a hotel.

One of my room mates was Stephanie, a senior and lead alto sax in Jazz Band A. As soon as we got to our room, she unpacked and neatened up and put a cup with apple flavored granola bars on the dresser. She told us all to help ourselves whenever we wanted. My own mom was not as cosy and motherly as Stephanie.

Soon after, she got very sick with the flu. Outside, snow began to fall and everyone went out to walk around Reno and look at the lights. The teacher brought Stephanie soup. The next day was the music festival and competition we had come for. In the morning Stephanie’s voice was gone and she couldn’t eat breakfast.

Jazz band B played and we did okay, but didn’t win. Then it was time for Jazz Band A. It’s important to know that Stephanie was a huge part of the A team. She was also the first chair clarinet in Concert Band.

The teacher told us that unlike a concert band festival, we could cheer for our jazz band in the audience, so we sat close and did. Jazz Band A blew everyone away. They had a number of excellent musicians. But I knew what it took for Stephanie to be up there. She played as if she was as fit as a champion.

Being a good lead is no small thing. Leads start out as the best player in the room. But much more is asked of a leader than just playing better than four random people. A leader is supposed to sing, and light the way for others. In truth, I’ve never seen a player get that call but didn’t heed it. Stephanie was particularly great at it. Music doesn’t lie. When you heard Stephanie play, you heard strength and joy.

Jazz Band A won, and even though I wasn’t in Jazz Band A, I felt a sizzle run through me when they announced it. It was a big deal for a band from a smallish city in Canada to win in the big bad States. I could not wait for next year, when I could try out for Jazz Band A.

The real punchline, though, is this. It was the apple flavored granola bars. That someone so homey and nice could also be a true musical superhero – well, that made my heart grow five sizes in one day, like the Grinch’s. On that band trip to Reno.


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My baseline hero

In 1976, a major earthquake struck in Tangshan, China. Because many of the buildings had not been built to withstand earthquakes, and because search and rescue infrastructure at the time was poor, the death toll was large: the official estimate is about a quarter of a million people.

In The Good Women of China, I read about a woman whose experience was horrific. Her fourteen year old daughter was pinned from the waist down by concrete rubble from their apartment building. For over two weeks they waited for rescue, unable to move the rubble themselves. At first they were hopeful. The girl could talk and eat and drink. She wasn’t in much pain, as she couldn’t feel anything in the lower half of her body. In the massive surrounding destruction no help came at all. The girl lost consciousness and died, as her mother watched, hoped, waited, despaired. Second by second.

Here is why this mother is my baseline hero, the one I find myself comparing all other heroes to. She had lost everything in the quake: her family, her home. And what did she do? She got a live-in job at an orphanage, wiping noses, washing diapers, making oatmeal.


What’s so heroic about that? One, she saw an extensive problem that she had the skills to solve, and she went and worked on it. I’m sure there’s some line that says orphans are intrinsically compelling, after all, and much can be made of small tokens: occasional hugs, smiles, crayon drawings of hearts. But if these tokens are enough, why do so few people take care of orphans? No. Devoting her work time to them is a big thing, and when I read about how she went about it, there was no flavor of sentimentality or angels. It was pragmatism. The problem was large, right in front of her, and she could do something about it. She did. What it looks like to me is a data driven choice.

Two, in order to do that, she had to struggle past her own tragedy. She had good reason, after what she went through, to sit in a bar and “stick her neck out for nobody.”

The book described how her grief and horror never went away. Maybe outrage, too. She had regular nights when she could not sleep. But in the morning the work still had to be done, and she did it.

I can’t think of a purer example of someone who had to get over herself in order to accomplish what she set out to do. With very little help: without the crutches of fame or fabulous heights of remuneration. No honorary PhD of good-scout-logy. Not even a donated Vera Wang gown to wear on the red carpet. No red carpet.

Where I live, tech heroes are very popular. Arts and science heroes too. It’s also popular to spend a lot of time on passions and dreams. And a lot of what happens in the realm of philosophical discussion is about privilege.

For me, thinking about this woman from 70s Tangshan cuts a clean swath through narcissism and victimhood. She is an ultimate hero because what she has done with her life is so awesome, she shows the way. I want to be on her team.

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What is deep learning?

Deep learning is a buzz phrase that refers to a branches of machine learning that involve multiple, interactive layers of nonlinear data transformations. It’s deep because there are multiple layers, and it’s deep learning because the layers can communicate with one another. Deep learning techniques have been growing in accuracy and success and are interesting to study in themselves.

As a data scientist, I’m drawn to deep learning because it involves some of my favorite things: multidimensional math, linear algebra in particular; cool algorithms; the chain rule; and surprises – which in the applied math world means “nonlinear effects”.

So my goal is to document adventures in deep learning in the next months, and make this blog a place where interested people can learn along with me.

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How I learned to stop worrying and love computer programming

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As a go-getting engineering student, I chose my first university elective to be Computer Science 101. I didn’t like it. The lectures put me right to sleep – and I’m a lecture lover. The exams were not a problem. But … Continue reading

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My brutish love of poetry, part 4

Poetry can be dangerous. Yes, it can be pretty, pithy, wise, amusing. But it’s gotten me and countless others into trouble. My web site here is a pretty safe place and I’ve quoted some pretty safe poems. There are many … Continue reading

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My brutish love of poetry, part 3

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Another gift of Canada is exposure to French Canadian poetry. If you know even a little bit of french, you can poke around at the edges. I am a fan of Anne Hébert and of her cousin Hector de Saint-Denys … Continue reading

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