or Connect
Styleforum › Forums › Men's Style › Classic Menswear › Australian Members - Part II - if you read the first post, you'll get what this is all about.
New Posts  All Forums:Forum Nav:

Australian Members - Part II - if you read the first post, you'll get what this is all about. - Page 143

post #2131 of 3803
Quote:
Originally Posted by clayb View Post
 

 

Thanks Coxsackie, it sounds like a lot of work to be done when you get caught in a heavy rain with your leather shoes.

 

Not really. I was out and about last night in Melbourne, with intermittent drizzle and plenty of water on the ground, in leather-soled shoes. I simply avoided stepping in puddles. Shoes were fine.

 

I won't get into the Topy argument (which has been played out to the death on this thread many times over), except to say that they are not my personal preference.

 

Bottom line: when building your shoe collection, try to end up with at least one pair of black and one of brown fitted with some kind of waterproof sole. Check the weather report before deciding on your footwear for the day. But don't panic if you find yourself caught out in the rain with leather soles - just take a bit of extra care.

post #2132 of 3803
For Sydneysiders, an RM Williams sale:

https://www.ozbargain.com.au/node/256450
post #2133 of 3803
Great deal actually.
What is everyone's thoughts on a Navy M65 jacket ? or it must be olive ??
Application - casually on the weekend & over suit not so often.
Edited by md2010 - 7/7/16 at 7:42pm
post #2134 of 3803
Quote:
Originally Posted by clayb View Post
 

 

Sorry for coming across like that. I didn't mean to try to learn Big Data stuff via those 'toys', they are just to give me the first taste of this emerging technology so that I can decide if it's for me or not. And I wholeheartedly with you that we should start with the basic and master it first. Personally I have problems with people who like to tell me web development equals to knowing some fancy frameworks like React etc.

 

 

For some it can be billions of records. I'm not sure if that's big enough or not but the Big Data stack, while is still evolving, has been pretty much decided already.

 

 

Coincidentally, I have been eyeing the GTX 1080 for some experiments/personal projects, but will have to think through it first.

 

 

That's a surprise to me. I always thought removing outliers must be one the first things to do. I've been warned that a majority of time would be spent on cleaning up the data, not the interesting stuff like analysing, training, or modelling.  

 

I have experienced it :) C++ classes with thousands of lines and people keep adding new methods to them and no one dares, or bothers, to refactor the 20 year old code base. It's a text (e.g. NoSQL) database btw.

 

 

Interesting link. Also on the same subject, I know someone, who has a background in statistics, always complains to me that they don't have enough of knowledge of computer science to do the job good enough and I always have to assure them that their own knowledge is more important and that they only need to know the basic of CS to run commands and use the tools provided etc.

 

For me I guess I'm still at the exploration phase, pondering which area to dive into. Australia, imho, is not a very large market when it comes to IT, not to mention the outsourcing trend, and it's important to choose the right areas to stay relevant and competitive in the long term. Big Data seems to be one of them, and also is intellectually challenging, which is attractive to me. So I decide to give it a shot and will see how it goes. 

Billions of rows is standard for an analytical workload. You're going to have to tweak your DB a little bit to speed up ELT and be careful with indices and maybe a bit of partitioning, but you can stay relational and you might even be able to do it on your laptop if it's got enough disk. Google "Postgres billions of rows" and you'll find some threads dating back to the early 2000s when hardware was a lot less capable... If you haven't yet, I'd recommend reading an easy book like https://www.amazon.com/SQL-Antipatterns-Programming-Pragmatic-Programmers/dp/1934356557 as well as a proper theoretical one like https://www.amazon.com/Introduction-Database-Systems-8th/dp/0321197844/r or https://www.amazon.com/Relational-Model-Database-Management-Version/dp/0201141922/

 

I'd shy away from GPUs unless you are interested in low level programming. Truth is, there's very few genuine applications for GPUs - maybe very complex algorithms running on very large datasets, or stuff like... video games actually processing graphics - so it's a bit of going down the rabbit hole with not much to show for it. If you're interested in parallel programming this is a decent book: http://chimera.labs.oreilly.com/books/1230000000929/index.html

 

Cleaning the data IS the interesting part. Because that's one of the things that has the largest impact on the performance of your model. It forces you to make a lot of design decisions. Model selection, validation, etc. are fun the first few times, but I'd argue a well designed multilinear regression is going to beat deep nets and XGBoost if the guys building it know what they are doing. In fact they have in a recent Kaggle competition which created quite a bit of buzz in the community. 

 

You don't just "remove outliers". Some observations naturally have a lot of influence on the model but they may be valid. In which case you need to do some transformations on your variables or you need to pick a different model. E.g. https://www.youtube.com/watch?v=s5X_Poq9dJA&t=5m16s

post #2135 of 3803
Quote:
Originally Posted by md2010 View Post

Great deal actually.
What is everyone thoughts on a Navy M65 jacket ? or it must be olive ??
Application - casually on the weekend & over suit not so often.

get both :D

post #2136 of 3803
@crdb - all very interesting stuff from what I can follow. Any recommendations for a real beginner to look at just to get an overview and understanding - website, book, video. Just enough so I can keep up with this thread? (Seriously, although I feel like I should add a menswear comment too - what is appropriate attire for analysing big data and computer programming - Cucinelli cashmere hoodie?)
post #2137 of 3803
Quote:
Originally Posted by Pink Socks View Post

@crdb - all very interesting stuff from what I can follow. Any recommendations for a real beginner to look at just to get an overview and understanding - website, book, video. Just enough so I can keep up with this thread? (Seriously, although I feel like I should add a menswear comment too - what is appropriate attire for analysing big data and computer programming - Cucinelli cashmere hoodie?)

I think the two most important things are:

- understanding the relational model, and applying it in the best open source database available today, PostgreSQL (the most relational of the lot by far thanks to decades of academic research in Ingres and elsewhere);

- understanding statistical learning and if you have time, statistics itself. 

 

The first allows you to reason about data declaratively - that is, without specifying how whatever you like is computed. It's actually incredibly conceptually easy; if you understand Venn diagrams and logic, you can write correct, relational SQL. Which is why I am mystified that most CS courses teach it from a flawed POV that dates from before Codd's seminal paper (https://www.seas.upenn.edu/~zives/03f/cis550/codd.pdf).

 

So, you take your input data, and you literally "declare" what you want and voila, data cleaned and result obtained, provably correctly. Understand the concept of a transaction, of a logical unit of work, of a relation variable vs a relation (a variable vs values, if you will), domains, types, etc. and you're good to go.

 

The second is about making sense of the data. Curve fitting, basically. Your brain does it everyday with everything, processing GB of data per second from all your senses. Reading SF, you're looking at posts about clothes from dressers of varying ability; first you "learn" which ones are good, then you "learn" why what they are doing is good, and voila, you have learnt about fashion by abstracting from examples (which are your data). There might be obvious patterns (the X vs )( quarters, the jacket ending halfway your silhouette, the famed "Northern Lights"), constraints (no open lacing with a suit) and non-obvious ones ("which skin type works best with which shirt pattern and colour palette"). 

 

Statistical learning is the formalisation of this. You have data, you fit a model to it (by minimising the error between the model and the data, usually) and you derive some kind of use from it (in the SF example: you learn to dress "better" although really you learn to buy expensive clothes that very few people will understand beyond "he looks nice"). You can use these models for intuition (e.g. aforementioned "obvious patterns" that "explain"; or the revenue equation mentioned before) or for prediction (try a bunch of new shirt and jacket patterns together and "feel" that they are wrong or right, i.e. the amount of "error" in what you just did vs what you think looks good, which you could call taste).

 

And there we talk about the separation between model and implementation. The model is only concerned with how things are, defining your input and output, at a conceptual level. Implementation is about how you make it happen. This is a very important distinction. A constraint on an SQL column is a model consideration: this column can only take these values, how you implement it is not important but it has to happen. An index is an implementation consideration (although something like CREATE UNIQUE INDEX WHERE [logical statement]; in SQL straddles the two - it's an implementation trick used to implement a model-level constraint). From that point of view, statistical learning is about the model, and Spark/Hadoop/Redshift (yes you can)/R/whatever is about implementation (at different levels). 

 

I used to try and learn from MOOC but in my experience you just pick up patterns of behaviour that you can then apply in a job without really understanding the fundamentals. That used to cut it in 2008, not so much today. For the same number of hours, read the textbooks and understand what they say, then be able to abstract from that to new situations, patterns and models, and you're a much better thinker for it. A CM equivalent might be the difference between understanding the reason for which a wool tie does not go with the finest worsted suit, or understanding why things work at different levels of formality, vs parroting "no brown in town". 

 

And so I repeat my recommendations: ISLR for statistical learning (free on http://www-bcf.usc.edu/~gareth/ISL/ - although you can bump up to ESLR if you feel comfortable with linear algebra) and Code or Date for the relational model as per above post. Date is I think a bit more readable. They disagree on a few issues. Total reading time 20-50 hours depending on how comfortable you want to get with the material.

post #2138 of 3803

I should probably point out that I'm the business guy of the company so take what I say with a pinch of salt :P

post #2139 of 3803
Quote:
Originally Posted by Pink Socks View Post

(Seriously, although I feel like I should add a menswear comment too - what is appropriate attire for analysing big data and computer programming - Cucinelli cashmere hoodie?)

I would have thought something with a pattern.
post #2140 of 3803
Quote:
Originally Posted by The Ernesto View Post


I would have thought something with a pattern.

post #2141 of 3803
Quote:
Originally Posted by md2010 View Post

Great deal actually.
What is everyone's thoughts on a Navy M65 jacket ? or it must be olive ??
Application - casually on the weekend & over suit not so often.

I had an olive one. Make sure you get the slim fit model.
post #2142 of 3803
Quote:
Originally Posted by Coxsackie View Post


Bottom line: when building your shoe collection, try to end up with at least one pair of black and one of brown fitted with some kind of waterproof sole. Check the weather report before deciding on your footwear for the day. But don't panic if you find yourself caught out in the rain with leather soles - just take a bit of extra care.

+1, Exactly.

I always choose a waterproof sole if substantial rainfall is likely on any given day.

In regards to the preference for Topy vs no Topy, I like to Topy for the most part. Not so much for fear of shoes getting wet, but more to avoid the inconvenience and hassle of resoling when I prematurely wear out the toe area of my soles. This could change if I ever find someone who can do a decent job of fitting flush metal toe taps.
post #2143 of 3803
Quote:
Originally Posted by Pink Socks View Post

..... I feel like I should add a menswear comment too - what is appropriate attire for analysing big data and computer programming -[/URL]?)
3/4 length black cargo pants, band t shirt, greasy hair in long pony tail, doc martins with explorer red socks - earphones playing Jesus & Mary Chain.
post #2144 of 3803
Quote:
Originally Posted by crdb View Post

I should probably point out that I'm the business guy of the company so take what I say with a pinch of salt :P
{{wink}}
post #2145 of 3803
Quote:
Originally Posted by clayb View Post


To go back to the topic, it has been raining all day pretty much every day of this week in Melbourne so far. Now that I have just bought a few new pairs of quality shoes, I wonder what do you guys usually do to protect your beautiful dress shoes? Overshoes or wearing some sort of boots on the road and then swapping to dress shoes in the office? I'm thinking of buying some Tingley or Totes overshoes from Amazon, but don't know if it's better to go for the Swims or something else.
WIWT
New Posts  All Forums:Forum Nav:
  Return Home
  Back to Forum: Classic Menswear
Styleforum › Forums › Men's Style › Classic Menswear › Australian Members - Part II - if you read the first post, you'll get what this is all about.