Over the past few years, much of the progress in deep learning for computer vision can be boiled down to just a handful of neural network architectures.Hacking Elance The Step by Step Guide to How I Made 2.Weeks.At 2.SAT test prep company.Things were going great and business was going well.But preparing students for a comprehensive exam is no easy task.Clasificacion Del Software Y Hardware Pdf Merge '>Clasificacion Del Software Y Hardware Pdf Merge .Download Your Free Pantone Plus Color Library For Pc Or Mac For Photo '>Download Your Free Pantone Plus Color Library For Pc Or Mac For Photo .I/51fvKuwV-IL.jpg' alt='Guide To School Network Hacking Image' title='Guide To School Network Hacking Image' />Its actually quite exhausting.Gradually I began to wonder if there was anything I could do that would a.Allow me to work remotely, so I could take my work with me and have much more freedom.Pay me more money in less time.I thought about it for a few weeksand then it came to meHey, I can make a basic website.Maybe I can make money doing that.Just the idea of a fresh pursuit was getting me excited.With the test prep business, I was always driving to clients.So the idea of being able to collect money with morning breath was extremely appealing.My girlfriend wasnt going to be crazy about the breath, but shed love the money.I started thinking about all the obvious pros.It was a viable skill set.Everyone needs a website these days, right I could make my own hours and work from anywhere I wanted to.And even if there were some things I couldnt handle at first, the learning curve was gradual enough for me to actually learn on the job.Everything seemed perfect.I figured I could just hop on Elance a site for remote freelancers, book some quick design jobs and get my business flowing.Oh, Daniel.Youre so nave it hurts.I created my account, logged on to look for jobs and then.I was in a waiting room with 2 million other freelancers from foreign countries.And they were all charging less.Id have a better shot at Kim Kardashian finally responding to all those letters Ive been sending her.As you read through this guide, think about it from your own perspective, with your own skills in mind.Web design is just an exampleplaceholder that can be changed out for almost any other skill set that you choose to leverage.So how did I finally break through Keep readingIts not as easy as showing up.Outsourcing is one of the biggest challenges facing Americans in the growing international workforce.In every field, from manufacturing to technology, someone with a comparable or superior skill set is willing to do the same work as you for drastically lower cost.Its just the way things are these days.The minute I logged on to Elance, I was met with the crushing realization that there were literally over 2.This was going to be much harder than I thought.How was I going to get clients when I was competing with all the freelance designers in the world not on the value and quality I provided, but on price I couldnt compete on price.My rent is 1,1.I really couldnt afford to spend hours on a 3.I was at a loss.And then, it occurred to me I needed to become a premium service provider.Is Mercedes Benz bashful about charging 8.E Class I think not.They are widely perceived as a luxury brand and come with ridiculous customer service to justify their price point.Thats the bracket I needed to aim for.But was it even possible Could I even do something like that on Elance I hoped so.First, I needed to test my assumptions.Think about how you can apply testing to your unique situation.Setting up the test.I designed a test to answer two primary questions Exactly what strategies do my successful competitors use to stand out in the crowd and thenHow can I completely obliterate them by being ridiculously overpreparedTesting assumptions had worked pretty well for me in setting up the test prep endeavor setting up small classes first, tweaking them and seeing what worked.But until I actually went out there and did it, I didnt truly understand the value of feedback.Now I know that feedback is literally the difference between success and failure.Testing allows you to determine if a business will work without risking failure.Heres how I structured the test 1.I set up a dummy account in order to create a fake posting looking for web developers.The purpose behind this was to find out exactly what types of proposals other developers were submitting.Heres what the posting looked like 2.None of the copy in the ad is random.Everything has a strategic purpose, meant to find something out about my competition.A few tactical things to notice here The budget is high.According to Elance stats, most jobs go for around 1,0.So why put the price point so highI wanted to attract the best possible candidates.What Ive found anecdotally and through personal experience is that high prices often scare off underperformers.Its part of the whole the cream rises to the top mentality.I wanted to see which contractors identified themselves as worthy of a 1.Theoretically, these should be the best proposals.The job is marked as fixed price I wanted to see what rates they would throw at me and what negotiation tactics they would use.I was very clear with my needs and the range of skill sets required to do the job.Ironically, these are the skill sets that I had and was trying to leverage, so I was looking for people with identical credentials to see what I was up against.I sat back and popped a bag of Orville Redenbacher as things began to get interesting.The results are inWithin 3.I received 7.All things being equal, this means that each applicant had a 1.Of course, my goal was to figure out how to shift these odds dramatically, but more on that later.Take a look at the breakdown by region Now its time to put ourselves in the shoes of a prospective client.So just based on initial impressions, before reading any of the actual proposals that were submitted, here are my observations The lions share 5.India and South Asia.North American applicants constituted about 2.The rest of the world made up the last 2.Now compare the sample data above with the lifetime hiring data provided by Elance With over 1.North America completely blows every other country off the map.The next closest is Australia, with barely over 1.If we take a step back and think about what this means, its pretty easy to spot an imbalance between the types of people applying for jobs and the ones doing the hiring.English speaking Americans do mostly all the hiring and every other country does almost all the labor.Its actually a pretty familiar pattern, dont you think Native English speakers WANT to work with other English speakers who can easily understand their needs.The problem most American freelancers run into on Elance is that since their rates are naturally higher due cost of living, they miss out on jobs by getting ruthlessly lowballed by foreigners using the volume approach.I knew that the Americans doing the hiring WANTED to hire other Americans, but were resistant to higher American prices.I was attempting to find out how could I remove this objection and make price a non issue.Reading the proposals.Id already learned a ton of information just looking at the distribution curve of applicants, but now it was time to do the actual dirty work read the proposals.Remember my first objective figure out exactly what the successful competition was doing.When I opened my Elance inbox, the first feelings I had were those of nausea.I knew right away that I didnt want to and WASNT going to read through all 7.I just couldnt.But I did notice certain elements of proposals that made them stand out.Heres what I found that helped me narrow down which ones I would even bother reading Sponsored Proposals freelancers can buy monthly credits, which they use to be able to submit more proposals.These credits can also buy a Sponsored Proposal which sticks to the top of the page.No matter how many bids the job gets, their proposal will stay at the top.Only 3 contractors per job may be sponsored.I always looked at these for two reasons First, I knew they were already making a small investment in me by paying to show their bid.Second, with almost 1.CopyPitch if they hooked me in the first line or two of their proposal with something interesting, Id read the whole thing.This got harder as I went along, so it helped if they were on of the first 2.Specific reference to the project I posted, not a generic copy paste job.This also gave me a good idea of how proficient they were at English.I just dont feel like dealing with a communication barrier.Price point this is important, but for different reasons than you may expect.If someone was ridiculously low, Id instantly forget about them.An Intuitive Guide to Deep Network Architectures Towards Data Science Medium.Over the past few years, much of the progress in deep learning for computer vision can be boiled down to just a handful of neural network architectures.Setting aside all the math, the code, and the implementation details, I wanted to explore one simple question how and why do these models work At the time of writing, Keras ships with six of these pre trained models already built into the library VGG1.VGG1.Res.Net. 50.Inception v.Xception.Mobile.Net.The VGG networks, along with the earlier Alex.Net from 2.Mobile.Net is essentially a streamlined version of the Xception architecture optimized for mobile applications.The remaining three, however, truly redefine the way we look at neural networks.This rest of this post will focus on the intuition behind the Res.Net, Inception, and Xception architectures, and why they have become building blocks for so many subsequent works in computer vision.Res.Net.Res. Net was born from a beautifully simple observation why do very deep nets perform worse as you keep adding layers Intuitively, deeper nets should perform no worse than their shallower counterparts, at least at train time when there is no risk of overfitting.As a thought experiment, lets say weve built a net with n layers that achieves a certain accuracy.At minimum, a net with n1 layers should be able to achieve the exact same accuracy, if only by copying over the same first n layers and performing an identity mapping for the last layer.Similarly, nets of n2, n3, and n4 layers could all continue performing identity mappings and achieve the same accuracy.In practice, however, these deeper nets almost always degrade in performance.The authors of Res.Net boiled these problems down to a single hypothesis direct mappings are hard to learn.And they proposed a fix instead of trying to learn an underlying mapping from x to Hx, learn the difference between the two, or the residual.Then, to calculate Hx, we can just add the residual to the input.Say the residual is FxHx x.Now, instead of trying to learn Hx directly, our nets are trying to learn Fxx.This gives rise to the famous Res.Net or residual network block youve probably seen Res.Net block.Each block in Res.Net consists of a series of layers and a shortcut connection adding the input of the block to its output.The add operation is performed element wise, and if the input and output are of different sizes, zero padding or projections via 1x.If we go back to our thought experiment, this simplifies our construction of identity layers greatly.Intuitively, its much easier to learn to push Fx to 0 and leave the output as x than to learn an identity transformation from scratch.In general, Res.Net gives layers a reference point x to start learning from.This idea works astoundingly well in practice.Previously, deep neural nets often suffered from the problem of vanishing gradients, in which gradient signals from the error function decreased exponentially as they backpropogated to earlier layers.In essence, by the time the error signals traveled all the way back to the early layers, they were so small that the net couldnt learn.However, because the gradient signal in Res.Nets could travel back directly to early layers via shortcut connections, we could suddenly build 5.At the time, this was a huge leap forward from the previous state of the art, which won the ILSVRC 2.Res.Net is one of my personal favorite developments in the neural network world.So many deep learning papers come out with minor improvements from hacking away at the math, the optimizations, and the training process without thought to the underlying task of the model.Res.Net fundamentally changed the way we understand neural networks and how they learn.Fun facts The 1.I would not really recommend you try re training it, butIf youre feeling functional and a little frisky, I recently ported Res.Net.Clojure ML library Cortex.Try it out and see how it compares to KerasInception.If Res.Net was all about going deeper, the Inception Family is all about going wider.In particular, the authors of Inception were interested in the computational efficiency of training larger nets.In other words how can we scale up neural nets without increasing computational costThe original paper focused on a new building block for deep nets, a block now known as the Inception module.At its core, this module is the product of two key insights.The first insight relates to layer operations.In a traditional conv net, each layer extracts information from the previous layer in order to transform the input data into a more useful representation.However, each layer type extracts a different kind of information.The output of a 5x.At any given layer, how do we know what transformation provides the most useful information Insight 1 why not let the model choose An Inception module computes multiple different transformations over the same input mapin parallel, concatenating their results into a single output.In other words, for each layer, Inception does a 5x.And the next layer of the model gets to decide if and how to use each piece of information.The increased information density of this model architecture comes with one glaring problem weve drastically increased computational costs.Not only are large e.And this increase becomes a deadly bottleneck in our model.Think about it this way.For each additional filter added, we have to convolve over all the input maps to calculate a single output.See the image below creating one output map from a single filter involves computing over every single map from the previous layer.Lets say there are M input maps.One additional filter means convolving over M more maps N additional filters means convolving over NM more maps.In other words, as the authors note, any uniform increase in the number of filters results in a quadratic increase of computation.Our naive Inception module just tripled or quadrupled the number of filters.Computationally speaking, this is a Big Bad Thing.This leads to insight 2 using 1x.In order to solve the computational bottleneck, the authors of Inception used 1x.A 1x.For example, using 2.By reducing the number of input maps, the authors of Inception were able to stack different layer transformations in parallel, resulting in nets that were simultaneously deep many layers and wide many parallel operations.How well did this workThe first version of Inception, dubbed Goog.Le.Net, was the 2.ILSVRC 2.I mentioned earlier.Inception v.In v.Inception rapidly became a defining model architecture.The latest version of Inception, v.Inception Res.Net hybrid.Most importantly, however, Inception demonstrated the power of well designed network in network architectures, adding yet another step to the representational power of neural networks.Fun facts The original Inception paper literally cites the we need to go deeper internet meme as an inspiration for its name.This must be the first time knowyourmeme.Google paper.The second Inception paper with v.Res.Net paper. 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