Machine Learning With BSL Demo Mac OS

• Machine Learning With Bsl Demo Mac Os Catalina
• Machine Learning With Bsl Demo Mac Os 11
• Machine Learning With Bsl Demo Mac Os X
• Machine Learning With Bsl Demo Mac Os Download

Core ML delivers blazingly fast performance with easy integration of machine learning models, allowing you to build apps with intelligent new features using just a few lines of code. Easily add pre-built machine learning features into your apps using APIs powered by Core ML or use Create ML for more flexibility and train custom Core ML models right on your Mac. Samples for using RevoScalePy and MicrosoftML packages. NOTE This content is no longer maintained. Visit the Azure Machine Learning Notebook project for sample Jupyter notebooks for ML and deep learning with Azure Machine Learning. Revoscalepy and microsoftml are machine learning libraries provided by Microsoft. They contain many battled tested and high performance machine learning. Machine learning is a form of AI that enables a system to learn from data rather than through explicit programming. However, machine learning is not a simple process. Starcraft demake mac os. As the algorithms ingest training data, it is then possible to produce more precise models based on that data.

Every company is sucking up data scientists and machine learning engineers. Slapfest! mac os. You usually hear that serious machine learning needs a beefy computer and a high-end Nvidia graphics card. While that might have been true a few years ago, Apple has been stepping up its machine learning game quite a bit. Let's take a look at where machine learning is on macOS now and what we can expect soon.

2019 Started Strong

More Cores, More Memory

Goodbye (zs laboratories) mac os. The new MacBook Pro's 6 cores and 32 GB of memory make on-device machine learning faster than ever.

Depending on the problem you are trying to solve, you might not be using the GPU at all. Scikit-learn and some others only support the CPU, with no plans to add GPU support.

eGPU Support

If you are in the domain of neural networks or other tools that would benefit from GPU, macOS Mojave brought good news: It added support for external graphics cards (eGPUs).

(Well, for some. macOS only supports AMD eGPUs. This won't let you use Nvidia's parallel computing platform CUDA. Nvidia have stepped into the gap to try to provide eGPU macOS drivers, but they are slow to release updates for new versions of macOS, and those drivers lack Apple's support.)

Neural Engine

2018's iPhones and new iPad Pro run on the A12 and A12X Bionic chips, which include an 8-core Neural Engine. Apple has opened the Neural Engine to third-party developers. The Neural Engine runs Metal and Core ML code faster than ever, so on-device predictions and computer vision work better than ever. This makes on-device machine learning usable where it wouldn't have been before.

Experience Report

I have been doing neural network training on my 2017 MacBook Pro using an external AMD Vega Frontier Edition graphics card. I have been amazed at macOS's ability to get the most out of this card.

PlaidML

To put this to work, I relied on Intel's PlaidML. PlaidML supports Nvidia, AMD, and Intel GPUs. In May 2018, it even added support for Metal. I have taken Keras code written to be executed on top of TensorFlow, changed Keras's backend to be PlaidML, and, without any other changes, I was now training my network on my Vega chipset on top of Metal, instead of OpenCL. Other os versions - redesign rebuild reclaim mac os.

What about Core ML?

Why didn't I just use Core ML, an Apple framework that also uses Metal? Because Core ML cannot train models. Once you have a trained model, though, Core ML is the right tool to run them efficiently on device and with great Xcode integration.

Metal

GPU programming is not easy. CUDA makes managing stuff like migrating data from CPU memory to GPU memory and back again a bit easier. Metal plays much the same role: Based on the code you ask it to execute, Metal selects the processor best-suited for the job, whether the CPU, GPU, or, if you're on an iOS device, the Neural Engine. Metal takes care of sending memory and work to the best processor.

Many have mixed feelings about Metal. But my experience using it for machine learning left me entirely in love with the framework. I discovered Metal inserts a bit of Apple magic into the mix.

When training a neural network, you have to pick the batch size, and your system's VRAM limits this. The number also changes based on the data you're processing. With CUDA and OpenCL, your training run will crash with an 'out of memory' error if it turns out to be too big for your VRAM.
When I got to 99.8% of my GPU's available 16GB of RAM, my model wasn't crashing under Metal the way it did under OpenCL. Instead, my Python memory usage jumped from 8GB to around 11GB.

Latest version of ios for macbook pro. When I went over the VRAM size, Metal didn't crash. Instead, it started using RAM.
This VRAM management is pretty amazing.
While using RAM is slower than staying in VRAM, it beats crashing, or having to spend thousands of dollars on a beefier machine.

Training on My MBP

The new MacBook Pro's Vega GPU has only 4GB of VRAM. Metal's ability to transparently switch to RAM makes this workable.
I have yet to have issues loading models, augmenting data, or training complex models. I have done all of these using my 2017 MacBook Pro with an eGPU.

I ran a few benchmarks in training the 'Hello World' of computer vision, the MNIST dataset. The test was to do 3 epochs of training:

• TensorFlow running on the CPU took about 130 seconds an epoch: 1 hour total.
• The Radeon Pro 560 built into the computer could do one epoch in about 47 seconds: 25 minutes total.
• My AMD Vega Frontier Edition eGPU with Metal was clocking in at about 25 seconds: 10 minutes total.

You'll find a bit more detail in the table below.

3 Epochs training run of the MNIST dataset on a simple Neural Network

Looking Forward

Thanks to Apple's hard work, macOS Machine Learning is only going to get better. Learning speed will increase, and tools will improve.

Machine Learning With Bsl Demo Mac Os Catalina

TensorFlow on Metal

Apple announced at their WWDC 2018 State of the Union that they are working with Google to bring TensorFlow to Metal. I was initially just excited to know TensorFlow would soon be able to do GPU programming on the Mac. However, knowing what Metal is capable of, I can't wait for the release to come out some time in Q1 of 2019. Factor in Swift for TensorFlow, and Apple are making quite the contribution to Machine Learning.

Create ML

Not all jobs require low-level tools like TensorFlow and scikit-learn. Apple released Create ML this year. It is currently limited to only a few kinds of problems, but it has made making some models for iOS so easy that, with a dataset in hand, you can have a model on your phone in no time.

Turi Create

Create ML is not Apple's only project. Turi Create provides a bit more control than Create ML, but it still doesn't require the in-depth knowledge of Neural Networks that TensorFlow would need. Turi Create is well-suited to many kinds of machine learning problems. It does a lot with transfer learning, which works well for smaller startups that need accurate models but lack the data needed to fine-tune a model. Version 5 added GPU support for a few of its models. They say more will support GPUs soon.

Machine Learning With Bsl Demo Mac Os 11

Unfortunately, my experience with Turi Create was marred by lots of bugs and poor documentation. I eventually abandonded it to build Neural Networks directly with Keras. But Turi Create continues to improve, and I'm very excited to see where it is in a few years.

Conclusion

Machine Learning With Bsl Demo Mac Os X

It's an exciting time to get started with Machine Learning on macOS. Tools are getting better all the time. You can use tools like Keras on top of PlaidML now, and TensorFlow is expected to come to Metal later this quarter (2019Q1). There are great eGPU cases on the market, and high-end AMD GPUs have flooded the used market thanks to the crypto crash.

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Machine Learning With Bsl Demo Mac Os Download

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2021/1/29
Homework 1
Homework 1
Programming Language BSL
Due Date: Friday January 29, 9pm Purpose To write simple functions. Expectations
This will be an individual (non-pair) assignment. For this and all future assignments you must upload one .rkt file in the language specified at the top of the assignment to the Handin Server. Failure to do so invalidates your submission and will therefore, unfortunately, result in a score of 0.
Unless otherwise stated, all functions you write must be accompanied by a signature and a purpose statement, in the format we studied in class.
Finally: Homework problems will sometimes end with questions like 'What conclusion should be drawn from these findings?', or 'Interpret this result!'. These are intended to encourage you to not just write functions and test them, but to use them to increase our knowledge and make informed decisions. After all, we are problem solvers! We will not grade what you write here (since such conclusions can be subjective), but we may take off points if you write nothing.
Exercise 1 Design a function poly1 that takes a single number as input and calculates the following polynomial. For this and the problem after this, translate the expression after the = into BSL¡¯s prefix notation, but do not otherwise rewrite the expression. Enter your code in the definitions window.
poly1(x) = (1/100)*x3 – (2/100)*x2 + 2x + 1
Exercise 2 Design a function poly2 that takes a single number as input and
calculates the following polynomial:
poly2(x) = 1 + x * (2 + x * (-2/100 + x / 100))
Now play with both functions in the interactions window, comparing the values they return for a number of inputs x. If you encoded the functions correctly you should find that ¡ª ? correct, the two BSL functions seem to compute the same mathematical
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2021/1/29 Homework 1
function. (If that is not what you are finding, go back and check your code for poly1 and poly2.)
Background: The form of the polynomial used in poly2 is known as Horner¡¯s scheme. Next time you are waiting at the bus stop, think about what advantages of the second form over the first form may be.
Exercise 3 Your friend claims he did the math on paper and believes the two polynomials are actually not the same. We could try to find his math error. But instead we want to test, on a large number of inputs, that they return the same value. (This is not a proof, but it is better than just testing a handful of cases by manual input.)
Here is a cool way to run those tests, along with some visualization.
(a) Define two constants of type Image. The first, called SCENE, defines an empty canvas of size 500×500. Use the empty-scene function (look it up in the doc). The second, called DOT, defines a solid circle of size 5, of a color of your choice (not white, please!).
(b) We are now going to visualize the difference |poly1(x) – poly2(x)| between the two polynomials. We expect it is zero (but your friend doesn¡¯t.) The |.| denotes the absolute value of a real number (find this function in BSL!).
Define a function poly-diff->scene that takes a single number x and produces an image. The image places DOT into the SCENE at position
(x,|poly1(x) – poly2(x)|+50)
The +50 is just to scale up the y coordinate a little. For the placement, use the
place-image function.
(c) How do we use this function? in an animation! Load the universe library near the top of your definitions window (where you also load the image library), run, and then type (animate poly-diff->scene) in the interactions window. If you did everything right, you should see ¡ª what?
Finally, introduce a 'bug': change poly2 so that it computes a different function than poly1, and run the animation again. One change that works well is: modify the '2 +' part to '3 +'.
To turn in: summarize your findings briefly in English. Just some buttons (n.o.p.e.) mac os. Is your friend convinced now?
Exercise 4 Suppose, purely hypothetically, you are taking a course that comes with 12 homeworks, and the homeworks together should constitute 30% of the total course grade. These two facts are non-negotiable.
The initial plan was to make all homeworks worth the same, so they each contribute 30%/12 = 2.5% to the course grade. But then the start of the class was delayed due to some pandemic, and the first class week turned out shorter. Accordingly, the first homework must be shorter and needs to count less than the other homeworks. Here are the exact rules:
The eleven homeworks HW02,…,HW12 all count the same.
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2021/1/29 Homework 1
The first homework, HW01, counts some percentage p of the other homeworks. For example, if p=50, then HW01 counts 1/2 of all the others (50%). The value of p must be a natural number in the range [0,100].
Design a function hw02-12 that takes p as input and returns the percentage that each of HW02,…,HW12 contributes to the total course grade, under the above rules. For example, if p=100, we have the special case that HW01 contributes the same to the total course grade as all other homeworks (the original plan). In this case, function hw02-12 should return ¡ª ? correct, 30/12=2.5.
This problem requires a little bit of mathematical modeling. Show your steps, so we can give you partial credit if you are off. To do that, add these steps in comments to your homework file. You can include a whole block of text as a comment like this:
#|
A multi-line comment.
This is line 2
|#
Exercise 5 Design a function hw01 that takes p as input and returns the percentage that HW01 contributes to the total course grade. For example, if p=100, we have the special case that HW01 contributes the same to the total course grade as all other homeworks (the original plan). In this case, function hw01 should return ¡ª ? correct, 2.5., as above. What about (hw01 0) ?
Hint: for this problem, do not reinvent the wheel. Consider how (hw01 p) and (hw02-12 p) are related.
Exercise 6 The homework creator designed these two functions and then went to the instructor, to ask her what value of p to use. But the instructor is tired and doesn¡¯t want to see a lot of numbers. So the homework creator offers her to present some data about the possible choices for p as a histogram. (Do a quick search on the web to see what a histogram is, in case you are not sure.)
The following is what we call a helper function for the histogram: Design a function histogram-bar that takes a number h as input and draws an outline of a rectangle of width 30, height 50*h-20 (yes, please use that exact formula; it simply scales the height (linearly) to make it easy to see), and a color of your choice.
Exercise 7 Now we are ready to construct a histogram. Design a function histogram
that takes eleven inputs (yes), call them p0,p1,…,p10. (These will be eleven possible
choices for value p from above.) Your function draws eleven vertical bars, next to each other in a row, aligned at the bottom. For i=0,…,10, bar number i is created using function histogram-bar, to which you pass the value returned by calling hw02- 12 on pi.
Note: A function with eleven inputs¡ªthat is pretty poor style, for several reasons. The most obvious is that we really want to draw a histogram for any number of given values, not only eleven! But we have seen only such a small fragment of the BSL language so far that we cannot do that yet.
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2021/1/29 Homework 1
For the signature of your function, you can denote the fact that it takes eleven inputs by I^11, but you need to replace I by the proper type of these inputs.
Exercise 8 Now it¡¯s time for some drawing. First test your function using the call (histogram 0 300 600 900 1200 1500 1800 2100 2400 2700 3000)
(yes, in this call the p values are not all in the range [0,100]). If you did it right, the top line of the histogram should roughly show the curve of a well-known and simple mathematical function. Ld47-loop robot mac os. Which function? If you cannot figure it out from the picture, look at the formula for function hw02-12, as a function of p.
Now we remember that the p values really should be percentages: how much HW01 counts as a fraction of the other homeworks. So here is a more realistic test case:
(histogram 0 10 20 30 40 50 60 70 80 90 100)
The homework creator presents this graph to your instructor. What conclusion should the two draw from this graph about the 'right' choice for p? Comparing the graphs obtained using the two test cases will help!
Before you submit: Make sure all your function definitions come with a signature and a purpose statement!
https://course.ccs.neu.edu/cs2500/hw1.html
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