The issue of precision is principally one of performance. While Wolfam Language can handle precise symbolic arithmetic and arbitrary levels of high-precision arithemetic, they are both much slower than floating point arithmetic which is effectively performed on the CPU while the others are in software. Since machine learning methods applied to big data are already very demanding and the difference between 10^-10 and 0 rarely makes a difference, the priorit goes to speed. Indeed some GPUs are designed to work at even lower "single" precision to enable more calculations per second.

There is also a second issue. Some of the training process is probabalistic, so you will acheive different results each time you run the training. For reproducibility one should control the random state with functions like SeedRandom (and some new tools coming in Wolfram Language 12.2) but since I didn't do this when I recorded the videos, we will likely never know how to reproduce the exact answer that I got.

thanks

Detecting the oldest and youngest in a picture.

https://www.wolframcloud.com/obj/tsukisan/LookingForExtremes

A final challenge

Create an deploy a simple machine learning app of your choice. Feel free to use one of our built-in classifiers or predictors.

To get you started, here is the example we looked at in the study group session:

CloudDeploy[
 FormPage[
  {{"myPicture", "Your picture"} -> "Image"},
  Module[{entity},
    entity = Classify["NotablePerson", #myPicture];
    Row[{#myPicture, Spacer[10], entity, Spacer[10], 
      entity["Image"]}]
    ] &,
  AppearanceRules -> <|
    "Title" -> "Which famous person do you look like?", 
    "Description" -> 
     "Enter your image and we'll show you your famous twin!!",
    "SubmitLabel" -> "Show me who"|>,
  PageTheme -> "Black"
  ],
 "FamousLookAlike",
 Permissions -> "Public"
 ]

Here's the simpler version for testing: https://wolfr.am/RddbHZR4

Thanks!!!! I totally didn't even look at the emails because I had the event saved in my calendar. My bad.

Where do we find the quiz for the course? I haven't seen an email with a link or a file for it. I want to try it before our final Q and A class tomorrow.

FeatureSpacePlot[
 DeleteDuplicates@
  TextWords[
   "The Wolfram Language includes a wide range of state-of-the-art \
integrated machine learning capabilities, from highly automated \
functions like Predict and Classify to functions based on specific \
methods and diagnostics, including the latest neural net approaches. \
The functions work on many types of data, including numerical, \
categorical, time series, textual, image and audio. 
   The Wolfram Language has state-of-the-art capabilities for the \
construction, training and deployment of neural network machine \
learning systems. Many standard layer types are available and are \
assembled symbolically into a network, which can then immediately be \
trained and deployed on available CPUs and GPUs. 
   Using a variety of state-of-the-art methods, the Wolfram Language \
provides immediate functions for detecting and extracting features in \
images and other arrays of data. The Wolfram Language supports \
specific geometrical features such as edges and corners, as well as \
general keypoints that can be used to register and compare images."], 
 FeatureExtractor -> 
  NetModel["GloVe 100-Dimensional Word Vectors Trained on Tweets"]]

thank you. I'm just now beginning to see how esoteric this topic is typically considered, yet how clearly it was evident while immersed in learning R just after your Summer Boot Camp sessions.

I believe yet another example was provided in today's lecture when we were evaluating predictive probabilities for boolean operators (or something like that) with values of .99998 accompanied by mean error of 1.00058 (or something like that) note: it's been awhile since I've taken a stats class.

So... perhaps a more precise version of my question for Friday is: If we accept that these tiny values are, in fact, measurements of computational error with magnitude attributable to specifications of the system which had computed it; and we are willing to admit these values of something times 10 to the negative 50th power are meaningless aside from representations of computational processor error...

why not just round it zero? or perhaps some symbolic feature of irrelevance? continued use seems to create user confusion from inconsistent outputs and higher risk of error propagation than benefit.

looking through the documentation I saw some functions which appear to effectuate this under the heading of Precision & Accuracy Control, but why not make such settings default? or default for a particular user type? (like "student/learner") then everyone in a class would receive similar results regardless of what type of computer they are using.

Unfortunately it appears that PredictorMeasurements cannot deal with test data being in a Dataset. We will have to wrangle it into key-value pairs {feature1, feature2,...} -> target

For this specific dataset we can do the following:

modifiedTesting = Most[#] -> Last[#] & /@ Values[Normal[testing]]

which will set up the test data as: {date, text of address}-> age and allow you to use the PredictorMeasurements function.

A new study group covering the basics of machine learning begins Monday! This group will be led by Wolfram certified instructor @Abrita Chakravarty and meets daily, Monday to Friday for one week. Abrita will share the excellent short lesson videos created by @Jon McLoone for the Wolfram U course Zero to AI in 60 Minutes. Study group sessions include time for additional examples, exercises, discussion and Q&A. Certification of program completion is available. Sign up: https://wolfr.am/R2s6PERk

This is being automatically done by the Classify or Predict function. You will observe it in the training progress panel as the training happens. You will see it is trying different algorithms.

I run into some trouble with the format of DataSet. The following runs without errors and trains fairly:

addressData = ResourceData["State of the Union Addresses"];
addressEssensialData = addressData[All, {"Date", "Text", "Age"}];
{training, testing} = TakeDrop[addressEssensialData, 200];

Note that at this point:

In[16]:= training // Head
Out[16]= Dataset

Next, I can successfully use variable training in the following way

p = Predict[training -> "Age"]

Further, I am OK with getting predictions and actual data "by hand":

predictionsOnTesting = p[testing]
actualsOnTesting = testing[[All, 3]] // Normal

And further I can plot their comparison plot (also "by hand")

However, if I try feeding testing into PredictorMeasurements[..]

pm = PredictorMeasurements[p, testing]

or similarly as it worked earlier with Predict[..]

pm = PredictorMeasurements[p, testing->"Age"]

It runs with the following error

PredictorMeasurements::bdfmt: Argument Dataset [<<33>>] should be a rule or a list of rules.

And usually crashes Mathematica if I try doing anything further. Though one time I somehow managed:

I am a bit at a loss how to properly figure out PredictorMeasurements[...] with DataSet variable for the training set... (I also tried turning DataSets into Lists but this seems wrong and I didn't have much success with it either)

Attachments:

MLChallenge2.nb

Thank you! That worked perfectly!

I'm wondering about slide 7 of the presentation where it mentions automating the method choice by testing different approaches on subsets of data - do you have any examples of this?

Thanks!

Here's my attempt at analyzing the sentiment of "Pride and Prejudice":

prideAndPrejudice = 
  TextCases[ResourceData["Pride and Prejudice"], "Sentence"];

sentiment = 
  Classify["Sentiment", prideAndPrejudice, 
   "Probability" -> "Positive"];

MovingAverage[sentiment, 500] // ListLinePlot

Hi,

I'm confused about how to import excel files into the "{parameters}->label" format. My excel file looks like the picture below, with some of the data being blank.

Pretty fun. I have used The Picture of Dorian Gray. After taking off sentences of less than 3 words (arbitrary decision...) I have used a lowpassfilter with very low cuttoff. Funny part, the graphs showed a high probability of a neutral end (??). After reviewing the text I realized that the end of the text was license agreement. After removing that, the end of the book could be positive too.... In the images green is positive, red is negative and orange is neutral.

ListLinePlot[
 Classify["Sentiment", 
  TextSentences[ExampleData[{"Text", "AliceInWonderland"}]], 
  "Probability" -> "Positive"]]

Looking forward to reviewing these topics along with you all. Please feel free to post any questions you may have, from the course or from the daily study group sessions, here. Also look out for the fun daily challenges to showcase your new machine learning skills.

See you on Monday.