Thanks a lot :-) Also, the links that are posted during the webinar in the chat, are unavailable for those who cannot attend at the live webinar, but like me, have to watch later. Please provide us as well with the link to the VideoClass "building apps with Neuralnet Depositery" with Megan Sheer. Thanks.

I always have to watch the recording of the webinar, as I can never attend, unfortunately. But the last webinar on may 6th is not playable. I can reach the page, but all I get "The webinar ended" and no possibility to play the recording. Even with my VPN switched off.

I would appreciate a valid link.

Thanks.

Hello,

Thank you for organizing this series!!

I was wondering whether there is a NN model that is trained to scan text from a pdf and do character recognition to obtain a text document from it. More specifically, what I would like to do is scan old typewritten mathematical text and produce a latex document from it. But a first step is to be able to do character recognition from a pdf'ed text. Or is there a function in Mathematica that can do this? For the kind of mathematical text I am trying to recognize and put into LaTex, I am uploading a little piece of it (source: AMS)

Attachments:

example.PNG

Thank you Abrita!!

@B. Cornas are you refering to the example using NetModel["Wolfram ImageIdentify Net V1"] on slide 22 (that classifies the image of a tiger and looks at the output from the intermediate layers of the neural network)?

If yes, then it is possible to perform similar Image Classification tasks with out-of-core images. The Neural Net Framework is able to work with large images that may need to stored out-of-core.

An example can be found here: https://www.wolfram.com/language/11/neural-networks/out-of-core-image-classification.html where you will notice that the training data is an association of File expressions (a symbolic representation of a location in the local file system) and labels instead of the actual image itself.

The trained network is also used to classify images directly from file paths:

Here is the blog post (sorry not community post) I was referring to in today's session: https://blog.wolfram.com/2021/01/07/deploy-a-neural-network-to-your-ios-device-using-the-wolfram-language/

@Mitchell Sandlin Unfortunately we do not seem to have such a model in our neural net repository, but I found a couple of academic papers that might be of interest (both seem to use Convolution Neural Nets):

• https://www.mdpi.com/2072-4292/11/19/2326/htm#B33-remotesensing-11-02326
• https://www.nature.com/articles/s41598-022-08571-9

Hi Patrick, The "lenet-CIFAR-10.wlnet" file has been included in the zip file that you downloaded. Could you please confirm that after you unzipped the download, the notebook "ExploringNNFramework-BuildingToTraining.nb" and the file "lenet-CIFAR-10.wlnet" appear in the same location?

If yes, then

Import[FileNameJoin[{NotebookDirectory[],"lenet-CIFAR-10.wlnet"}]

should be able to load the simple network modeled after Lenet into your session and you can work further with it.

Yes the model is slightly different from the network shown on the slide 4. Here is the code to create it yourself:

lenet = 
   NetChain[{
      ConvolutionLayer[20, 5], 
      Ramp, 
      PoolingLayer[2, 2], 
      ConvolutionLayer[50, 5], 
      Ramp, 
      PoolingLayer[2, 2], 
      FlattenLayer[], 
      500, 
      Ramp, 
      10, 
       SoftmaxLayer[]
   }, 
  "Output" -> NetDecoder[{"Class", classes}], 
  "Input" -> NetEncoder[{"Image", {32, 32}}]]

You can also find it in the tutorial at https://reference.wolfram.com/language/tutorial/NeuralNetworksComputerVision.html#280210622 within the example demonstrating "CIFAR-10 Object Classification".

First, thank you for the presentation earlier today.

lenet is used throughout the presentation notebook. However, when I run the notebook from the beginning I'm getting errors.

At the end of the notebook there is an Initialization cell:

trained=lenet=Import[FileNameJoin[{NotebookDirectory[],"lenet-CIFAR-10.wlnet"}]];

I have some questions.

Is the lenet-CIFAR-10.wlnet an empty network that is assigned to trained and lenet?

Is the file essentially the NetChain statement on slide 4?

Hi! Why I'm I getting this error when evaluating the code in "Human in the Loop: Interpretable Machine Learning" notebook?

Hi! 1. I'm using 12.0.0.0 version. 2. Yes, as you can see in the picture attached 3. The formatting you are suggesting is working 4. Can I ask one more question. What is the difference between LearnDistribution and Predict? Can I get the same probability distribution contour plot (for 2D data) with Predict as for LearnDistribution?

It is helpful to think of the problem as attempting to answer a question of s specific type and then choose the function to help answer it. Classify is good for answering questions like "Is this A or B (or C or D or E)?" while Predict is good for answering "How much or How many?"

So if you want to forecast the attendance of a person as "attended" or "unattended" (Is this A or B), Classify would be the way to go. While "attended" is represented by 1 and unattended is represented by 0, they are still categorical or nominal features, NOT numeric features.

If you want to forecast "how many" of the events the person is likely to attend, Predict would be the way to go.

Looking forward to the next session in the Data Science Friday series: "Supervised and Unsupervised Learning" on Friday, April 8 at 1:00 PM Central Time (US & Canada) (GMT -5:00)

See you there.

May I ask when using machine learning in Mathematica?
Do we need to check the data distribution beforehand (or even convert it to a normal distribution) like traditional statistics?
Is it the same when using directives like LinearModelFit?

Sorry for the confusion about where you can download presentation notebooks. We will include the link in all upcoming reminders, so it's easy to find. A reminder will be sent today, so please watch for that in your inbox. All the notebooks for the full series will be placed in a single location, so you can download the latest each week.

We want to be sure everyone who is interested is signed up for the series, so that you receive all the details you need. You will receive reminder emails, recording notifications and further resources. Sign up for Data Science Friday Webinars.

@Charles Glover and @John Snyder we will send out an event reminder email with the link to the download folder containing all the notebooks from the entire webinar series.

@Abrita Chakravarty, I missed the link to the notebook for this presentation. Would you please post it.

Thanks

The "Machine Learning and Statistics: Better Together Talk" by @Jon McLoone on Friday April 1 at 1:00 PM CT had many technical errors during the presentation. It would be nice to get a cleaned up version via email to watch later. In other words, please fix the audio and video by re-recording the webinar if you don't mind. I don't mind you extending the webinar next week by 30 minutes, but in the release of the webinar for later viewing, please take the time to fix the presentation by elimination of the technical problems from the video and replace these sections with the material that the presenter meant to present. Thanks!

I missed the download link of the notebook for the "Machine Learning and Statistics: Better Together Talk."
Can you help! Thanks!

Hello, @Luis Antonio Gonzalez, I see that you signed up yesterday and that you missed the notebook link. I will send that to you by email. I also encourage you to register for the full series, so that you receive reminders and emails with links to all the upcoming topics. Sign up for the Data Science Friday webinar series here.