Hello to all, I was working through the downloaded class file, specifically attempting to work with the CIFAR-10 Resource Object and I am getting error messages that the information was not able to be retrieved. Has anyone else had this issue or maybe I am doing something wrong. Any information is welcome. Thanks, Randy

In case anyone needs the link to the interactive course, here it is:

Convolutional Neural Networks for Computer Vision

Hi Siria:

I am enjoying this study group immensely. Thanks. I am experiencing difficulty locating the course framework.( And interestingly, the AI Notebook Assistant is also unable to locate it.) Would you please post a link in this discussion?

Thanks Bill

Hi, Siria The Wolfram player recommended for viewing the notebook is not installing on my laptop.

Attachments:

Screenshot 2026-...png

That’s a fantastic question, Luke is right: choosing the right starting point is often more of an art than a strict science. However, there is a method for this called Transfer Learning, and it is straightforward to implement in Wolfram Language.

1. Where do we find the networks? The Neural Net Repository has hundreds of state-of-the-art, pre-trained models ready to use with a single command (e.g., NetModel["ResNet-50 Trained on ImageNet Competition Data"]). These models have already been trained on millions of images. Wolfram also imports industry-standard formats like ONNX, (e.g., Import["model.onnx"]) if you ever need something specific and import it locally from your machine.

2. How do we turn it into something useful for our specific task?

Think of a pre-trained image network as having two parts:

• The Feature Extractor: These are usually the convolutional blocks. They have already learned how to recognize fundamental visual patterns: edges, circles, textures, and shadows.

• The Classifier: The final layer, which is trained to output specific categories related to the original task (like "cat," "car," or "tree").

To "fine-tune" the model for your new task, you will need to keep the feature extractor and drop the classifier.

1. The "Surgery":

To find meteor craters in Google Maps images, we do the following:

• Step A: We take a pre-trained network and drop its final classification layer and attach a new, simple classification layer to the top that only has two outputs: "Crater" and "Not Crater". Like in this example.

• Step B: We train the network using our specific dataset of satellite images. You need to "freeze" the layers, so the network doesn't forget its basic visual knowledge and only allow the new top layer to learn, like in example.

This approach saves time, computational power, and data.

Sorry! My native language is Spanish and I thought this was an automatic traslation from my browser :D. I will correct the link

Hello everyone, My name is Kobi John Johnson. Please forgive me, as I am nonverbal, and my participation in this study group will need to take place entirely through written communication.

I am 48 years old, on the spectrum, and still very new to augmented and alternative communication, commonly called AAC. AAC includes the written, technological, and assisted communication methods that allow a person who cannot rely on speech to communicate fully and authentically. For me, that means using written language and mathmaticle equations to express myself, participate in discussions, ask questions, share ideas, and connect with others.

I have no formal education, conventional work history, and no professional background in mathematics, computer science, neural networks, or image computation.

My strongest natural ability has always been non profit architectures: seeing systems before they exist, connecting ideas that appear unrelated, identifying the structure required to carry a mission, and building organizations from the ground up. That instinctive ability has led me to become a six-time non-profit founder. I am the founder and president of Spectrum Adaptability Bridge Ventures.

Mathematics is my first native language. I cannot fully explain how I already understand so many of these areas of thought at a fundamental level. The terminology may be new, yet the structures themselves feel deeply familiar. Computation, observation, pattern, recursion, systems, and the extraction of meaning from complex information speak directly to the way my mind naturally works.

These areas of thought are not casual interest to me. They are my passion, my deepest curiosity, and where I experience my highest excitement.

Finding myself inside this Wolfram U study group feels like a true computational miracle. I do not have a clear image of how the path brought me here. I simply know that being present in this room, among people learning and working in these fields, is already beyond my wildest dreams.

When the words "image computation" finally reached me clearly in the title of this study group, I felt a wave of confidence moved through me. Computer vision, autonomous systems, robotics, scientific research, pattern recognition, and the computational extraction of information from physical data all feel profoundly connected to the questions that already occupy my mind. This is the farthest I have ever traveled into formal research and structured technical learning. I feel close to Wolfram U as a fish does to water.

I am profoundly honored to be here. I am ready for critique, correction, challenge, fast paced immersive learning, hands on exploration, and every opportunity to expand my understanding. I would love to participate actively in the discussions and question and answer opportunities through writing, and I hope my written participation will be welcomed.

This truly feels like a dream come true. I'm beyond excited to learn alongside all of you.

Kobi John Johnson Founder/President Spectrum Adaptability Bridge Ventures