Aren’t they the same thing?

Thanks a lot, gonna try it!

Practical Deep Learning python by Kneusel , Dive into Data science, Practical TensorFlow

Compared to Andrew Ang’s specialization, that text is pretty advanced (though a classic). You might have better luck with Dive Into Deep Learning first.

The first link seems to have no code. But for the others just click on "code". Then you can sort the public notebooks by "most votes", "best score", etc. and have a look at them.

Thank you everyone!

can’t agree more. This is really an amazing book.

It depends on how similar the images are and on the amount of defect data, i.e. images containing cracks. You can rephrase it as image segmentation problem and use U-nets (without attention) as model. Due to the limited receptive fields of the individual "neurons" the dataset will effectively be much larger. If the input data is already aligned you could also try using the difference in feature space, i.e. calculate the difference in the activations of a pretrained network for non-defective images and the current image. I'd suggest using cosine distance.

Data driven science and engineering book by Brunton

Dive into Deep Learning. Free and up to date. https://d2l.ai/

Will look into these. Thank you!

Deep Learning book - Courville, Bengio, Goodfellow

Andrew Ng ML and DL specializations on Coursera

One thing people haven't mentioned is you could create synthetic images via 3D modelling. If you can get someone to set up realistic 3D models of those microchips, and then randomly generate cracks, you can get a pretty good baseline model you can then finetune on real data.

There are companies which could do that too but I'm not that sure that the price would be approachable, or if outsourcing it is a viable solution given trade secrets. But ex. Datagen is a company that can do it.

You can try. Augment hard, use a pretrained network and freeze everything except the last layer, and don't let anyone actually try and deploy this thing. 100 images is enough to do a small proof-of-concept, but nothing more than that.

With such a small dataset, you should use pre-existing classification models most similar to your data (search huggingface), and then re-train just the last layer or last couple of layers ("freeze" all the prior layers). And yes you can use the data augmentation suggestion but if you build the entire network from scratch it will be challenging to get good results. AKA "transfer learning".

How can I see the code? Of the first link for example?

For introduction watch Andrew Nga course about Deep Learning on YT and then do what @EnlightenMePIss suggests

😂😂😂😂

I've heard of it when I started delving into deep learning, but it seemed too complex for me at the time. I'll check it out!

Sweet! Thanks a lot!

At neural networks specifically haha!

Haha and you claim to be a noob 😅

I would try without data augmentation first. You need a baseline to understand what helps and what doesn’t to increase performance. If there is a strong signal that can differentiate between the classes, 100 images may be enough. The amount of data you need is problem dependent it’s not a one size fits all. As others have said make sure youre splitting into train and test sets to evaluate performance and that each has a distribution of classes similar to the overall population (matters if you have an imbalanced dataset). Keep the network lightweight if you’re not using transfer learning and build it up from there. At a certain point it will overfit but it will most likely happen faster the larger your network is.

Yeah as long as your VM is in the same region as the bucket it should be fine. Even if you have 200GB it doesn't take that long to move between regions either

Wandb is the best! https://wandb.ai/

Check the hyperparameter sweep option. It is FANTASTIC!
you can set range/values for each hyperparameter and let it run.