Ah… That one.
I just checked out the latest syllabus of IBM, and found that it has changed quite a lot. However, I am not able to see the latest list of items because my account is locked to only display the items for the old version of the IBM courses. If I just look at the syllabus (which may not be accurate), I don’t think it is very machine learning centric.
DLS is certainly ML (neural network) centric. The DLS has 5 courses.
The first half of the 1st course
- is like a bridge that will go through similar topic as MLS course 1 & 2 but more maths-oriented
- introduces notations that will be used in the rest of the DLS
The second half will cover
- backprop and vectorization that were optional in MLS
- deep neural network
The 2nd course will cover
- the concept of bias/variance and regularization techniques
- advanced gradient descent algorithms
- hyperparameter tunings
The 3rd course will be mainly about general concept of how to improve model through its development cycle, and some other topics like transfer learning.
The 4th course is about CNN and will introduce some popular architectures and their rationales.
The 5th course is about RNN and transformer.
Now, your goal is the following:
To actually train some model,
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first, you need to know what techniques you can apply in a model which I believe course 2 will give you what every ML practitioner should have in their toolbox.
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second, you need to know how to evolve your model configuration which will be gone through in course 3.
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third, you choose the data of interest. If it is image data, then you want to finish course 4. If it is sequential data, then course 5. If it is tabular data, then course 1 will be already sufficient. However, I am not suggesting that we can skip course 1 if you are just interested in image data, instead, I would still recommend you to go through the courses in order.
Lastly, you might give yourself a small challenge even before you start DLS. I had a discussion with another learner in this forum. They had a simple dataset and trained two models with two methods - one is sklearn’s SGDRegressor, and another one is their own gradient descent algorithm which was very similar to the MLS’s. The learner came with some questions but then later on the challenge became how to match the results from both methods, and this is what I want to tell you about. To match them, we need to know the model’s hyper-parameters well, such as the learning rate. There are also other things we need to know and is beyond MLS but certainly managable. There could be a lot more interesting discussion points if the learner had time to keep moving on, but the learner had to stick to their plan so we stopped. If you are interested in what we have discussed and how the learner did it, you might read from the 4th post of this thread which this link will bring you to.
Cheers,
Raymond