Natural Language Processing: NLP With Transformers in Python (Udemy.com)

So detailed and so patient!

Not much applicable to Administrators especially application DB and SAp Basis consultants

What Went Well? I successfully connected the different stages of the training pipeline. I understood how to create a DataLoader with batching and shuffling. I learned how to prepare the model for training by moving it to the correct device, setting training mode, and configuring the optimizer. I gained experience implementing a training loop that handles both MLM and NSP objectives simultaneously. I became more familiar with the structure of Hugging Face BERT model inputs and outputs. What Could Be Improved? I could strengthen my understanding of how MLM labels are generated and how tokens are masked during preprocessing. I would like to explore the individual MLM and NSP losses in more detail rather than only using the combined loss. I could improve my debugging skills for common training issues such as incorrect tensor shapes, label mismatches, and device errors. More practice with hyperparameter tuning (learning rate, batch size, epochs) would help optimize model performance.

very nice sessions it is useful to every learner

The engagement of the tutor can be improved. Mind many times fluctuates because of the monotonous tone of the tutor. The content is brilliant but need more engaging examples to make it a better tutorial for neural networks as more interesting topic.

It was good knowledge sessions

Good Content and definitely will be helpful to match the work in the role.

it's interesting, not sure if I can install all the SW due to company policies

Very good learning experience

In this course, we cover everything you need to get started with building cutting-edge performance NLP applications using transformer models like Google AI's BERT, or Facebook AI's DPR.