What I learn from Learning Machine Learning Developmen

ML Course at Dicoding

Belajar Pengembangan Machine Learning

I want to tell, what the criteria that you need to achieve to finish this course. This course have name “Belajar Pengembangan Machine Learning”, in English we can say Learning Machine Learning Development. I will publish in here what I submit on that course. In this course, need 3 types of submission. The first submission about NLP, the second submission about Time Series, and the third submission about Image Classification. Each submission have rate criteria, the highest rate will have high criteria to achieve and what you should know, the highest rate will bring you to achieve what Tensorflow Certification test. I think this course really worth to get and learn. Learning Machine Learning Development really make me know more how to use Machine Learning and give me some good technical “hack” that you can use to give you better result. I have learned some Machine Learning at my Undergraduate Courses but this is really different, give you more technical into the Tensorflow itself, but I’m sure this also not only about Tensorflow.

Machine Learning Experiment

First Submission — NLP

You can see my first submission here. The criteria to get highest rate in this submission are,

  • Must use LSTM in model architecture.
  • Must use a sequential model.
  • The validation set is 20% of the total dataset.
  • Must use Embedding.
  • Must use tokenizer function.
  • The accuracy at training set and validation set of the model is more than 90%.
  • Using callback
  • Draw plot loss and accuracy at training and validation.

Second Submission — Time Series

Here the second submission. I’m sure, this is also bring me safe case. In this submission, I use Multivariate Time Series as my dataset, but actually the submission only need the Univariate. Seems I must learn more how to process the Multivariate, I know my solution not really good.

  • Must use LSTM in model architecture.
  • The validation set is 20% of the total dataset.
  • The model must use a sequential model.
  • Must use Learning Rate in Optimizer.
  • Have to implement callback
  • Give plot of loss and accuracy at training and validation.

Third Submission — Image Classification & Deployment

This third submission is awesome. How you can train 10000 more image fastly? I have a challenge to train them. Hemm… I bit thinking, why so slow when I’m not set anything, I just realize, I need to change to GPU processing, I’m new at Tensorflow. It makes the training faster than before. Just in some minutes, not like before, about 30 minutes, so far differences.

  • The validation set is 20% of the total dataset.
  • Must use a sequential model.
  • Models must use the Conv2D Maxpooling Layer.
  • The accuracy at training set and validation set of the model is more than 92%.
  • Have to implement callback
  • Give plot of loss and accuracy at training and validation.
  • Writes code to save the model in TF-Lite format.

TLDR

Maybe you don’t want to read the long thread. I want to bring you a summarization. Mostly this course have a good start to know what Tensorflow Certification test. I see this bring you about 80% criteria on the Certification. But, is it enough? No, You should read more, at least at Tensorflow documentation, you can read through it. For me, this course is good start for you to achieve Tensorflow Certification and have little bit good knowledge to use it. I’m new at this, I’m new to use Tensorflow. In this course, I can know how to use it and some tricks. It’s worth for you when you need Bahasa Indonesia references about Tensorflow. Great job, Dicoding!

Backend Developer — Focus at Microservices and Cloud Computing

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