Reading Material

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Book 📘

Some thoughts: Prefer to read books of general knowledge, instead of introducing a specific framework/library.

• List of free ML book, grouped by topics, domains
  • https://github.com/josephmisiti/awesome-machine-learning/blob/master/books.md
• Free ebook from https://machinelearningmastery.com/
  • the blog that daily bnomial always quote

• Python data science - https://www.oreilly.com/library/view/python-data-science/9781491912126/

• Deep Learning theory explanation - https://deeplearningtheory.com/

  • Free book on arxiv
  • Mainly maths

More topic specific :

• Semi-supervised learning
  • Semi-Supervised Learning, 2006.
  • Introduction to Semi-Supervised Learning, 2009.
  • Semi-Supervised Learning: Background, Applications and Future Directions, 2018.
  • Graph-Based Semi-Supervised Learning, 2014.
• Pattern Recognition and Machine Learning (PRML), 2006
  • https://www.microsoft.com/en-us/research/publication/pattern-recognition-machine-learning/
  • https://github.com/gerdm/prml

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Event 🌃

• Meet up events, many things to share
  • https://www.meetup.com/Machine-Learning-Tokyo/
• MLops sharing of real company
  • https://mlops.connpass.com/

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Video Blog 📹

• Google research youtube - https://www.youtube.com/c/GoogleResearch
• Google developer - https://www.youtube.com/googlecode
• Datacamp - https://www.youtube.com/c/Datacamp
• Full Stack Deep Learning Youtube channel - https://www.youtube.com/c/fullstackdeeplearning
• PyTorch youtube
  • https://www.youtube.com/c/PyTorch

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Regular Updated Blog 📝

• bnomial blog
  • https://machinelearningmastery.com/blog/
• Karpathy ML blog
  • http://karpathy.github.io/
• Neptune blog
  • https://neptune.ai/blog
• MLops blog
  • https://data.gunosy.io/entry/mlops
• AWS MLU (ML University)
  • https://mlu-explain.github.io/
• Open ai blog - https://openai.com/blog/
• Facebook ai blog - https://ai.facebook.com/

Newsletter

• https://read.deeplearning.ai/the-batch/
• https://dataelixir.com/
• https://www.deeplearningweekly.com/
• Reddit (r/MachineLearning)
• Twitter (following data scientists, engineers, project managers, etc)

Resource Lists :

• List of ML blogs
  • https://github.com/josephmisiti/awesome-machine-learning/blob/master/blogs.md
  • https://github.com/ujjwalkarn/Machine-Learning-Tutorials#blogs
• List of resources
  • Machine learning resource Awesome list
    • https://github.com/ujjwalkarn/Machine-Learning-Tutorials#other
    • machine learning and deep learning tutorials, articles and other resources
  • Python DataScience list
    • https://github.com/ujjwalkarn/DataSciencePython
    • Generic algorithms
    • common data analysis and machine learning tasks using python

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Tutorial / Doc

• The ML landscape

  

• The Maths aspect in ML (image from reddit)

  

• Recommended - By Jason Brownlee PhD - guide posts by various ML path

  • https://machinelearningmastery.com/start-here/#getstarted
  • PS: this platform bnomial always quote this blog
• Recommended - 100 ML lectures(notes) in University of British Columbia
  • Author claims : It is meant to be followed in order
  • Good roadmap! Recommended
  • https://www.cs.ubc.ca/~schmidtm/Courses/LecturesOnML/
  • Or download the all-included PDF in my drive (https://drive.google.com/file/d/1r-L2Bsazi13n_v8iMgtcaZ0b69ycg6Yo/view?usp=sharing) Note: private access
• Recommended - kaggle course
  • https://www.kaggle.com/learn
  • Text course, reading friendly
  • E.g. ML, python
  • Can get certificate
• Recommended - Dive into Deep learning - https://d2l.ai/index.html
  • Super great interactive DL book with notebbok

• ML simple learning via drafting illustration

  • https://illustrated-machine-learning.github.io/index.html

  • Like this:

    

• ML From Scratch - https://mlfromscratch.com/

  • It is abandoned, so remove it after reading all interested posts
  • Activation function - https://mlfromscratch.com/activation-functions-explained/
• Scikit learn
  • Its user guide is too complex. But is a very great outline of algorithm that you should learn. Just use it as a target list.
• Tensorflow - https://www.tensorflow.org/tutorials
  • Tensorflow training sample by using fashion MNiST.
    • https://www.tensorflow.org/tutorials/keras/classification
  • Strategy to prevent overfit or underfit
    • https://www.tensorflow.org/tutorials/keras/overfit_and_underfit#strategies_to_prevent_overfitting
  • Optimize dataset for training
    • https://www.tensorflow.org/tutorials/keras/text_classification#configure_the_dataset_for_performance
    • .cache - avoid store all data in memory, also faster than loadjng millions of small files
    • .prefetch - allow to do preprocess while training at the same time.

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Video Lesson / Course

• Coursera
  • Deep learning specialization (by deeplearning.ai) https://www.coursera.org/specializations/deep-learning
  • GAN specialization (by deeplearning.ai) https://www.coursera.org/specializations/generative-adversarial-networks-gans
• Codecademy - provide many project-wise lesson path, can build up portfolio seamlessly
  • https://www.codecademy.com/learn/paths/master-statistics-with-python
• Fastai
  • Course, not the framework
  • It's approach is from top level and learn practical first
• Deeplearning ai
  • Opposite from fastai, start from low level like Maths, and then gradually deep dive to Tensorflow

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Random Posts / Pages

• MLCC Text Classification Guide
  • https://developers.google.com/machine-learning/guides/text-classification/

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