Best AI and Deep learning books to read in 2022

Are you planning your New Year? Here are some great books to add to your personal or vocational learning. I am adding a few from this list to my AI learning for this year.

After careful consideration, we divided 4 axons of approaching the topic:

  • Machine and Deep Learning fundamentals (for beginners).
  • Framework-centered books: Pytorch, Tensorflow and Keras.
  • MLOPs: cloud, production, and deep learning engineering.
  • Deep learning theory.

You can choose the one that works best for you!

Are you an aspiring Test Automation Developer?

Whether you are new to Test Automation or a seasoned veteran, you need to check out Test Automation University, provided by Applitools. I have taken a few courses and continue to explore new content. The courses are really well done and by reputable folks from the Global Testing / Test Automation community.

A few names:

  • Angie Jones
  • Joe Colantonio
  • Bas Dijkstra
  • Tariq King
  • Andrew Knight
  • Jason Arbon
  • and many more …

Here are a few of my favourite courses:

Why QA Testers Quit And How To Retain Top Performers

If you’re experiencing high turnover in your QA team, the tips outlined above can help you drive costs down by ensuring you retain top-performing employees. While you may need to implement some changes, the result will unequivocally justify the means. Neglecting your employees by providing inadequate training and bypassing deserved promotions will only hurt you in the end. In DevOps practice, where thwarting defects from infiltrating public sites is critical to retaining clients, companies won’t endure by delivering subpar results. As for good QA testers? They’ll survive regardless. Especially in today’s robust economy.

Check out the full article here.