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While many of the programming libraries encapsulate the inner working details of graph and other algorithms, as a data scientist it helps a lot having a reasonably good familiarity of such details. A solid understanding of the intuition behind such algorithms not only helps in appreciating the logic behind them but also helps in making conscious decisions about their applicability in real life cases. There are several graph based algorithms and most notable are the shortest path…Continue
In 2019, Google announced TensorFlow 2.0, it is a major leap from the existing TensorFlow 1.0. The key differences are as follows:
Ease of use: Many old libraries (example tf.contrib) were removed, and some consolidated. For example, in TensorFlow1.x the model could be made using Contrib, layers, Keras or estimators, so many options for the same task confused many new users. TensorFlow 2.0 promotes TensorFlow Keras for model experimentation and Estimators…Continue
Summary: AI/ML itself is the next big thing for many fields if you’re on the outside looking in. But if you’re a data scientist it’s possible to see those advancements that will propel AI/ML to its next phase of utility.
Another good article by Ajit Joakar.
Co-relation does not equal causation – is a mantra drilled into a Data Scientist from an early age
That’s fine. But very few talk of the follow-on question ..
How exactly do you determine causation?
This problem is further compounded because most books and examples are based on standard datasets (ex: Boston, Iris etc) . These examples do not discuss…Continue
Written by Ajit Jaokar.
Firstly, there are three broad categories of algorithms: