Since the day Google had released TensorFlow 1.0 in 2017, it gained immediate popularity with machine learning engineers as one of the open-source machine learning libraries. However, two years later, when Google launched its updated version – TensorFlow 2.0 on 30th September 2019 – the entire AI community went into a frenzy.
With AI Engineers around the world debating about the differences between TensorFlow 1.0 and TensorFlow 2.0, it became important to understand the differences between the two.
But before we delve into the differences between the two let’s have a look at some of the TensorFlow facts –
With TensorFlow, almost all the genres of industries were transformed including banking, healthcare, agriculture, pharma to name a few
TensorFlow had played a pivotal role in enabling organizations to leverage AI and thus make their services or products better than before
Talk about being an apple of somebody’s eyes, well TensorFlow quickly became one for almost all the industry biggies including LinkedIn, Twitter, PayPal to name a few
Tensor Flow’s use cases – sentiment analysis, object, and video detection, including image and speech recognition
These were the reasons why TensorFlow 1.0 became such a hit with deep learning enthusiasts. However, there were some loopholes in this renowned open-source ML library, which usually made AI Engineers turn toward other high-level options like PyTorch and Keras.
So again what is the difference between the two?
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