Let’s try to define Google’s TensorFlow in one single sentence. TensorFlow is a machine learning framework.
Although machine learning is a complex discipline, yet we have discussed that also in simple terms. For the beginners, of course.
Certainly, machine learning, or machine learning algorithms is complex and requires years of study and practice.
But, implementing machine learning models is not that complex, or difficult because of Google’s TensorFlow.
Why?
Because TensorFlow helps us acquire data, training models, and finally serves predictions.
In addition, tensorFlow is an open source library for numerical computation.
We have seen that too while we have discussed NumPy.
The Google Brain team initially released TensorFlow in 2015.
But later it has evolved a lot.
We have got TensorFlow 2.0, which is more powerful and ideal for large scale machine learning.
Not only machine learning, but TensorFlow also helps us in implementing deep learning models and algorithms.
We also call it neural networks.
Have you already learned python and learned machine learning?
In that case, it will be easier to use TensorFlow for deep learning.
Let’s see how TensorFlow works.
There are lots of training models in TensorFlow. As a result, with the help of TensorFlow training models we can have large scale production prediction.
On the other hand, we can also use it for image recognition, natural language processing, simulations and many more.
TensorFlow has a broad library of pre-trained models that we can use in our own projects.
Moreover, we can also use code from the TensorFlow Model Garden.
For one reason of course.
It helps us understand the best practices for training the models.
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