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Why You Should Use TensorFlow?

Why You Should Use TensorFlow?

TensorFlow gives you the flexibility and control with features like the Keras Functional API and Model Subclassing API for creation of complex topologies. For easy prototyping and fast debugging, use eager execution.

Why did you choose deep learning?

Deep learning is ideal for predicting outcomes whenever you have a lot of data to learn from – ‘a lot’ being a huge dataset with hundreds of thousands or better millions of data points. Where you have a huge volume of data like this, the system has what it needs to train itself.

Why we use keras in Python?

Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear and actionable feedback upon user error.

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Why do I want to study machine learning?

Studying Machine Learning opens a world of opportunities to develop cutting edge applications in various areas, such as cybersecurity, image recognition, medicine, and face recognition.

Why should I choose machine learning?

The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that has gained fresh momentum.

Why do we use TensorFlow and keras?

TensorFlow provides both high-level and low-level APIs while Keras provides only high-level APIs. Both frameworks thus provide high-level APIs for building and training models with ease. Keras is built in Python which makes it way more user-friendly than TensorFlow.

Why is keras in TensorFlow?

Keras is the high-level API of TensorFlow 2: an approachable, highly-productive interface for solving machine learning problems, with a focus on modern deep learning. It provides essential abstractions and building blocks for developing and shipping machine learning solutions with high iteration velocity.

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Why do we use TensorFlow and Keras?