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Google’s TensorFlow is one of the leading tools for training and deploying deep learning models.

It’s able to optimize wildly complex neural-network architectures with hundreds of millions of parameters, and it comes with a wide array of tools for hardware acceleration, distributed training, and production workflows.

Before getting to the TensorFlow code, it’s important to be familiar with gradient descent and linear regression.

In the simplest terms, it’s a numerical technique for finding the inputs to a system of equations that minimize its output. In the context of machine learning, that system of equations is our model, the inputs are the unknown parameters of the model, and the output is a loss function to be minimized, that represents how much error there is between the model and our data. For some problems (like linear regression), there are equations to directly calculate the parameters that minimize our error, but for most practical applications, we require numerical techniques like gradient descent to arrive at a satisfactory solution.

Google’s TensorFlow is one of the leading tools for training and deploying deep learning models. It’s able to optimize wildly complex neural-network architectures with hundreds of millions of parameters, and it comes with a wide array of tools for hardware acceleration, distributed training, and production workflows. These powerful features […]

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