## TensorFlow High-Level Libraries: TF Estimator

TensorFlow has several high-level libraries allowing us to reduce time modeling all with core code. TF Estimator makes it simple

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# Tag: Python

## TensorFlow High-Level Libraries: TF Estimator

## TensorFlow Way for Linear Regression

## Cholesky Decomposition for Linear Regression with TensorFlow

## Linear Least Squares Regression with TensorFlow

## Classification Loss Functions (Part II)

## Loss Functions (Part 1)

## Declaring tensors in TensorFlow

software developer & machine learning engineer

TensorFlow has several high-level libraries allowing us to reduce time modeling all with core code. TF Estimator makes it simple

In my two previous posts, we saw how we can perform Linear Regression using TensorFlow, but I’ve used Linear Least

Although Linear Least Squares Regression is simple and precise, it can be inefficient when matrices get very large. Cholesky decomposition

Linear Least Squares Regression is by far the most widely used regression method, and it is suitable for most cases

In my previous post, I mentioned 3 loss functions, which are mostly intended to be used in Regression models. This

Implementing Loss Functions is very important to machine learning algorithms because we can measure the error from the predicted outputs

[Requirement: Tensorflow and NumPy installed on Python +3.5] [Requirement: import tensorflow as tf] [Requirement: import numpy as np] Tensors are