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The aim is to help developers understand the basic distributed TF concepts that are reoccurring, such as TF servers.

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Keras is compact, easy to learn, high-level Python library run on top of TensorFlow framework.

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In this chapter, we will focus on the difference between CNN and RNN -

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TFLearn can be defined as a modular and transparent deep learning aspect used in TensorFlow framework.

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Logistic regression or linear regression is a supervised machine learning approach for the classification of order discrete categories.

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Artificial neural networks is the information processing system the mechanism of which is inspired with the functionality of biological neural circuits.

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Artificial neural networks is the information processing system the mechanism of which is inspired with the functionality of biological neural circuits.

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Word embedding is the concept of mapping from discrete objects such as words to vectors and real numbers

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TensorFlow includes a visualization tool, which is called the TensorBoard. It is used for analyzing Data Flow Graph and also used to understand machine-learning models

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Recurrent neural networks is a type of deep learning-oriented algorithm, which follows a sequential approach.

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Deep learning is a division of machine learning and is considered as a crucial step taken by researchers in recent decades.

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Tensors are used as the basic data structures in TensorFlow language

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It is important to understand mathematical concepts needed for TensorFlow before creating the basic application in TensorFlow.

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To install TensorFlow, it is important to have “Python” installed in your system. Python version 3.4+ is considered the best to start with TensorFlow installation.

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TensorFlow is a software library or framework, designed by the Google team to implement machine learning and deep learning concepts in the easiest manner.

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As we know that ML models are parameterized in such a way that their behavior can be adjusted for a specific problem.

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Ensembles can give us boost in the machine learning result by combining several models

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In order to execute and produce results successfully, a machine learning model must automate some standard workflows.

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There are various metrics which we can use to evaluate the performance of ML algorithms, classification as well as regression algorithms

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K-nearest neighbors (KNN) algorithm is a type of supervised ML algorithm which can be used for both classification as well as regression predictive problems.

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Hierarchical clustering is another unsupervised learning algorithm that is used to group together the unlabeled data points having similar characteristics.

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As discussed earlier, it is another powerful clustering algorithm used in unsupervised learning.

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K-means clustering algorithm computes the centroids and iterates until we it finds optimal centroid.

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Clustering methods are one of the most useful unsupervised ML methods. These methods are used to find similarity as well as the relationship patterns among data samples and then cluster those samples into groups having similarity based on features.

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Linear regression may be defined as the statistical model that analyzes the linear relationship between a dependent variable with given set of independent variables.

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Regression is another important and broadly used statistical and machine learning tool.

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Support vector machines (SVMs) are powerful yet flexible supervised machine learning algorithms which are used both for classification and regression

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Logistic regression is a supervised learning classification algorithm used to predict the probability of a target variable. .

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Random forest is a supervised learning algorithm which is used for both classification as well as regression

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Naïve Bayes algorithms is a classification technique based on applying Bayes' theorem with a strong assumption that all the predictors are independent to each other

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