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TensorFlow.js Interview Questions
Tensorflow Loaders are used for adding algorithms and data backends one of which is tensorflow itself. For example, a loader can be implemented to load, access and unload a new type of servable machine learning model.
TensorFlow provides support for multiple client languages, one of the best among them is Python. There are some experimental interfaces which are available for C++, Java, and Go. A language bindings for many other languages such as C#, Julia, Ruby, and Scala are created and supported by the open-source community.
There are two ways that you can use to load data into TensorFlow before training Machine Learning algorithms :
 
Data load into memory : Here, the data is loaded into the memory as a single array unit. It is the easiest way to load the data.

TensorFlow data pipeline : It is making use of the built-in APIs to load the data and feed it across to the algorithm.
There are five main steps that govern the working of the majority of algorithms in TensorFlow. They are as follows:
 
* Data import or data generation, alongside setting up a data pipeline
* Data input through computational graphs
* Generation of the loss function to evaluate the output
* Backpropagation to modify the data
* Iterating until output criteria are met
* Import data, generate data, or setting a data-pipeline through placeholders.
* Feed the data through the computational graph.
* Evaluate output on the loss function.
* Use backpropagation to modify the variables.
* Repeat until stopping condition.
* Dropout Technique
* Regularization
* Batch Normalization
The TensorFlow managers are responsible for loading, unloading, lookup, and lifetime management of all servable objects via their loaders. TensorFlow Managers control the full lifecycle of Servables, including :
 
* Loading Servables
* Serving Servables
* Unloading Servables

It is an abstract class. Its syntax is :
#include <manager.h>  
TensorFlow is used in all of the domains that cover Machine Learning and Deep Learning. Being the most essential tool, the following are some of the main use cases of TensorFlow:
 
* Voice recognition
* Video upscaling
* Image recognition
* Test-based applications
* Time series analysis
Python is the primary language when it comes to working with TensorFlow. TensorFlow provides ample number of functionalities when used with the API, such as :
 
* Automatic logging
* Automatic checkpoints
* Simple training distribution
* Queue-runner design methods
Following are some of the APIs developed by Machine Learning enthusiasts across the globe :
 
* TFLearn : A popular Python package

* TensorLayer : For layering architecture support

* Pretty Tensor : Google’s project providing a chaining interface

* Sonnet : Provides a modular approach to programming