Tensor Flow

an open source software library for numerical computation

Tensor Flow

It is an open source library created by industry legend Google for its research and production needs. In technical terms, it is used to meet the requirements for the system capable to build and train neural networks for detecting and deciphering patterns and correlations, and analogues to reasoning and learning. It was released on 9th Novemebr 2015 as an open source software. TensorFLow is the second-generation machine learning system of Google Brain. It can run on multiple GPUs and CPUs. It is available on iOS, Android, macOS, and 64-bit Linux.
In simple language TensorFlow is Python Library for rapid numeric computing made and released by Google. The API is technically for Python Programming language.

Installation Process of Tensor Flow

If you have Python SciPy environment, then TensorFlow installation is quite straightforward. TensorFLow works with Python 3.3+ and Python 2.7. You can see the download and setup instructions on TensorFlow website. It will be good to prefer PyPI for the simplest installation.
You can also prefer docker images and virtualnev. It is only Linux that is supported to use GPU and it needs the Cuda Toolkit.

Algorithms

Before getting into the machine learning algorithm, it will be good for you to expand your knowledge about using the tools correctly. Suppose you are writing Python code without a useful computing library, how it will feel like? It will be like using a smartphone without internet connection. You also install a robust and eminent library named NumPy by installing the TensorFlow library that helps in doing mathematical operation in Python. Machine learning algorithms need great amount of mathematical operations. Initially you need to ensure everything is in right order. Create a new file named test.py for first piece of code. You can import TensorFlow by downloading below mentioned script:

Import tensorflow as tf

such import will prepare TensorFlow for bidding. If there is no interruption by Python interpreter, then you are all set to use the TensorFlow. You may have difficulty at this stage due to an error i.e. library fails to search for the CUDA drivers if you install the GPU version. Therefore, you should know if you compiled library with CUDA, then it is essential for to update environment variables with the CUDA path.

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Companies using TensorFlow

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airbus.192

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ceva.192

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dropbox

ebay.192

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intel.192

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movi

qualcomm.192-1

xiaomi.192

snapchat

twitter.192

Relying on TensorFlow conventions

TensorFlow library is imported with tf qualified name usually. Qualifying TensorFlow with the tf is a great idea to stay consistent with open-source TensorFLow Projects and other developers. After knowing about importing TensorFlow into Python source file, it is time for you to know about the method of using it efficiently. Feature vectors can be highly effective for machine learning. Each data item is loaded with feature vector. A list of vectors in known as matrix. A wide range of functions regarding statistical distributions are provided by TensorFlow located in tf.contrib.distributions,includes but certainly not limited to distributions such as Uniform, Gamma, Dirichlet, Chi2, Beta, Bernoulli etc. they are extremely important building blocks when it is the matter of building machine learning algorithm. You will find layer operations producing functions and related weight and bias variables inside tf.contrib.layers,. They come in use for creating different types of deep learning constructions. There are different functions for dropout layer, convolution layer, and batch normalization etc.In tf.contrib.layers.optimizersyou will find different types of optimizers like Momentum, SGD, Adagrad etc. They come in use for solving optimization issues regarding numeric analysis.

In tf.contrib.layers.regularizers module you will find regularizers like L1 and L2. They come in use for reducing the overfitting risk by penalizing large volume of features utilized in the model. For machine learning blocks, it comes in use as building blocks for example Ridge and Lasso Regression.Deep learning algorithms need gradients’ calculation for model optimization. TensorFlow provides provided plenty of initializers like Xavierinitializer in tf.contrib.layers.initializers,used for the weights for keeping gradients’ scale similar in all layers. TensorFlow gives a wide range of loss functions to choose from tf.contrib.losses, like sum of pairwise squares, sum of squares, hinge loss, log-loss, SoftMax cross entry, and sigmoid etc. if you want more variety of metrics like MSE, auc, accuracy, recall, and precision etc. in tf.contrib.metrics

Algorithms in Tensor Flow

Before getting into the machine learning algorithm, it will be good for you to expand your knowledge about using the tools correctly. Suppose you are writing Python code without a useful computing library, how it will feel like? It will be like using a smartphone without internet connection. You also install a robust and eminent library named NumPy by installing the TensorFlow library that helps in doing mathematical operation in Python. Machine learning algorithms need great amount of mathematical operations. Initially you need to ensure everything is in right order.

import tensorflow as tf

Such import will prepare TensorFlow for bidding. If there is no interruption by Python interpreter, then you are all set to use the TensorFlow. You may have difficulty at this stage due to an error i.e. library fails to search for the CUDA drivers if you install the GPU version. Therefore, you should know if you compiled library with CUDA, then it is essential for to update environment variables with the CUDA path.

We provide Machine learning development in Tensor Flow

if you want to have deep knowledge about machine learning development in TensorFlow then you can come to us. We have team of highly experienced professionals. We give you the precise knowledge that will strengthen your foundation about TensorFlow.

Some of the salient advantages are mentioned underneath-

  • Setup of basic and advanced installations regarding TensorFlow.
  • Deep consider training, validation, and monitoring the training performance
  • Empowered to go from concept to machine-learning that is production ready
  • Creation of pipelines to deal with the real scenario input-data

TensorFLow is loaded with plenty of advantages like

  • Multi type GPU support
  • Training across eminent distributed resources
  • Checkpointing of model
  • Loaded with high performing metaframeworks.

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