Found 178 Articles for Tensorflow

In this article, we are going to learn about how to create Models in Keras Introduction Keras is an open-source library in Python which provides APIs for building Artificial Neural Network Models with great flexibility. Modelling in Keras can be done either using the Functional API or the Keras Sequential Model. Keras module is also available under the popular Tensorflow Library. Latest version and installation The latest version of keras as of writing this article is 2.1.0. Keras can be installed from PyPI repository using pip. Advantages of Keras for Modelling Keras is used for fast implementation due to ... Read More

Machine learning is a subfield of artificial intelligence (AI) and computer science that focuses on using data and algorithms to mimic human learning processes and progressively increase accuracy. The developing discipline of data science heavily relies on machine learning. In data mining projects, algorithms are taught to generate classifications or predictions using statistical approaches, revealing important insights. These insights then influence decision-making within applications and enterprises, perhaps impacting significant growth KPIs. Big data's continued growth and expansion will drive up demand for data scientists, who will be needed to help identify the most important business issues and the data needed ... Read More

Tensorflow is an open-source Machine learning framework that helps develop models, train pre-trained models by providing high level APIs. TensorFlow is an end-to-end platform to easily build and deploy Machine Learning models. TensorFlow makes it easy for novices and experts to create machine learning models for cloud, desktop, mobile, and web. Features of TensorFlow Let us now see the features of TensorFlow that also explains why it is widely popular Build and Train models easily TensorFlow offers multiple levels of abstraction to make it quick for you to choose the correct one. Build and train models by using the high-level ... Read More

TensorFlow Lite is a mobile library designed to deploy models on mobile, microcontrollers and edge devices. It comes with tools that enable on-device machine learning on mobile devices using 5 aspects − latency, privacy, connectivity, size, and power consumption. It provides support on Android, iOS, embedded Linux and microcontrollers. Supports multiple languages such as Java, Swift, Objective C, C++, and Python. Also provides hardware acceleration and model optimization. The documentation provides end-to-end examples for machine learning projects such as image classification, object detection, question answering, pose estimation, text classification, and many more on different platforms. There are two aspects to ... Read More

TensorFlow TensorFlow is an open-source end-to-end platform to build machine learning applications. It was developed by researchers and developers at Google Brain. Let us now see the features of TensorFlow − Build and Train models easily − TensorFlow offers multiple levels of abstraction to make it quick for you to choose the correct one. Build and train models by using the high-level Keras API, which makes beginning with TensorFlow and machine learning easy. Eager execution lets immediate iteration and intuitive debugging. For large ML training tasks, use the Distribution Strategy API for distributed training on different hardware configurations without changing ... Read More

In this article, you will understand the significant differences between Tensorflow and Keras libraries.TensorFlowTensorFlow is an open-source end-to-end platform to build machine learning applications. It was developed by researchers and developers at Google Brain. Let us now see the features of TensorFlow − Build and Train models easily − TensorFlow offers multiple levels of abstraction to make it quick for you to choose the correct one. Build and train models by using the high-level Keras API, which makes beginning with TensorFlow and machine learning easy. Eager execution lets immediate iteration and intuitive debugging. For large ML training tasks, use the ... Read More

In this article, you will understand the significant differences between Tensorflow and Theano libraries.TensorFlowIt is an open-source end-to-end platform to build machine learning applications and was developed by researchers and developers at Google Brain. Let us now see the features of TensorFlow − Ecosystem of powerful add-on libraries − TensorFlow also supports an ecosystem of powerful add-on libraries and models to experiment with, including Ragged Tensors, TensorFlow Probability, Tensor2Tensor and BERT. TensorFlow Serving − It is a flexible and high-performance serving system for machine learning models, designed for production environments. It runs ML models at production scale on the ... Read More

TensorFlow Hub is a repository that contains trained machine learning models. These models are ready to be fine-tuned and deployed anywhere. The trained models such as BERT and Faster R-CNN can be reused with a few lines of code. It is an open-repository, which means it can be used and modified by anyone.The tfhub.dev repository contains many pre-trained models. Some of them include text embeddings, image classification models, TF.js/TFLite models and so on.It can be installed using the below code:!pip install --upgrade tensorflow_hubIt can be imported into the working environment as shown in the below code:import tensorflow_hub as hub Read More

A sequential model can be built using Keras Sequential API that is used to work with plain stack of layers. Here every layer has exactly one input tensor and one output tensor.A pre-trained model can be used as the base model on the specific dataset. This saves the time and resources of having to train the model again on the specific dataset.A pre-trained model is a saved network which would be previously trained on a large dataset. This large dataset would be a large-scale image-classification task. A pre-trained model can be used as it is or it can be customized ... Read More

TensorFlow Text contains collection of text related classes and ops that can be used with TensorFlow 2.0. The library helps in pre-processing which is required by text-based models, and includes other features that are needed for sequence modelling. These features are not present in TensorFlow.Using the ops during text pre-processing is similar to working with Tensorflow graph. This means the user wouldn’t need to worry about tokenization in training being different from tokenization at interference. Ops also helps in managing pre-processing scripts.It can be installed using the below command:pip install -q tensorflow-textTensorFlow Text requires TensorFlow 2.0, and is compatible with ... Read More