TensorFlow

Understand basic TensorFlow programming concepts

We understand basic TensorFlow programming concepts using a code to add two numbers in TensorFlow. We created placeholders like tf.placeholder, operations like tf.constant and tf.add, sessions using tf.session and configuration using tf.configProto

TensorFlow

How TensorFlow uses Graph data structure concepts?

In this article, we explain various concepts in TensorFlow such as tensors, dataflow graphs and several optimizations such as decision tree pruning and demonstrate the use of graph data structure and algorithm concepts in TensorFlow

TensorFlow

Run TensorFlow Convolutional Neural Network (TF CNN) benchmarks in CPU

We will walk you through running the official benchmark of (TF CNN benchmark) TensorFlow for Convolutional Neural Network on your machine. The process is simple and we have divided it into three simple steps: install tensorflow, get the benchmarking code and run the benchmark and observe results

TensorFlow

Key ideas in TensorFlow

TensorFlow is a popular Deep Learning library that is used to build Deep Learning models and is backed by Google. A few key ideas of TensorFlow are tensor, distributed computing, kernel abstraction, operation abstraction, computational graph, automatic gradient computation and others

TensorFlow

Build and install TensorFlow from source with MKL DNN support and AVX enabled

In this guide, we will walk you through building and installing TensorFlow from source with support for MKL DNN and with AVX enabled. By following the six simple steps, you can build and install TensorFlow from source in 20 minutes

Machine Learning (ML)

PyTorch vs TensorFlow

We have compared PyTorch and TensorFlow on the basis of various metrics to help you determine the framework you should go forward with. In short, TensorFlow gives you more control and high computational efficiency while PyTorch gives you the simplicity to develop applications.