In this episode, we’ll discuss GPU support for TensorFlow and the integrated Keras API and how to get your code running with a GPU!
🕒🦎 VIDEO SECTIONS 🦎🕒
00:00 Welcome to DEEPLIZARD - Go to deeplizard.com for learning resources
00:30 Help deeplizard add video timestamps - See example in the description
15:24 Collective Intelligence and the DEEPLIZARD HIVEMIND
Keras and Tensorflow are two very popular deep learning frameworks. Deep Learning practitioners most widely use Keras and Tensorflow. Both of these frameworks have large community support. Both of these frameworks capture a major fraction of deep learning production.
Which framework is better for us then?
This blog will be focusing on Keras Vs Tensorflow. There are some differences between Keras and Tensorflow, which will help you choose between the two. We will provide you better insights on both these frameworks.
Keras is a high-level API built on the top of a backend engine. The backend engine may be either TensorFlow, theano, or CNTK. It provides the ease to build neural networks without worrying about the backend implementation of tensors and optimization methods.
Fast prototyping allows for more experiments. Using Keras developers can convert their algorithms into results in less time. It provides an abstraction overs lower level computations.
Tensorflow is a tool designed by Google for the deep learning developer community. The aim of TensorFlow was to make deep learning applications accessible to the people. It is an open-source library available on Github. It is one of the most famous libraries to experiment with deep learning. The popularity of TensorFlow is because of the ease of building and deployment of neural net models.
Major area of focus here is numerical computation. It was built keeping the processing computation power in mind. Therefore we can run TensorFlow applications on almost kind of computer.
#keras tutorials #keras vs tensorflow #keras #tensorflow
Good News!!..…TensorFlow providing in-built GPU.
Great, but how to install it ?? Don’t worry we will crack this in 2 Steps.
Note: This could be done only if the system has Nvidia Graphics Card (Must and should)
Come on let’s jump into it.
Why GPU’s ?
Using GPU’s we could run our neural network problems so comfortably not wasting time on unusual things (I mean sitting all the day to train them by watching epoch by epoch).
So, in this blog we would like to know the installation process very easily (Trust me) in 2 steps.
#data-science #tensorflow #gpu-computing #ai #tensorflow-gpu #cuda
Welcome to DataFlair Keras Tutorial. This tutorial will introduce you to everything you need to know to get started with Keras. You will discover the characteristics, features, and various other properties of Keras. This article also explains the different neural network layers and the pre-trained models available in Keras. You will get the idea of how Keras makes it easier to try and experiment with new architectures in neural networks. And how Keras empowers new ideas and its implementation in a faster, efficient way.
Keras is an open-source deep learning framework developed in python. Developers favor Keras because it is user-friendly, modular, and extensible. Keras allows developers for fast experimentation with neural networks.
Keras is a high-level API and uses Tensorflow, Theano, or CNTK as its backend. It provides a very clean and easy way to create deep learning models.
Keras has the following characteristics:
The following major benefits of using Keras over other deep learning frameworks are:
Before installing TensorFlow, you should have one of its backends. We prefer you to install Tensorflow. Install Tensorflow and Keras using pip python package installer.
The basic data structure of Keras is model, it defines how to organize layers. A simple type of model is the Sequential model, a sequential way of adding layers. For more flexible architecture, Keras provides a Functional API. Functional API allows you to take multiple inputs and produce outputs.
It allows you to define more complex models.
#keras tutorials #introduction to keras #keras models #keras tutorial #layers in keras #why learn keras
The very first and important step is to check which GPU card your laptop is using, based on the GPU card you need to select the correct version of CUDA, cuDNN, MSVC, Tensorflow etc. To check the GPU card on your windows 10 laptop follow below simple steps:
#cudnn #cuda #tensorflow #gpu #geforce-gtx-1650
Installing Tensorflow for GPU is an immensely complicated task that will drive you crazy. There are n-number of tutorials online that claims their way of doing things is the most efficient one. Despite their presence, I had a hard time getting stuff done as installing Tensorflow 2.1.0 is a bit different than its predecessor(Tensorflow 1). A minor difference in code will trigger AttributeError. So once I have succeeded, the very thought was to share my experience as a blog elaborating on the process.
The below-mentioned steps will definitely make your life easy:
#gpu #python3 #tensorflow #cudnn #cuda #python