Point Cloud Classification with PointNet - Create, Train and Predict in Keras and TensorFlow

In this Neural Networks Tutorial, we are going to do Point Cloud Classification with PointNet. We are going to load in a dataset with point clouds of different objects. We will create the PointNet architecture from the bottom, train it on the dataset, and then do predictions on new point clouds that the neural network has not seen before. We will go through the process, talk about what point clouds are, and evaluate the results.

The code example is available on my GitHub: https://github.com/niconielsen32​

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#keras #tensorflow

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Multi-cloud Spending: 8 Tips To Lower Cost

A multi-cloud approach is nothing but leveraging two or more cloud platforms for meeting the various business requirements of an enterprise. The multi-cloud IT environment incorporates different clouds from multiple vendors and negates the dependence on a single public cloud service provider. Thus enterprises can choose specific services from multiple public clouds and reap the benefits of each.

Given its affordability and agility, most enterprises opt for a multi-cloud approach in cloud computing now. A 2018 survey on the public cloud services market points out that 81% of the respondents use services from two or more providers. Subsequently, the cloud computing services market has reported incredible growth in recent times. The worldwide public cloud services market is all set to reach $500 billion in the next four years, according to IDC.

By choosing multi-cloud solutions strategically, enterprises can optimize the benefits of cloud computing and aim for some key competitive advantages. They can avoid the lengthy and cumbersome processes involved in buying, installing and testing high-priced systems. The IaaS and PaaS solutions have become a windfall for the enterprise’s budget as it does not incur huge up-front capital expenditure.

However, cost optimization is still a challenge while facilitating a multi-cloud environment and a large number of enterprises end up overpaying with or without realizing it. The below-mentioned tips would help you ensure the money is spent wisely on cloud computing services.

• Deactivate underused or unattached resources

Most organizations tend to get wrong with simple things which turn out to be the root cause for needless spending and resource wastage. The first step to cost optimization in your cloud strategy is to identify underutilized resources that you have been paying for.

Enterprises often continue to pay for resources that have been purchased earlier but are no longer useful. Identifying such unused and unattached resources and deactivating it on a regular basis brings you one step closer to cost optimization. If needed, you can deploy automated cloud management tools that are largely helpful in providing the analytics needed to optimize the cloud spending and cut costs on an ongoing basis.

• Figure out idle instances

Another key cost optimization strategy is to identify the idle computing instances and consolidate them into fewer instances. An idle computing instance may require a CPU utilization level of 1-5%, but you may be billed by the service provider for 100% for the same instance.

Every enterprise will have such non-production instances that constitute unnecessary storage space and lead to overpaying. Re-evaluating your resource allocations regularly and removing unnecessary storage may help you save money significantly. Resource allocation is not only a matter of CPU and memory but also it is linked to the storage, network, and various other factors.

• Deploy monitoring mechanisms

The key to efficient cost reduction in cloud computing technology lies in proactive monitoring. A comprehensive view of the cloud usage helps enterprises to monitor and minimize unnecessary spending. You can make use of various mechanisms for monitoring computing demand.

For instance, you can use a heatmap to understand the highs and lows in computing visually. This heat map indicates the start and stop times which in turn lead to reduced costs. You can also deploy automated tools that help organizations to schedule instances to start and stop. By following a heatmap, you can understand whether it is safe to shut down servers on holidays or weekends.

#cloud computing services #all #hybrid cloud #cloud #multi-cloud strategy #cloud spend #multi-cloud spending #multi cloud adoption #why multi cloud #multi cloud trends #multi cloud companies #multi cloud research #multi cloud market

Point Cloud Classification with PointNet - Create, Train and Predict in Keras and TensorFlow

In this Neural Networks Tutorial, we are going to do Point Cloud Classification with PointNet. We are going to load in a dataset with point clouds of different objects. We will create the PointNet architecture from the bottom, train it on the dataset, and then do predictions on new point clouds that the neural network has not seen before. We will go through the process, talk about what point clouds are, and evaluate the results.

The code example is available on my GitHub: https://github.com/niconielsen32​

Subscribe: https://www.youtube.com/channel/UCpABUkWm8xMt5XmGcFb3EFg

#keras #tensorflow

What are the benefits of cloud migration? Reasons you should migrate

The moving of applications, databases and other business elements from the local server to the cloud server called cloud migration. This article will deal with migration techniques, requirement and the benefits of cloud migration.

In simple terms, moving from local to the public cloud server is called cloud migration. Gartner says 17.5% revenue growth as promised in cloud migration and also has a forecast for 2022 as shown in the following image.

#cloud computing services #cloud migration #all #cloud #cloud migration strategy #enterprise cloud migration strategy #business benefits of cloud migration #key benefits of cloud migration #benefits of cloud migration #types of cloud migration

Keras vs. Tensorflow - Difference Between Tensorflow and Keras

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.

What is Keras?

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.

Major Applications of Keras

• The performance of Keras is smooth on both CPU and GPU.
• Keras provides modularity, flexibility to code, extensibility, and has an adaptation for innovation and research.
• The pythonic nature of Keras makes it easy to explore and debug the code.

What is Tensorflow?

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.

Major applications of Tensorflow

• From mobiles to embedded devices and distributed servers Tensorflow runs on all the platforms.
• Tensorflow is the enterprise of solving real-world and real-time problems like image analysis, robotics, generating data, and NLP.
• Developers are implementing tools for translation languages and the detection of skin cancers using Tensorflow.
• Major projects using TensorFlow are Google translate, video detection, image recognition.

#keras tutorials #keras vs tensorflow #keras #tensorflow

Keras Tutorial For Beginners | What is Keras | Keras Sequential Model | Keras Training

In this video on Keras, you will understand what is Keras and why do we need it, how to compose different models in Keras like the Sequential model and functional model, and later on how to define the inputs, how to connect layers over, and finally hands-on demo.
Why Keras is important

Keras is an Open Source Neural Network library written in Python that runs on top of Theano or Tensorflow. It is designed to be modular, fast, and easy to use. Keras is very quick to make a network model. If you want to make a simple network model with a few lines, Keras can help you with that.

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Link: https://www.youtube.com/watch?v=nS1J-2uoKto

#keras tutorial for beginners #what is keras #keras sequential model #keras training