Avis clients sur TensorFlow

A Machine and Deep Learner must have Library

Avantages :

This Library is very flexible for doing Matrices and Tensor So building very deep high level but quick and scalable ready to use neural networks is at your finger tips. The added other Anaconda Library and Keras compatibility

Inconvénients :

Depreciation of the code is frustrating. To use one form just to throw a Error message.

Relatively Straightforward Deep Learning Framework

Commentaires : Human pattern recognization, image recognization. Habits and trends.

Avantages :

The 2.0 version is easy to set up and there are a lot of APIs that are integrated for using various programming languages to do the same thing. I personally have been using python with this application and have had very little problems getting going. There are a lot of tutorials on getting started, some good data available for free to assist with the learning process. Everything can be run locally which makes it easy to expand on-site. Cloud options are also affordable.

Inconvénients :

The learning curve is a bit steep. This isn't specifically an issue because of TensorFlow itself, the idea of neural networks are not simple. TensorFlow has made improvements on 2.0, that make it easier to use compared to previous versions.

Review of Google Cloud ML Engine

Commentaires : My overall experience with Google Cloud ML platform was very good. I used it's machine learning services to integrate those in my web applications.

Avantages :

The feature of the Google Cloud ML Engine that I most like is the machine learning features that have been provided by this platform. The ML features of this engine provide SOTA results in every task in machine learning and artificial intelligence. The ML features are very handy and easy to use and integrate in other applications as well. I would recommend everyone to use Google Cloud ML Engine for developing AI systems.

Inconvénients :

The pricing, when exceeded the free tier of Google Cloud ML platform, is high. The pricing is high compared to other services like Azure Cloud ML platform.

TensorFlow is useful, although it requires a healthy time commitment to produce accurate models

Commentaires : The benefits I received from this software is more accurate modeling and an interesting insight into what makes one software better than another. TensorFlow did for me what it says it does - produce high quality models, such as neural networks, with a lot of human capital input.

Avantages :

TensorFlow is fascinating in seeing how it produces results in a reasonable time frame. It is completely flexible compared to its costly competitors. The software connects well with various data sources and in setting up scripts to run automatically.

Inconvénients :

TensorFlow takes a lot of time to become an expert in what it is doing. The programming time-commitment might not be worth it unless you plan on customizing your modeling to work with other software.

Very helpful in the new world of machine learning.

Commentaires : You will learn a lot from TensorFlow. It is a good way of entering the machine learning world.

Avantages :

I used TensorFlow on AWS which was easier with all the infrastructure AWS built. It was a good start to machine learning with all the AI and neural network popularity going on these days. It was challenging and exciting to prepare datasets, train them and see the satisfactory results in dashboard. It is also open source and this gives an advantage to TensorFlow.

Inconvénients :

There is a long and challenging learning period. Documentation is rich but it would be so much better to learn and use it with some visual aids.

Best performance for ML tasks

Commentaires : I often work with ML engine, and it appears very complex to me. Because of that I suggest Newbies to start with AutoML first.

Avantages :

ML and AutoML by google dramatically simplify work of Machine Learning developers, in my opinion. Google provides a complete infrastructure that can import export, train and deploy model within the ML environment. On the other hand AutoML provides even more simplicity with operations.

Inconvénients :

It is often difficult to implement ML solution and require time and efforts that are not always available due to certain constraints.

Incredibly powerful

Commentaires : The framework has been amazing for me both for getting into machine learning and for developing more advanced projects.

Avantages :

The software is not the easiest to grasp but there are myriad amounts of documentation and examples online which can help with most situations. The Github repo is also well maintained with references to any bugs and problems that one may encounter

Inconvénients :

Debugging is incredibly difficult with version 1 of the framework (this is meant to be addressed in version 2) and can take a long time to get a handle of the particular concepts. The complete library is exhaustive but to the point of abstracting certain concepts too much.

My Review of TensorFlow

Commentaires : My overall experience is very good using TensorFlow to develop AI models.

Avantages :

I like that TensorFlow has a version that runs on GPU which is very useful when applying Machine learning. Also, I like that TensorFlow is updated regularly to support different libraries and with new features.I like that TensorFlow supports all the project lifecycle from building and programming to deployment.

Inconvénients :

I don't like that TensorFlow requires expertise as it is not easy for beginners. Also, TensorFlow has a slow speed which is not good in deploying deep learning models compared to other frameworks.

Deep learning Bestfriend!

Avantages :

Tensorflow helps me build, train and test models in machine learning and Deep learning. With its commpatibilty to create Deep learning neurons for training purpose and having methods to directly apply it makes tensorflow the best to pursue!!

Inconvénients :

So far tensorflow helps even beginners to use it easily with a number of tutorials and documentations making it less likely to have any thing not to like or havee any complaints to users like me.

Most advance machine learning library

Commentaires : Building machine learning model from scratch and want full power of customisation then choose this tool.

Avantages :

I think it is the most advance library for machine learning specially for deep learning. It very easy to write neural network in this library. It comes with lot of inbuilt function to process data. Also, it has lots of prebuilt function which ease the implementation of neural network.

Inconvénients :

There is no bad thing about this but initially it takes lot of time to understand it as it works on tensors instead of simple vector or array object. But once you learn this, it will be easy to write code.

TensorFlow: The Root of all ML

Commentaires : TensorFlow is one of the most powerful frameworks made for machine learning and analysis. It's so powerful that almost all of the other machine learning frameworks are built over TensorFlow or inspired from it. It comes in handy for almost any stage related to Machine Learning, as it houses methods and toolkits to load data, analyze it, visualize results and much more.

Avantages :

TensorFlow is a very powerful framework, and with the new version and the Keras interface, it is 10 times much easier to use, for simple usage. Earlier it used to require a deeper level of understanding to use the library, but now it is very fluid, simple, and at the same time effective.

Inconvénients :

Even though the Keras interface offers a simple way to work with TensorFlow, it is sometimes not possible or convenient to use Keras. Hence, one must fall back to the previous API which is confusing to use sometimes.

simplicity while being resourceful

Commentaires : ability to do machine learning in the cloud with the ability to monitor data quality and also transform data along the way to serve optimal results in ML models.

Avantages :

Simplicity, speed and very low latency of performance are the best parts of google cloud ML. It also has the ability to manage the end to end process in machine learning while also giving the ability to store data and importantly tools to monitor data quality along the ML journey.

Inconvénients :

This is a fully cloud based solution and hence for most optimal performance the data also needs to be in google cloud - I wish there was an on prem version of this product since we are hybrid and have data both on prem and in the cloud.

One of the best deep learning libraries

Commentaires : Good.

Avantages :

Best library for matrix manipulations and tensor operations. Tensorboard is the best feature.

Inconvénients :

It is difficult it pick up TensorFlow. TensorFlow2 is somewhat easier but there are better options.

I adore this

Avantages :

Great way to have all in one place- cakendar,docs,calculations. It makes my work do much easier and convenient.

Inconvénients :

It has all I need in one place,so no flaws

Extensive and versatile machine learning library

Commentaires : Convolutional neural networks for multi-dimensional arrays (2 to 5 dimensions)

Avantages :

Very good documentation present online. Integrated very well with Google Colab. I like that both beginners and experts use this software.

Inconvénients :

Very hard to get started initially. I was struggling a lot at first. But when you get used to it, it's not that bad. I also wish that there were less bugs. Sometimes my network doesn't compile although there is nothing wrong with it.

Great for Machine Learning and Deep Learning tasks

Avantages :

Supported with python, Allows to control gpu memory usage, tensorboard feature provides nice charts.

Inconvénients :

Hard for beginners, difficult to move model files from tesnorFlow v1 to v2 sometimes.

Mr

Commentaires : Saved a lot of time by removing the need to maintain VMs.

Avantages :

I love how it makes Deep Learning simpler by removing the need to maintain VMs. Tensorflow is an amazing framework that does pretty much everything in deep learning.

Inconvénients :

Tensorflow has a steep learning curve. More tutorials may be provided for beginners to get them on board.

just use this for deep learning

Commentaires : a must use library for deep learning and ML

Avantages :

I think it's the best and most powerful ML and deep learning library available as of now. Tensor has lot and lots of support for deep learning algorithms. It comes with lot of inbuilt function which makes the thing easy for ML developer.

Inconvénients :

Only cons about this is it's long learning curve.

Feedback

Commentaires : good product

Avantages :

easy to use. good performance .integration with python is easy

Inconvénients :

expensive. AI tools need to be more graphically represented

Great for scaling your Machine Learning needs

Commentaires : Great for anyone starting to use ML as a analytical tool. It provides nee=cessary training for you to move forward

Avantages :

Ease of use, adaptability, and speed associated with the cloud platform is amazing. It can help solve any research problems

Inconvénients :

It uses standard template whcih might be difficult to customize in special needs scenario. Some odf the functionality is locked out limiting usage

The best powerfull libary for your neural network

Commentaires : I use Tensorflow to design my first code in machine learning to build an autopilot car game.

Avantages :

Tensorflow it's easy to set up and provides a simple way to start learning machine learning with a guides tutorial that comes with data that needed to train the algorithm.

Inconvénients :

It does not available to a 32bit machine, you need 64bit.

Becoming the standard for Machine Learning tasks

Avantages :

Support for GPU acceleration. A huge number of tutorials/resources. Many different algorithms to choose from and very flexible.

Inconvénients :

Requires significant expertise--not a simple piece of software. Development largely controlled by one company--Google.

The best deep learning library

Commentaires : I have been using this for one and half year, and it's a good learning. And also efficient to build deep learning models.

Avantages :

It is best library to wirte models for deep learning. One advantage is that it is open source. One of the best thing is that, now it has lots of pre built neural network architecture in it.

Inconvénients :

This library requires a long learning period, understanding everything in this library is not very easy.

Excellent Software

Avantages :

User friendly. Great features and functionalities. Availability of tracking bug. The software shows CPU usage.

Inconvénients :

The CPU usage is only available in percentage.

If you want to visualize your deep learning , this is the place to visit

Avantages :

It is essentially the best machine learning and deep learning software. You get to visualize what you are doing with their dashboard visualizer which is basically google analytics for deep learning. I fell in love with Deep learning because of tensorflow.

Inconvénients :

I had a little difficulty with setting up the working environment as it acts as a server and shows the visualization in the browsers local host.