TensorFlow Reviews

I use the solution for NLP tasks that involve building neural networks. 

TensorFlow is an efficient product for building neural networks. 

Enhancements could include increasing use cases and improving the accuracy of previously built models in TensorFlow. For instance, when we run certain models, the computing power of laptops becomes high.

I have been using the product for three years. 

The product is stable. 

I haven't contacted the solution's support team yet. 

The solution's deployment is easy. 

The solution is free. 

Using TensorFlow is beneficial, especially for building models that are hard to create with other tools. It is easy to learn and great for NLP and computer vision tasks, which often require complex neural networks. TensorFlow has strong community support. I recommend using TensorFlow because you can build models easily and achieve good accuracy. Additionally, TensorFlow has JavaScript support, which allows you to run models on web servers and web pages.

I rate it a nine out of ten. 

In one of my latest projects, I used convolutional neural networks along with several other models for Finance; The objective was to predict future Close Price of S&P500 index. And in the end, I discovered that an ensemble model of convolutional neural networks works the best; I got a very low error and pretty good accuracy. That was my most recent project.

Another project that I used the solution for was using convolutional neural networks was in visual recognition in which the goal was to take a picture of somethin. Then the model would recognize what the images are. That's the pretty standard use case of convolutional neural networks. Along with that, I also used generative adversarial networks and style transfer in TensorFlow. 

The primary feature that I personally like is the fact that TensorFlow allows us to utilize GPUs. At present, in data-driven deep learning, the most important thing is the usage of GPUs which accelerate the training of the model by many folds. What I love about TensorFlow is that it allows me to use GPUs.

TensorFlow is a framework that makes it really easy (and quick) to use deep learning. For example, it has an API, which is called 'Sequential API' , and using that, you can create a whole Deep learning model in about five lines of code. That's another core benefit from my perspective.

TensorFlow is primarily geared towards Python community at present. JavaScript is a different thing and all the websites, web apps and all the mobile apps are built-in JavaScript. JavaScript is the core of that. What can be improved with TensorFlow is how it can mix in. How the JavaScript developers can use TensorFlow. 

There's a huge gap currently. If you are a web developer, then using Machine Learning with TF is not as straightforward as using a regular Javascript library by reading its documentation. TensorFlow should provide a way to do that easily. 

TensorFlow is very stable. It's very reliable. Training a model won't halt in the middle for unknown backend issues. Behavior of each TF layer is predicable based on its documentation. It's one of the top frameworks. If you want to do deep learning, then TensorFlow is the way to go. 

On the other hand, the environment on cloud you use for TensorFlow is a separate story: there are both paid and free versions. For example, Google Colab provodes free aceess to unlimited GPUs yet it's not stable. You might see frequent disconnections or reset of runtimes. Luckily, the paid versions resolve all such issues for a small price.

If you're building large models, then TensorFlow is going to be scalable partially because it allows you to use GPUs. If you have a huge data set, and if you want to train it on your local computer, then it is going to take a whole lot of time if you are not using GPUs with TensorFlow.

I've never used their technical support services. I can't speak to how knowledgable or responsive they are.

TenserFlow and PyTorch are the two big giants of deep learning and they are widely used. If you want to use deep learning, your first choice is either TensorFlow or PyTorch.

I actually recently developed a whole video tutorial on NLP and all the code was written using PyTorch. I also work in PyTorch.

TensorFlow was recently updated to Version Two. Until a year ago, it was TensorFlow Version One and TensorFlow One and PyTorch were hugely different. PyTorch is more Python-like. If you're writing code in PyTorch, you'll feel like you're simply writing your regular Python code as a developer. TensorFlow-1 was a different design in which the developer will first build the computational graph and later creating a session to execute.

Recently, TensorFlow has been updated to TensorFlow-2. It has been made more like PyTorch due to its popularity. At present, when you are writing code at a high level, for example, in a sequential API, you won't even notice, from a programmer's perspective, that there are many differences in TensorFlow and PyTorch. At the same time, both of them allow us to use GPUs, which is the primary use case.

At present, if you're working on Colab, you do not need to set up or anything or install TensorFlow as Colab is specifically for TensorFlow and PyTorch and they are pretty much built-in and everything is there already. If you're working on a cloud too, you can just write TensorFlow.

In terms of maintenance, from the developer's perspective, it doesn't require any maintenance, however, from the creators of TensorFlow's perspective, they are obviously building TensorFlow and maintaining and optimizing it all the time.

This is an open-source solution.

I primarily work on Google Colab in which everything is installed. The most recent versions of TensorFlow are already installed on the Colab.

I have written a deep learning library, which is like very much TensorFlow and PyTorch. It's like my own miniature version of TensorFlow, which I have written as it was an academic project. TensorFlow hides all the details of like nitty-gritty details like how is it working, how the matrices are being multiplied, how is it being handled on GPUs? All these details have been abstracted. If you're writing a model in TensorFlow, you will write just five lines of code (in Sqequential API), in these five lines everything is happening. When I was developing that library, I learned that those five lines of code acutally map to, say, a 1000 lines of code underneaththe hood. I actually wrote all that in order to learn how exactly it works. I just learned what those a thousand lines are.

TensorFlow is just a tool for deep learning. You can use a complete model, which will recognize images in five lines of code. However, to really do deep learning you have to go underneath the hood and understand how exactly things are working. If you are coming from a different background and you write five lines and you can do a model, that's great, however, for example, if a debugging problem comes, you will be in much better shape if you have learned what's underneath. You will be much better shaped to debug your code. If you better understand your code you can better optimize your code. TensorFlow provides you a layer of abstraction, however, that layer of abstraction is bad in some ways.

Primarily, I'm a machine learning engineer. Most of my projects are on using TensorFlow, from my perspective I use TensorFlow a lot and PyTorch occasionally. I also am a full stack developer, I develop apps using React, Django, and D3 yet I don't work a lot in that area. Primarily I work from TensorFlow. From my perspective, they are widely used.

Overall, I would rate the solution ten out of ten. It's very good.

I've used TensorFlow for image classification tasks, object detection tasks, and OCR.

TensorFlow is easy to implement and offers inbuilt functions for various tasks. It supports image classification, object detection, and OCR. The availability of an app makes it simple to integrate OCR functionality directly to mobile applications.

Providing more control by allowing users to build custom functions would make TensorFlow a better option. It currently offers inbuilt functions, however, having the ability to implement custom libraries would enhance its usefulness for enterprise-level applications.

I have worked with TensorFlow for two years.

I initially used TensorFlow and later switched to PyTorch since it provides more control over functionalities, allowing for the creation of custom functions.

I am not familiar with the pricing setup cost and licensing.

I rate TensorFlow seven out of ten. The availability of inbuilt functions makes it suitable for easy development, but PyTorch provides more control.

I have mainly used the solution for solving deep learning problems like image classification and the NLP.

It provides us with 35 features like patch normalization layers, and it is easy to implement using the Kras library when the Kaspersky flow is running behind it. 

The solution is hard to integrate with the GPUs. 

It is a stable solution. 

TensorFlow is scalable. I rate the scalability a nine out of ten.

The initial setup is easy. The deployment depends on the nature of the problem and its classification by the organization. It is also dependent on the internet availability.

It is a recommended solution but at the beginner level, they should have some assistance. I rate the overall solution a ten out of ten. 

With TensorFlow, it is all just personal research that I've done. I'm hoping to bring it to work. TensorFlow is one of the most commonly used platforms for machine learning and deep learning. I specialize in natural language processing and computer vision. Right now, a lot of the clientele work that I have is basic data science of just cleaning and managing data and getting it to fit. I am planning to give a nice example of what we could do by building models that actually predict things that they're looking to do. The models that they have right now are literally just basic, statistical, and linear regression models. They can easily be outperformed with just a very shallow Deep Neural Network.

It is usually on-prem. We run all programs on local machines. A lot of our clients are more old school.

It is open-source, and it is being worked on all the time. You don't have to pay all the big bucks like Azure and Databricks. You can just use your local machine with the open-source TensorFlow and create pretty good models. 

It would be nice to have more pre-trained models that we can utilize within layers. 

I utilize a Mac, and I am unable to utilize AMD GPUs. That's something that I would definitely be like to be able to access within TensorFlow since most of it is with CUDA ML. This only matters for local machines because, in Azure, you can just access any GPU you want from the cloud. It doesn't really matter, but the clients that I work with don't have cloud accounts, or they don't want to utilize that or spend the money. They all see it as too expensive and want to know what they can do on their local machines.

I have been using this solution for a year.

It is awesome.

It is super scalable. You can parallelize it. You can even visualize all the different nodes with TensorBoard. There are so many cool apps you can use. It is heavily used in big industries.

I have not used support at all.  

It is not hard at all as long as you read the documentation.

It is open-source software. You don't have to pay all the big bucks like Azure and Databricks.

I would definitely advise understanding your data and what you're doing because it may not be worth the time if you're going to dive deep into Deep Neural Networks or even just basic Convolutional Neural Networks when you don't really need to. What's the point of building a regressor that is going to be scalable with TensorFlow if all you're trying to do is basic statistics? It depends on the size of the data science work that you're doing.

You can just use your local machine with the open-source TensorFlow and create pretty good models. Getting it into production depends on the security of the system. I don't know what the data engineers are going to have to do to close the pipelines.

I would rate TensorFlow a ten out of ten any day.

We have a project that a Canada-based client is expecting us to develop. If there is a hardware product, it's a mirror LCD device, that is installed in your home and when you start doing an exercise, our AI algorithm will detect what kind of exercise, whether you're doing pushups, jump, etc. We also detect what kind of hardware equipment is being used. We also use TensorFlow to count. 

TensorFlow improves my organization because our clients get a lot of investment from their investors and we progressively improve the products. Every six months we release new features. 

They have a very good vision and roadmap for the next two years. 

TensorFlow is like a library. PyTorch is also a library. These are deep learning libraries that provide a set of functions. Ultimately you have to build a framework. TensorFlow as a whole is useful to us because we use a lot of functions, like activation functions or volition functions, feature mapping, and feature extraction. 

In terms of improvement, we always look for ways they can optimize the model, accelerate the speed and the accuracy, and how can we optimize with our different techniques. There are various techniques available in TensorFlow. Maintaining accuracy is an area they should work on. When there are more and more objects involved with the model, the models get confused. So maintaining the accuracy and speed with the number of classes is the biggest area for improvement. It is a major challenge that we are seeing right now and we are trying to solve the problem.

It's quite stable. It also helps while we have to take it to the browser platform as well as the Android platform because Google is developing in such a way that we can easily migrate. I'll definitely go with TensorFlow when we have to deal with the different platforms. We can easily convert into TF Lite. We can run the model into the browser, as well as on the Android platform.

We have 13 developers and 10 developers mainly focus on TensorFlow, PyTorch, and all the deep learning things. The rest of the developers are C+ and computer vision developers.

If any maintenance is required our team is capable.

Scalability is a major challenge because under the exercise project right now, we have integrated 14 exercises, one port and client is targeting more than 50 exercises. That's where we need scalability. 

Sometimes when we face some issues, we mostly get solutions from stakeholders. So we are not using that much technical support from the TensorFlow team.

TensorFlow is from Google, PyTorch is from Facebook. PyTorch is mostly compatible with Python. If you have to consider it in different platforms like different languages C+, Android, then TensorFlow is really good. In that sense, I'd go with TensorFlow, but PyTorch is really stable. It's only helpful when we are dealing with the Python language. That's where it's really helpful. So both have some advantages and disadvantages.

We got some help from the internet blogs but by now our team is really capable. If there are any issues or errors with a particular version, they can immediately deploy changes. It's now a completely smooth process.

We use the open-source version.

There are always new versions coming out and some versions have issues while some versions don't. When you deploy with the latest version, just make sure that all the systems work as expected when you're deploying. 

I would rate TensorFlow an eight out of ten. 

I use the product mostly for machine learning and creating AI models.

We encountered version mismatch errors while using the product. It sometimes does not integrate well with other libraries in Python, which can be problematic. Additionally, it can be less intuitive when creating neural networks than PyTorch.

I have been using TensorFlow for around two years.

I haven't experienced any significant stability issues or bugs.

Around three to four executives use the product in my current organization.

We chose TensorFlow because we have developed our skills around it. Based on our expertise, it was the best option for us.

Sometimes, working on the setup process can be difficult because loading models takes a significant amount of time compared to PyTorch.

I recommend using TensorFlow. Its libraries make Python programming smoother and reduce the workload. It is easy to use and learn. 

I rate it an eight. 

I use this solution to create Neural Networks, which are computer algorithms for the recognition of objects. This is done based on the SL object that predefines it.

Most of our experience is computer related, but in most cases, we work with images.

Our company was working on a specific project that required a graph. It is now under development and TensorFlow allows us to implement this functionality for the customer who needs to work with recognizing and defining the special mark on the student's workbooks.

The most valuable features are the frameworks and the functionality to work with different data, even when we have a certain quantity of data flowing.

There are connection issues that interrupt the download needed for the data sets. We need to prepare them ourselves.

I have been using TensorFlow for one year.

I have experience not just in TensorFlow, but in the TensorFlow Keras, beginning from TensorFlow 2.0, there are package Keras in TensorFlow. Using this cache, I have created some Neural Networks on Python.

I am using the latest version.

It's a stable product.

It's a scalable solution and we can scale it for different tasks.

We have two specialists that are connected to TensorFlow.

We have not contacted technical support.

Before using TensorFlow, we used different neural networks that were based on Darknet.

The initial setup was easy. There is a lot of documentation available and it was not a problem for us.

It was easy to install.

Setting up TensorFlow on the local computer will take one to two hours to complete. However, if it is for an industrial product that has entered the market and needs to work in the real environment, it would depend on the goal and task that we are working on. 

We completed the installation ourselves without any external help.

Maintenance is based on the customer's needs. We have approximately 40 developers, so if the customer requires maintenance and support then we can provide that for them.

We are using the free version.

I would recommend TensorFlow for techniques that need to develop Neural Networks. I would also recommend PyTorch.

I would rate this solution a nine out of ten.