I use TensorFlow for R&D, and at a higher level, I do machine learning for prescriptive maintenance.

TensorFlow can be deployed in the cloud or on-premise.

TensorFlow can be used for a lot of things, such as prediction and identification.

Our main focus is on machine learning applications that enhance the security, efficiency, and functionality of Wi-Fi systems. One notable project involves creating and maintaining a machine learning-based fraud detection system. We conduct thorough assessments of governance and provide recommendations for different solution components in various domains. For instance, we excel in optimizing and automating trading strategies, catering to enterprise clients seeking maximum efficiency. Our expertise extends to predicting system upgrades and implementing robust encryption and cybersecurity measures.

I use the solution for computer vision and object recognition.

We have factorization software in our product, and we use natural language processing. We have tested TensorFlow on NVIDIA chips for ten years and even had TensorFlow running on IBM Power chips before IBM and Google did. We use the open-source framework, and our primary use case for TensorFlow is pixel analysis of images, and image analytics.

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.

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

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. 

I am using TensorFlow for many computing projects, such as image data analysis, data compressions, and data processing.

I have used TensorFlow GS, TensorFlow for mobile, and TensorFlow Lite. The way things operate is changing from a server-side to edge computing and mobile devices. I have been working on how to reduce the resource usage, we have downgraded and should have good performance with the best accuracy in our machine and models.

I primarily used the solution for computer vision applications, for example, detection and segmentation, and OCR.

We used an architecture from a published paper. It was based on TensorFlow and we upgraded it and developed on it. I also worked on face verification and likeness detection. We are working on anti-spoofing detection. We did some things around face verification and likeness detection. I used TensorFlow specifically.

I've also used the solution to detect hands, tracking customers in the supermarkets, and using the solution for detecting the pickup and dropping of objects from shelf to basket in the supermarket.

Lately, I've been working on a project in Arabic that is designed to detect handwriting. I also use PyTorch to help with this task.

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.

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. 

The main purpose of TensorFlow is to  develop neural networks for data science projects. For example, I had a project about a super-resolution GAN, which is a model that you give a low-resolution image, and it will complete the details for you. I used Keras and TensorFlow for this model and it was really easy to use. The time to implement was simply minimal in comparison to the time for testing, logic, and high-level implementation. That was the highlight of my academic project. For a client, I used TensorFlow and Keras to develop a predictive heat map for orders. He wanted to build a predictive model for a taxi company. They wanted to tell the drivers, "Okay, this area has more probability of having higher orders than another area." I used TensorFlow and Keras to develop a model to predict the areas which have a higher probability and built a heat map to show the drivers. That is actually the highlight of my industrial project. It was a client on Upwork.

I worked for a French company. They used TensorFlow for image classification. after that, I started working with a Russian-American Company who used TensorFlow mainly for object detection. TensorFlow is very good at object detection. We also used it once for natural language processing and audio processing, but I was not directly involved in that project. I was just assisting with deployment issues. We have some clients which wanted us to deploy on the cloud. Alternatively, some clients are releasing Tenserflow on some new edge devices, as an alternative to deploying on the cloud. It is going to be called NextGen AI or something like that from AWS. We use it for all aspects. including data processing, training, and sometimes deployment, but sometimes the use cases differ in practice for ML. As a result, we sometimes stay with TensorFlow or move into AWS specific architectures.

I have experience in NRP and time series forecasting and also in marketing-relevant tasks.

For example, I've used the workaround cutoffs to create a deep learning network to classify binary classification. I've done binary classification tasks and multi-label classification tasks. The multi-class classification is based on Hungarian and English texts. I have an ongoing project, where I created an LSTM and this LSTM is able to classify the text for cryptocurrencies. 

I use this solution out of personal interest — for AI.

Our company doesn't use it too much. We tend to use OpenML, which is included in the OpenCV package, OpenML.