Lystloc
Unolo
TrackOlap
TrackoField
dayTRACK
TrackoBit
Workforce Next
Effort
TensorFlow
PyTorch
Keras
IBM Watson Studio
Scikit-learn
Azure Machine Learning Service
Pega Platform
Azure Machine Learning Studio
Lystloc is a cloud-based field workforce management platform designed to track, monitor, and manage on-field employees in real time. Built with location intelligence, it enhances field operations through accurate data on attendance, travel, tasks, and productivity.
Field teams can mark attendance, track real-time location, update meeting details, and record tasks on the go. Managers get a user-friendly dashboard to monitor activities, assign work, and access analytics for better decision-making. Lystloc improves productivity, reduces costs, and enhances accountabilityโhelping businesses streamline field operations and boost performance.
Lystloc is used across industries such as field sales, logistics, service & maintenance, debt collection, and securityโtrusted by 15,000+ users across 45+ countries.
Step 1: Field employees install the Lystloc app (iOS/Android).
Step 2: The app captures location, routes, check-ins/out, meeting notes, expenses, and more.
Step 3: Managers assign tasks, set geofences, approve check-ins, track travel, and access analytics via the web or mobile dashboard.
Step 4: Data syncs automatically, with offline mode for low-network usage.
Step 5: Managers use insights to track productivity, detect deviations, and optimize field operations.
Lystloc TensorFlowLystloc's answer
Lystloc built specifically for field teams - offering precise location tracking, geo-verified attendance, and instant meeting updates without the need for extra hardware. Itโs faster to set up, easier to use, and delivers actionable insights that actually improve team accountability and performance. Unlike others, Lystloc blends flexibility with control, giving managers full visibility while empowering on-ground staff. Itโs not just a tool - itโs a complete field operations solution trusted by thousands.
Lystloc's answer
Lystloc stands out with real-time and offline GPS tracking, geo-fenced attendance. It combines field tracking, CRM, task management, and meeting notes in one app. Powerful analytics, route optimization, and seamless integrations boost team productivity. With SOC 2/ISO certifications, itโs secure, scalable, and trusted by 15K+ businesses globally.
Lystloc's answer
Lystloc was founded in 2017 by Mr. Bharath Annamalai. Headquartered in Chennai, Tamil Nadu, Lystloc proudly serves over ๐ญ๐ฑ๐+ ๐ฏ๐๐๐ถ๐ป๐ฒ๐๐๐ฒ๐ ๐ฎ๐ฐ๐ฟ๐ผ๐๐ ๐ฐ๐ฑ+ ๐ฐ๐ผ๐๐ป๐๐ฟ๐ถ๐ฒ๐ ๐๐ผ๐ฟ๐น๐ฑ๐๐ถ๐ฑ๐ฒ. With a passionate team of 50+ dedicated professionals, the company continues to drive innovation and deliver exceptional solutions that empower businesses to streamline their field operations and enhance productivity. We are now ๐ฆ๐ข๐ ๐ฎ ๐ฎ๐ป๐ฑ ๐๐ฆ๐ข ๐ฐ๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฒ๐ฑ, proving our commitment to security, privacy, and compliance.
Lystloc's answer
Our primary audience includes businesses with on-ground field teams - especially in sales, service, and delivery roles - who need real-time visibility, accountability, and performance tracking. This includes mid-sized to large enterprises across industries like FMCG, retail, logistics, healthcare, and field services. These teams value simplicity, speed, and control in managing distributed workforces and are looking for tools that reduce manual effort while improving efficiency and compliance.
Based on our record, TensorFlow seems to be more popular. It has been mentiond 8 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
The open-source movement offers hope here. Projects like Hugging Face are democratizing access to state-of-the-art models, while initiatives like Google's TensorFlow provide powerful frameworks without licensing costs. But even open-source solutions require technical expertise that many lack. - Source: dev.to / 5 months ago
Converting the images to a tensor: Deep learning models work with tensors, so the images should be converted to tensors. This can be done using the to_tensor function from the PyTorch library or convert_to_tensor from the Tensorflow library. - Source: dev.to / over 3 years ago
So I went to tensorflow.org to find some function that can generate a CSR representation of a matrix, and I found this function https://www.tensorflow.org/api_docs/python/tf/raw_ops/DenseToCSRSparseMatrix. Source: about 4 years ago
Can anyone offer up an explanation for why there is a performance difference, and if possible, what could be done to fix it. I'm using the installation guidelines found on tensorflow.org and installing tf2.7 through pip using an anaconda3 env. Source: about 4 years ago
I don't have much experience with TensorFlow, but I'd recommend starting with TensorFlow.org. Source: over 4 years ago
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