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Average Ratings 0 Ratings
Description
LiteRT, previously known as TensorFlow Lite, is an advanced runtime developed by Google that provides high-performance capabilities for artificial intelligence on devices. This platform empowers developers to implement machine learning models on multiple devices and microcontrollers with ease. Supporting models from prominent frameworks like TensorFlow, PyTorch, and JAX, LiteRT converts these models into the FlatBuffers format (.tflite) for optimal inference efficiency on devices. Among its notable features are minimal latency, improved privacy by handling data locally, smaller model and binary sizes, and effective power management. The runtime also provides SDKs in various programming languages, including Java/Kotlin, Swift, Objective-C, C++, and Python, making it easier to incorporate into a wide range of applications. To enhance performance on compatible devices, LiteRT utilizes hardware acceleration through delegates such as GPU and iOS Core ML. The upcoming LiteRT Next, which is currently in its alpha phase, promises to deliver a fresh set of APIs aimed at simplifying the process of on-device hardware acceleration, thereby pushing the boundaries of mobile AI capabilities even further. With these advancements, developers can expect more seamless integration and performance improvements in their applications.
Description
TFlearn is a flexible and clear deep learning framework that operates on top of TensorFlow. Its primary aim is to offer a more user-friendly API for TensorFlow, which accelerates the experimentation process while ensuring complete compatibility and clarity with the underlying framework. The library provides an accessible high-level interface for developing deep neural networks, complete with tutorials and examples for guidance. It facilitates rapid prototyping through its modular design, which includes built-in neural network layers, regularizers, optimizers, and metrics. Users benefit from full transparency regarding TensorFlow, as all functions are tensor-based and can be utilized independently of TFLearn. Additionally, it features robust helper functions to assist in training any TensorFlow graph, accommodating multiple inputs, outputs, and optimization strategies. The graph visualization is user-friendly and aesthetically pleasing, offering insights into weights, gradients, activations, and more. Moreover, the high-level API supports a wide range of contemporary deep learning architectures, encompassing Convolutions, LSTM, BiRNN, BatchNorm, PReLU, Residual networks, and Generative networks, making it a versatile tool for researchers and developers alike.
API Access
Has API
Yes
API Access
Has API
Yes
Integrations
TensorFlow
Yes
C++
Yes
Google AI Edge Gallery
Yes
JAX
Yes
Java
Yes
Kotlin
Yes
Objective-C
Yes
PyTorch
Yes
Python
Yes
Swift
Yes
Integrations
TensorFlow
Yes
C++
No
Google AI Edge Gallery
No
JAX
No
Java
No
Kotlin
No
Objective-C
No
PyTorch
No
Python
No
Swift
No
Pricing Details
Free
Free Trial
No
Free Version
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Deployment
Web-Based
No
On-Premises
No
iPhone App
Yes
iPad App
Yes
Android App
Yes
Windows
No
Mac
No
Linux
Yes
Chromebook
No
Deployment
Web-Based
No
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
Yes
Linux
Yes
Chromebook
No
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
Yes
In Person
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Founded
1998
Country
United States
Website
ai.google.dev/edge/litert
Vendor Details
Company Name
TFLearn
Website
tflearn.org
Product Features
Artificial Intelligence
Chatbot
No
For Healthcare
No
For Sales
No
For eCommerce
No
Image Recognition
No
Machine Learning
No
Multi-Language
No
Natural Language Processing
No
Predictive Analytics
No
Process/Workflow Automation
No
Rules-Based Automation
No
Virtual Personal Assistant (VPA)
No
Product Features
Deep Learning
Convolutional Neural Networks
No
Document Classification
No
Image Segmentation
No
ML Algorithm Library
No
Model Training
No
Neural Network Modeling
No
Self-Learning
No
Visualization
No