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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.

Description

Tensor aims to establish itself as the premier trading platform for professional NFT traders. The inception of Tensor was driven by our own experiences flipping NFTs on a daily basis, as we found the available tools to be lacking. Our desire for enhanced speed, broader coverage, more comprehensive data, and sophisticated order types led to the creation of Tensor. Upon visiting Tensor, users will encounter a streamlined decentralized application (dApp), although several components work harmoniously behind the scenes. Our bonding-curve-based orders, whether linear or exponential, allow for dollar-cost averaging into or out of NFTs with ease. We also prioritize the instant listing of new collections, recognizing the eagerness of traders to access the latest offerings. By providing liquidity and facilitating market creation for preferred NFT collections on TensorSwap, users can earn trading fees and liquidity provider rewards. Additionally, market makers play a crucial role in enhancing market liquidity, enabling other traders to enter and exit the market at more advantageous prices, which ultimately fosters a more dynamic trading environment. Together, these features make Tensor an indispensable tool for NFT enthusiasts looking to optimize their trading strategies.

API Access

Has API Yes 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Base App No 
Brave Browser No 
Brave Wallet No 
Dataoorts GPU Cloud No 
Glow Wallet No 
Ledger No 
Phantom No 
Rally No 
Slope No 
Solana No 
Solflare No 
TensorFlow Yes 
Torus No 

Integrations

Base App Yes 
Brave Browser Yes 
Brave Wallet Yes 
Dataoorts GPU Cloud Yes 
Glow Wallet Yes 
Ledger Yes 
Phantom Yes 
Rally Yes 
Slope Yes 
Solana Yes 
Solflare Yes 
TensorFlow No 
Torus Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version 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 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
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) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

TFLearn

Website

tflearn.org

Vendor Details

Company Name

Tensor

Website

www.tensor.trade/

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 

Product Features

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