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Twelve Data VS TensorFlow

Compare Twelve Data VS TensorFlow and see what are their differences

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Twelve Data logo Twelve Data

The simplest and most effective way to access both realtime and historical stock, forex, cryptocurrency data, and over 100 technical indicators.

TensorFlow logo TensorFlow

TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.
  • Twelve Data Landing page
    Landing page //
    2023-09-23
  • TensorFlow Landing page
    Landing page //
    2023-06-19

Twelve Data features and specs

  • Comprehensive Data Coverage
    Twelve Data provides access to a wide range of financial data including real-time and historical data for stocks, ETFs, mutual funds, indices, and forex, which is beneficial for comprehensive market analysis.
  • Affordable Pricing
    Twelve Data offers a variety of pricing plans, including a free tier with essential features, which makes it accessible to individual developers and small businesses.
  • Ease of Use
    The API is designed to be user-friendly with well-documented endpoints, making it easy for developers to integrate the service into their applications.
  • High Scalability
    Twelve Data offers robust and scalable API solutions that can handle a large volume of requests, suitable for both small projects and large-scale applications.
  • Global Market Coverage
    The platform provides data from global markets, allowing users to access information from multiple exchanges around the world.

Possible disadvantages of Twelve Data

  • Limited Advanced Features
    While Twelve Data offers a wide range of basic and intermediate features, it may lack some of the advanced analytics and tools needed by experienced traders or larger financial institutions.
  • Rate Limits on Lower Tiers
    The free and lower-tiered plans come with rate limits that can be restrictive for users requiring frequent data access or handling high traffic.
  • Dependency on API Availability
    As with any third-party data provider, users are dependent on the API's uptime and reliability, which can impact applications if there are outages or downtime.
  • Potential Data Lag for Free Plan
    Users on the free plan may experience delays or less frequent updates compared to those on paid plans, which can be a drawback for time-sensitive applications.

TensorFlow features and specs

  • Comprehensive Ecosystem
    TensorFlow offers a complete ecosystem for end-to-end machine learning, covering everything from data preprocessing, model building, training, and deployment to production.
  • Community and Support
    TensorFlow boasts a large and active community, as well as extensive documentation and tutorials, making it easier for beginners to learn and experts to get help.
  • Flexibility
    TensorFlow supports a wide range of platforms such as CPUs, GPUs, TPUs, mobile devices, and embedded systems, providing flexibility depending on the user's needs.
  • Integrations
    TensorFlow integrates well with other Google products and services, including Google Cloud, facilitating seamless deployment and scaling.
  • Versatility
    TensorFlow can be used for a wide range of applications from simple neural networks to more complex projects, including deep learning and artificial intelligence research.

Possible disadvantages of TensorFlow

  • Complexity
    TensorFlow can be challenging to learn due to its complexity and the steep learning curve, particularly for beginners.
  • Performance Overhead
    Although TensorFlow is powerful, it can sometimes exhibit performance overhead compared to other, lighter frameworks, leading to longer training times.
  • Verbose Syntax
    The code in TensorFlow tends to be more verbose and less intuitive, which can make writing and debugging code more cumbersome relative to other frameworks like PyTorch.
  • Compatibility Issues
    Frequent updates and changes can lead to compatibility issues, requiring significant effort to keep libraries and dependencies up to date.
  • Mobile Deployment
    While TensorFlow supports mobile deployment, it is less optimized for mobile platforms compared to some other specialized frameworks, leading to potential performance drawbacks.

Twelve Data videos

+ Add

Twelve Data Demo Review

TensorFlow videos

+ Add

What is Tensorflow? - Learn Tensorflow for Machine Learning and Neural Networks

More videos:

  • Tutorial - TensorFlow In 10 Minutes | TensorFlow Tutorial For Beginners | Deep Learning & TensorFlow | Edureka
  • Review - TensorFlow in 5 Minutes (tutorial)

Category Popularity

0-100% (relative to Twelve Data and TensorFlow)

Twelve Data

TensorFlow

Finance
100 100%
Finance
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
APIs
100 100%
APIs
0% 0
AI
0 0%
AI
100% 100

User comments

Share your experience with using Twelve Data and TensorFlow. For example, how are they different and which one is better?
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Reviews

These are some of the external sources and on-site user reviews we've used to compare Twelve Data and TensorFlow

Twelve Data Reviews

  1. Andrew
    Best API for real-time financial data

    Straightforward to use and powerful API to get financial data in real-time and complete historical data. Best in class Python package.

    Competitors: Alpha Vantage, Polygon.io
    Pros:    Equity data|Forex|Crypto|Affordable|Reliable

TensorFlow Reviews

7 Best Computer Vision Development Libraries in 2024
From the widespread adoption of OpenCV with its extensive algorithmic support to TensorFlow's role in machine learning-driven applications, these libraries play a vital role in real-world applications such as object detection, facial recognition, and image segmentation.
Source: www.labellerr.com
10 Python Libraries for Computer Vision
TensorFlow and Keras are widely used libraries for machine learning, but they also offer excellent support for computer vision tasks. TensorFlow provides pre-trained models like Inception and ResNet for image classification, while Keras simplifies the process of building, training, and evaluating deep learning models.
Source: clouddevs.com
25 Python Frameworks to Master
Keras is a high-level deep-learning framework capable of running on top of TensorFlow, Theano, and CNTK. It was developed by François Chollet in 2015 and is designed to provide a simple and user-friendly interface for building and training deep learning models.
Source: kinsta.com
Top 8 Alternatives to OpenCV for Computer Vision and Image Processing
TensorFlow is an open-source software library for dataflow and differentiable programming across a range of tasks such as machine learning, computer vision, and natural language processing. It provides excellent support for deep learning models and is widely used in several industries. TensorFlow offers several pre-trained models for image classification, object detection,...
Source: www.uubyte.com
PyTorch vs TensorFlow in 2022
There are a couple of notable exceptions to this rule, the most notable being that those in Reinforcement Learning should consider using TensorFlow. TensorFlow has a native Agents library for Reinforcement Learning, and Deepmind’s Acme framework is implemented in TensorFlow. OpenAI’s Baselines model repository is also implemented in TensorFlow, although OpenAI’s Gym can be...
Source: www.assemblyai.com

Social recommendations and mentions

Twelve Data might be a bit more popular than TensorFlow. We know about 9 links to it since March 2021 and only 8 links to TensorFlow. 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.

Twelve Data mentions (9)

  • I Wanted Real-Time Stock Charts on My Garmin Watch. Monkey C Had Other Plans.
    I chose Twelve Data for stock and crypto prices. Free tier: 800 calls per day. My math: 5 symbols × 96 fetches/day (every 15 minutes) = 480 calls. Comfortable margin. - Source: dev.to / about 1 month ago
  • Building a Gold (XAUUSD) Trend Tracker with Python and SQLite
    I am using TwelveData forex API for Extraction of raw data from the financial markets in JSON format. - Source: dev.to / over 1 year ago
  • Top 5 Free Financial Data APIs for Building a Powerful Stock Portfolio Tracker ⚡
    Twelve Data provides extensive financial market coverage, making it ideal for those looking for a free API for building a stock portfolio tracker. - Source: dev.to / over 1 year ago
  • Webflow Crypto Market Data Ticker
    I'd suggest Twelve Data for the financial data feed, if you have a budget for this: https://twelvedata.com/. Source: over 2 years ago
  • Where can I find a free source of historical S&P500 index data at hourly intervals?
    You can use https://twelvedata.com/ they offer an API and their free plan allows 800 credits a day so over a number of days you could get what you need. Source: over 4 years ago
View more

TensorFlow mentions (8)

  • Why 70% of Americans See AI as a Wealth Inequality Machine: The Developer's Role in Building Fairer Tech
    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 / 6 months ago
  • Creating Image Frames from Videos for Deep Learning Models
    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
  • Need help with a Tensorflow function
    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
  • Help: Slow performance with windows 10 compared to Ubuntu 20.04 with TF2.7
    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: over 4 years ago
  • [Question] What are the best tutorials and resources for implementing NLP techniques on TensorFlow?
    I don't have much experience with TensorFlow, but I'd recommend starting with TensorFlow.org. Source: over 4 years ago
View more

What are some alternatives?

When comparing Twelve Data and TensorFlow, you can also consider the following products

Alpha Vantage - Alpha Vantage offers free APIs in JSON and CSV formats for realtime and historical stock and forex data, digital/crypto currency data and over 50 technical indicators.

PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...

Polygon.io - Polygon.io offers streaming realtime data for stocks/equities, ETFs, Indecies and Forex/Currencies including crypto currencies. Our Real-Time Stock Data APIs help you build the future on fintech.

Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.

Financial Modeling Prep - Access all stocks discounted cash flow statements, market price, stock markets news, and learn more about Financial Modeling. Learn M&A, LBO, DCF, Comps, and Financial Statement Modeling thought concrete examples

IBM Watson Studio - Learn more about Watson Studio. Increase productivity by giving your team a single environment to work with the best of open source and IBM software, to build and deploy an AI solution.

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