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AdMob VS TensorFlow

Compare AdMob VS TensorFlow and see what are their differences

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AdMob logo AdMob

Earn more from your mobile apps using in-app ads to generate revenue, gain actionable insights, and grow your app with easy-to-use tools.

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.
  • AdMob Landing page
    Landing page //
    2023-10-20
  • TensorFlow Landing page
    Landing page //
    2023-06-19

AdMob features and specs

  • Wide Reach
    AdMob leverages Google's extensive ad network, providing access to a large user base and a variety of advertisers.
  • Monetization Options
    It offers diverse ad formats including banner, interstitial, native, and rewarded ads, enabling flexible monetization strategies.
  • Integration with Google Services
    Since AdMob integrates seamlessly with other Google services like Firebase, it's easier to manage analytics, user engagement, and monetization in one place.
  • Advanced Targeting
    AdMob provides advanced targeting features, allowing developers to reach specific user demographics and interests, which can improve ad relevance and performance.
  • Cross-Platform Support
    AdMob works with both Android and iOS platforms, making it a versatile choice for developers with apps on multiple platforms.
  • High Fill Rate
    Because of its large network of advertisers, AdMob can fill ad requests more consistently, reducing the chances of empty ad slots.

Possible disadvantages of AdMob

  • Revenue Share
    Google takes a portion of the ad revenue, which may be a significant drawback for some developers.
  • Complex Setup
    Setting up AdMob and integrating it with your app can be complex and time-consuming, particularly for those unfamiliar with Google's ecosystem.
  • Ad Quality Control
    While AdMob endeavors to provide high-quality ads, developers may occasionally encounter low-quality or inappropriate ads that can affect user experience.
  • Policy Compliance
    AdMob requires strict adherence to Google’s ad policies, which can sometimes be stringent and result in account suspensions if not carefully followed.
  • Dependency on Google Ecosystem
    Heavy reliance on Google services can be a drawback if you prefer a more diversified approach to app development and monetization.

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.

AdMob videos

+ Add

AdMob Revenue is Eerily Consistent -- Except for Today

More videos:

  • Review - Admob Earning 100$ Pay Day Get Best Cpc Automatically Allow New Google Certified Ad Networks
  • Review - Which Ad Network I Would Use If I Could Not Use AdMob

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 AdMob and TensorFlow)

AdMob

TensorFlow

Ad Networks
100 100%
Ad Networks
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Mobile Ad Network
100 100%
Mobile Ad Network
0% 0
AI
0 0%
AI
100% 100

User comments

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Reviews

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

AdMob Reviews

We have no reviews of AdMob yet.
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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

Based on our record, TensorFlow should be more popular than AdMob. It has been mentiond 7 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.

AdMob mentions (4)

  • Marketing advice for a start up
    The problem of scale for profitability from ads is certainly there. You could look to use something like admob to start with to make some money back (although the earnings likely will not cover with cost of marketing, without scale). Source: about 2 years ago
  • 5 Google products that have been built for Developers (Part-1)
    1. AdMob Google is the No 1 player in the mobile advertising market. It was already the largest online advertising company when it acquired AdMob. AdMob makes earning revenue easy with in-app ads, actionable insights, and powerful, easy-to-use tools that grow mobile apps. - Source: dev.to / almost 3 years ago
  • What to charge for your app?
    Use online services, such as Google AdMob, for filtering and sorting in-app ads. Source: over 3 years ago
  • This week in Flutter #22
    There are different ways to monetize with your Flutter app. You can make users pay to download it, you can have in-app purchase plans, you can let users subscribe using recurring payments to use all the features, or you can show ads to your users. In this article, Dhruv Nakum teaches you how to integrate AdMob into your app. - Source: dev.to / over 3 years ago

TensorFlow mentions (7)

  • 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 2 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: almost 3 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: almost 3 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: about 3 years ago
  • [Question] What are the best tutorials and resources for implementing NLP techniques on TensorFlow?
    I have looked at this TensorFlow website and TensorFlow.org and some of the examples are written by others, and it seems that I am stuck in RNNs. What is the best way to install TensorFlow, to follow the documentation and learn the methods in RNNs in Python? Is there a good tutorial/resource? Source: about 3 years ago
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What are some alternatives?

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

Google Ad Manager - Grow revenue wherever your users are with an integrated ad management platform that surfaces insights for smarter business decisions.

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

Unity Ads - Unity Ads allows to supplement the existing revenue strategy by allowing to monetize thr entire player base.

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

Facebook Audience Network - Facebook Audience Network is designed to help monetize your apps and websites with ads from global Facebook advertisers.

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

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