TensorFlow
PyTorch
Scikit-learn
Keras
IBM Watson Studio
OpenCV
Azure Machine Learning Service
CUDA Toolkit
Glambase
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AdCreative.ai
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The Glambase platform enables you to create and promote AI-powered virtual influencers. You can design your virtual influencer by choosing from a wide range of physical attributes and personality traits to create a unique digital persona completing with a bio that sets the stage for meaningful interactions.
You can reuse your content on other social networks, such as personal blogs or Instagram posts. The platform provides user-friendly tools to effortlessly craft posts, images, and videos without any steep learning curve. You can also influence and curate the content being produced by the AI.
Your virtual influencer can profit autonomously by chatting and selling exclusive content, even when you're not around.
As a user, you retain ownership of your virtual influencer's intellectual property, granting the platform a license for promotional purposes. Additionally, the Glambase platform allows you to monitor your financial progress with a straightforward dashboard featuring real-time analytics and multiple cash-out options.
TensorFlow GlambaseGlambase's answer:
Create lifelike unique virtual influencers for OnlyFans. Creating an influencer profile for other social networks like instagram (consistent character pics creation). NSFW pics creation for onlyfans/etc.
Glambase's answer:
Earn money creating a virtual girlfriend/boyfriend/friend to chat with and for the others to chat with.
Glambase's answer:
Aspiring Entrepreneurs: Seeking innovative ways to enter the influencer marketing domain. Tech-Savvy Creatives: Looking for cutting-edge tools to express their creativity digitally. Marketing Professionals: Experimenting with AI influencers to engage audiences and sell products. Content Creators: Interested in exploring new avenues for content creation and distribution.
I recently had the opportunity to explore this platform, and I must say, I'm impressed by its user-friendly interface and real-time analytics. The seamless integration with social media not only facilitates engagement but also enhances the overall experience. Additionally, the autonomy and interactive behaviour capabilities add a layer of personalisation that sets it apart. While there are minor drawbacks, such as limited physical traits customisation, the platform's early adopter benefits and rewarding early access system more than make up for it. Overall, it's a for anyone looking to leverage technology for profit generation without needing extensive technical knowledge.
The most technologically advanced website I've ever worked on. Real-time analytics, there is integration with social networks, but everything is clear and you do not need deep technical knowledge.
Glambase impresses with its user-friendly platform that requires no technical knowledge, making it accessible to all. The real-time analytics feature stands out, providing valuable insights for informed decision-making. The interactive behavior capability enhances user engagement, creating a dynamic experience. Additionally, the physical traits customization and personality traits customization options add a personal touch, making interactions more engaging. While minor improvements could enhance the platform further, Glambase's functionality and ease of use make it a valuable tool for users seeking customization and real-time insights.
Based on our record, TensorFlow seems to be more popular. 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.
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
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 3 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: over 3 years ago
I don't have much experience with TensorFlow, but I'd recommend starting with TensorFlow.org. Source: over 3 years ago
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: over 3 years ago
PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...
150 ChatGPT 4.0 prompts for SEO - Unlock the power of AI to boost your website's visibility.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
AdCreative.ai - Give your business an unfair advantage with creatives / banners generated by highly trained Artificial Intelligence.
Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.
ChatGPT - ChatGPT is a powerful, open-source language model.
