Scientists run into a lot of tradeoffs trying to build and scale up brain-like systems that can perform machine learning.
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Datasets are curated collections of images and annotated data that provide researchers and data scientists with the raw materials they need to train, test, validate, and fine-tune their algorithms and deep learning models. But what was the road like to get here? In this and the next readings that we will share over the next few weeks, we will help you understand the profound impact of datasets on computer vision-based systems development, how they’ve evolved over the years, and the challenges faced in creating and using them. 🟢Keep reading https://hubs.la/Q02gKMGh0 #Datasets #ComputerVision #DeepLearning #VisualPerception
Datasets for computer vision: the true catalyst for progress
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"Discover the latest breakthrough in chaos prediction! Researchers at UT Austin have introduced domain-agnostic machine learning models that can effectively forecast chaotic systems, transcending traditional limits. This marks a significant shift towards data-driven predictions in the realm of unpredictability. #MachineLearning #ChaosPrediction #DataScience"
"Discover the latest breakthrough in chaos prediction! Researchers at UT Austin have introduced domain-agnostic machine learning models that can effectively forecast chaotic systems, transcending traditional limits. This marks a significant shift towards data-driven predictions in the realm of unpredictability. #MachineLearning #ChaosPrediction #DataScience"
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The best explanation on back propagation so far for non AI geek like me. Backpropagation is like the neural network's GPS, helping it adjust and fine-tune its parameters during training to minimize the difference between actual and predicted outputs. It's the engine behind learning, making neural networks smarter over time. https://lnkd.in/gMG5q344
The Most Important Algorithm in Machine Learning
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KAN 🌟 KANs have the potential to achieve higher accuracy with more compact architectures! Appreciate for this post (Now i can understand about the concepts of this (Very little..!)) Will open the new era of deep learning soon?! https://lnkd.in/gevEjqba
KAN: Kolmogorov-Arnold Networks
arxiv.org
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Visualization of context and images input for Tensorflow machine learning. 🥺💬 For example only, I use AI games to explain AI, Neurons Networks, Tensorflow, Data Science, Optimizer, Loss and estimations, and Equation transform on StackOverflow and Google. *** All objects on the screen with speed, running on PC *** * have problem with computer 🖥️ will continue more on this subject because actual input not always balance.
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Backpropagation is a fundamental algorithm that enables the training of artificial neural networks, which are the backbone of modern machine learning models, particularly deep learning models.
The Most Important Algorithm in Machine Learning
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A famous dataset in data science is 'California Housing', made by Google. It's often used to train new machine learning students in the dark art of feature engineering. I wrote a blog post on how you can build a simple neural network and train it on the California Housing dataset, using a C# app and the awesome Microsoft CNTK library. Here’s what you’ll learn by reading the article: 🛠️ How to use the CNTK library with C#. 🛠️ Stacking neural layers to build any kind of neural network. 🛠️ Using CNTK to build a simple feed-forward regression model. Dive into the world of deep learning with the CNTK library and learn how you can build your own custom neural networks in modern C# applications. Check out the article for more information and code examples. #CSharp #CognitiveToolkit #DeepLearning #NeuralNetworks #SoftwareDevelopment 👉 https://lnkd.in/ecYwQQ_D
Use C# and a CNTK Neural Network To Predict House Prices In California
mdft.academy
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CMU Researchers Introduce MultiModal Graph Learning (MMGL): A New Artificial Intelligence Framework for Capturing Information from Multiple Multimodal Neighbors with Relational Structures Among Them
CMU Researchers Introduce MultiModal Graph Learning (MMGL): A New Artificial Intelligence Framework for Capturing Information from Multiple Multimodal Neighbors with Relational Structures Among Them
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Full-thrust in learning. Another interesting topic in computer vision, deep-neural-networks and TensorFlow. Would be glad to discuss on machine learning topics with those of you who are also interested in these technologies. #machinelearning #deeplearning #machinelearning #tensorflow #computervision
Completion Certificate for Convolutional Neural Networks in TensorFlow
coursera.org
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