Difference between revisions of "Image Classification"
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| − | |keywords=artificial, intelligence, machine, learning, models | + | |keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools |
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| − | [ | + | [https://www.youtube.com/results?search_query=Image+Classification+deep+machine+learning+ML+artificial+intelligence+ai Youtube search...] |
| − | [ | + | [https://www.google.com/search?q=Image+Classification+deep+machine+learning+ML+artificial+intelligence+ai ...Google search] |
* [[...predict categories]] | * [[...predict categories]] | ||
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* [[Case Studies]] | * [[Case Studies]] | ||
** [[Healthcare]] | ** [[Healthcare]] | ||
| − | ** [[ | + | ** [[Astronomy]] |
** [[Agriculture]] | ** [[Agriculture]] | ||
| − | * [[Image | + | * [[Video/Image]] ... [[Vision]] ... [[Enhancement]] ... [[Fake]] ... [[Reconstruction]] ... [[Colorize]] ... [[Occlusions]] ... [[Predict image]] ... [[Image/Video Transfer Learning]] |
| − | + | * [[DeepLens - deep learning enabled video camera]] | |
| − | + | ** [[Getting Started & Project: Object Detection]] | |
* [[(Deep) Convolutional Neural Network (DCNN/CNN)]] | * [[(Deep) Convolutional Neural Network (DCNN/CNN)]] | ||
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* [[Image-to-Image Translation]] | * [[Image-to-Image Translation]] | ||
* [[ResNet-50]] | * [[ResNet-50]] | ||
| − | * [ | + | * [https://yann.lecun.com/exdb/mnist/ The MNIST Database | Y. LeCun, C. Cortes, and C. Burges] |
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<youtube>0nqvO3AM2Vw</youtube> | <youtube>0nqvO3AM2Vw</youtube> | ||
<b>Lecture 2: Image Classification | <b>Lecture 2: Image Classification | ||
| − | </b><br>Lecture 2 introduces image classification as a core computer vision problem. We see that the image classification task is made challenging by the semantic gap, but that solutions to this task can be used as a building block in other more complicated computer vision systems. We introduce machine learning as a data-driven approach to solving hard problems like image classification. We discuss several common classification datasets in computer vision. Finally we introduce [[K-Nearest Neighbors (KNN)]] as our first machine learning algorithm. This leads to a discussion of hyperparameters and cross-validation strategies that will be crucial for all the machine learning algorithms we will later use. Slides: | + | </b><br>Lecture 2 introduces image classification as a core computer vision problem. We see that the image classification task is made challenging by the semantic gap, but that solutions to this task can be used as a building block in other more complicated computer vision systems. We introduce machine learning as a data-driven approach to solving hard problems like image classification. We discuss several common classification datasets in computer vision. Finally we introduce [[K-Nearest Neighbors (KNN)]] as our first machine learning algorithm. This leads to a discussion of hyperparameters and cross-validation strategies that will be crucial for all the machine learning algorithms we will later use. Slides: https://myumi.ch/ovgw7 |
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<youtube>eAVWos2-qp0</youtube> | <youtube>eAVWos2-qp0</youtube> | ||
<b>Image Classification using [[PyTorch]] in 2020 | <b>Image Classification using [[PyTorch]] in 2020 | ||
| − | </b><br>A virtual hands-on interactive [[PyTorch]] workshop, organized by People In Data together with [[Facebook]] DevC Stockholm. You will be guided through the code and implement your own [[(Deep) Convolutional Neural Network (DCNN/CNN)|CNN model]]! The only thing you need to bring is your [[Google]] account (as we will be using [[Google]] [[Colaboratory|Colab]]) and your curiosity. This webinar will be led by Pranjal Chaubey who is an AI mentor at Udacity and has done workshops for Developer Circles Hyderabad. He is a keen learner of anything data and always eager to share his knowledge. | + | </b><br>A virtual hands-on interactive [[PyTorch]] workshop, organized by People In Data together with [[Meta|Facebook]] DevC Stockholm. You will be guided through the code and implement your own [[(Deep) Convolutional Neural Network (DCNN/CNN)|CNN model]]! The only thing you need to bring is your [[Google]] account (as we will be using [[Google]] [[Colaboratory|Colab]]) and your curiosity. This webinar will be led by Pranjal Chaubey who is an AI mentor at Udacity and has done workshops for Developer Circles Hyderabad. He is a keen learner of anything data and always eager to share his knowledge. |
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= Microsoft Lobe = | = Microsoft Lobe = | ||
| − | * [ | + | * [https://lobe.ai/examples Lobe] aims to make it easy for anyone to train machine learning models. Free, private desktop application that has everything you need to take your machine learning ideas from prototype to production. This version of Lobe learns to look at images using image classification - categorizing an image into a single label overall. We are working to expand to more types of problems and data in future versions. |
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Latest revision as of 20:45, 7 July 2023
Youtube search... ...Google search
- ...predict categories
- Case Studies
- Video/Image ... Vision ... Enhancement ... Fake ... Reconstruction ... Colorize ... Occlusions ... Predict image ... Image/Video Transfer Learning
- DeepLens - deep learning enabled video camera
- (Deep) Convolutional Neural Network (DCNN/CNN)
- Image-to-Image Translation
- ResNet-50
- The MNIST Database | Y. LeCun, C. Cortes, and C. Burges
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Microsoft Lobe
- Lobe aims to make it easy for anyone to train machine learning models. Free, private desktop application that has everything you need to take your machine learning ideas from prototype to production. This version of Lobe learns to look at images using image classification - categorizing an image into a single label overall. We are working to expand to more types of problems and data in future versions.
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