Image Classification - Revision history

BPeat at 01:45, 8 July 2023

2023-07-08T01:45:53Z

BPeat at 19:50, 28 April 2023

2023-04-28T19:50:57Z

BPeat: Text replacement - "http:" to "https:"

2023-03-28T00:22:33Z

Text replacement - "http:" to "https:"

BPeat at 12:49, 17 February 2023

2023-02-17T12:49:16Z

BPeat at 03:40, 9 February 2023

2023-02-09T03:40:51Z

BPeat at 12:26, 28 October 2020

2020-10-28T12:26:31Z

BPeat at 12:19, 28 October 2020

2020-10-28T12:19:55Z

BPeat at 03:29, 28 October 2020

2020-10-28T03:29:46Z

BPeat at 03:14, 28 October 2020

2020-10-28T03:14:39Z

BPeat at 03:11, 28 October 2020

2020-10-28T03:11:33Z

@@ Line 2: / Line 2: @@
 |title=PRIMO.ai
 |titlemode=append
-|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, Facebook
+|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
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
 [https://www.youtube.com/results?search_query=Image+Classification+deep+machine+learning+ML+artificial+intelligence+ai Youtube search...]
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 * [[...predict categories]]
 * [[Case Studies]]
 ** [[Healthcare]]
 ** [[Astronomy]]
 ** [[Agriculture]]
 * [[DeepLens - deep learning enabled video camera]]
 ** [[Getting Started & Project: Object Detection]]
 * [[(Deep) Convolutional Neural Network (DCNN/CNN)]]
 * [[Image-to-Image Translation]]
 * [[ResNet-50]]

@@ Line 14: / Line 14: @@
 ** [[Astronomy]]
 ** [[Agriculture]]
-* [[Image Retrieval / Object Detection]]
+* [[DeepLens - deep learning enabled video camera]]
-** [[DeepLens - deep learning enabled video camera]]
+** [[Getting Started & Project: Object Detection]]
 * [[(Deep) Convolutional Neural Network (DCNN/CNN)]]
 * [[Image/Video Transfer Learning]]

@@ Line 5: / Line 5: @@
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
-[http://www.youtube.com/results?search_query=Image+Classification+deep+machine+learning+ML+artificial+intelligence+ai Youtube search...]
+[https://www.youtube.com/results?search_query=Image+Classification+deep+machine+learning+ML+artificial+intelligence+ai Youtube search...]
-[http://www.google.com/search?q=Image+Classification+deep+machine+learning+ML+artificial+intelligence+ai ...Google search]
+[https://www.google.com/search?q=Image+Classification+deep+machine+learning+ML+artificial+intelligence+ai ...Google search]
 * [[...predict categories]]
@@ Line 21: / Line 21: @@
 * [[Image-to-Image Translation]]
 * [[ResNet-50]]
-* [http://yann.lecun.com/exdb/mnist/ The MNIST Database | Y. LeCun, C. Cortes, and C. Burges]
+* [https://yann.lecun.com/exdb/mnist/ The MNIST Database | Y. LeCun, C. Cortes, and C. Burges]
@@ Line 47: / Line 47: @@
 <youtube>0nqvO3AM2Vw</youtube>
 <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: http://myumi.ch/ovgw7
+</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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 = Microsoft Lobe =
-* [http://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.
+* [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.
 {|<!-- T -->

@@ Line 12: / Line 12: @@
 * [[Case Studies]]
 ** [[Healthcare]]
-** [[Space/Planets]]
+** [[Astronomy]]
 ** [[Agriculture]]
 * [[Image Retrieval / Object Detection]]

@@ Line 2: / Line 2: @@
 |title=PRIMO.ai
 |titlemode=append
-|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Facebook
+|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, Facebook
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
@@ Line 55: / Line 55: @@
 <youtube>eAVWos2-qp0</youtube>
 <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.
 |}
 |}<!-- B -->