Forward-Forward - Revision history

BPeat at 06:02, 11 July 2023

2023-07-11T06:02:50Z

BPeat at 05:22, 11 July 2023

2023-07-11T05:22:38Z

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

2023-03-28T18:13:01Z

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

BPeat at 18:02, 26 February 2023

2023-02-26T18:02:30Z

BPeat at 18:02, 26 February 2023

2023-02-26T18:02:01Z

BPeat at 17:58, 26 February 2023

2023-02-26T17:58:54Z

BPeat at 17:57, 26 February 2023

2023-02-26T17:57:47Z

BPeat at 17:54, 26 February 2023

2023-02-26T17:54:54Z

BPeat: BPeat moved page Fast-Forward to Forward-Forward without leaving a redirect

2023-02-26T17:54:23Z

BPeat moved page Fast-Forward to Forward-Forward without leaving a redirect

BPeat at 17:54, 26 February 2023

2023-02-26T17:54:01Z

@@ Line 17: / Line 17: @@
 [https://www.google.com/search?q=Backpropagation+deep+machine+learning+ML ...Google search]
-* [[Backpropagation]] ... [[Feed Forward Neural Network (FF or FFNN)|FFNN]] ... [[Forward-Forward]] ... [[Activation Functions]] ... [[Loss]] ... [[Boosting]] ... [[Gradient Descent Optimization & Challenges|Gradient Descent]] ... [[Algorithm Administration#Hyperparameter|Hyperparameter]] ... [[Manifold Hypothesis]] ... [[Principal Component Analysis (PCA)|PCA]]
+* [[Backpropagation]] ... [[Feed Forward Neural Network (FF or FFNN)|FFNN]] ... [[Forward-Forward]] ... [[Activation Functions]] ...[[Softmax]] ... [[Loss]] ... [[Boosting]] ... [[Gradient Descent Optimization & Challenges|Gradient Descent]] ... [[Algorithm Administration#Hyperparameter|Hyperparameter]] ... [[Manifold Hypothesis]] ... [[Principal Component Analysis (PCA)|PCA]]
 * [https://www.cs.toronto.edu/~hinton/FFA13.pdf The Forward-Forward Algorithm: Some Prelininary Investigations |] [[Creatives#Geoffry Hinton | Geoffry Hinton]]
 * [[Objective vs. Cost vs. Loss vs. Error Function]]

@@ 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
+|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=backpropagation Youtube search...]
 [https://www.google.com/search?q=Backpropagation+deep+machine+learning+ML ...Google search]
 * [https://www.cs.toronto.edu/~hinton/FFA13.pdf The Forward-Forward Algorithm: Some Prelininary Investigations |] [[Creatives#Geoffry Hinton | Geoffry Hinton]]
 * [[Objective vs. Cost vs. Loss vs. Error Function]]
 * [https://en.wikipedia.org/wiki/Backpropagation Wikipedia]
 * [https://bdtechtalks.com/2022/12/19/forward-forward-algorithm-geoffrey-hinton/ What is the “forward-forward” algorithm, Geoffrey Hinton’s new AI technique? | Ben Dickson - TechTalks]

@@ 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=backpropagation Youtube search...]
+[https://www.youtube.com/results?search_query=backpropagation Youtube search...]
-[http://www.google.com/search?q=Backpropagation+deep+machine+learning+ML ...Google search]
+[https://www.google.com/search?q=Backpropagation+deep+machine+learning+ML ...Google search]
 * [https://www.cs.toronto.edu/~hinton/FFA13.pdf The Forward-Forward Algorithm: Some Prelininary Investigations |] [[Creatives#Geoffry Hinton | Geoffry Hinton]]
 * [[Backpropagation]]  ...[[Gradient Descent Optimization & Challenges]]  ...[[Feed Forward Neural Network (FF or FFNN)]]  ...[[Forward-Forward]]
 * [[Objective vs. Cost vs. Loss vs. Error Function]]
-* [http://en.wikipedia.org/wiki/Backpropagation Wikipedia]
+* [https://en.wikipedia.org/wiki/Backpropagation Wikipedia]
 * [[Manifold Hypothesis]]
 * [https://bdtechtalks.com/2022/12/19/forward-forward-algorithm-geoffrey-hinton/ What is the “forward-forward” algorithm, Geoffrey Hinton’s new AI technique? | Ben Dickson - TechTalks]

@@ Line 18: / Line 18: @@
 (i.e. real) data and the other with negative data which could be generated by the network itself. Each layer has its own objective function which is simply to have high goodness for positive data and low goodness for negative data. The sum of the squared activities in a layer can be used as the goodness but there are many other possibilities, including minus the sum of the squared activities. If the positive and negative passes can be separated in time, the negative passes can be done offline, which makes the learning much simpler in the positive pass and allows video to be pipelined through the network without ever storing activities or stopping to propagate derivatives.
-<img src="https://miro.medium.com/v2/resize:fit:828/format:webp/1*IwX3pdEu-4OM3XX97z7k-A.png" width="800">
+<img src="https://miro.medium.com/v2/resize:fit:828/format:webp/1*IwX3pdEu-4OM3XX97z7k-A.png" width="600">
 [https://medium.com/mlearning-ai/pytorch-implementation-of-forward-forward-algorithm-by-geoffrey-hinton-and-analysis-of-performance-7e4f1a26d70f PyTorch implementation of Geoffrey Hinton’s Forward-Forward algorithm and analysis of performance VS backpropagation | Diego Fiori - Medium]

@@ Line 18: / Line 18: @@
 (i.e. real) data and the other with negative data which could be generated by the network itself. Each layer has its own objective function which is simply to have high goodness for positive data and low goodness for negative data. The sum of the squared activities in a layer can be used as the goodness but there are many other possibilities, including minus the sum of the squared activities. If the positive and negative passes can be separated in time, the negative passes can be done offline, which makes the learning much simpler in the positive pass and allows video to be pipelined through the network without ever storing activities or stopping to propagate derivatives.
-https://miro.medium.com/v2/resize:fit:828/format:webp/1*IwX3pdEu-4OM3XX97z7k-A.png
+[https://medium.com/mlearning-ai/pytorch-implementation-of-forward-forward-algorithm-by-geoffrey-hinton-and-analysis-of-performance-7e4f1a26d70f PyTorch implementation of Geoffrey Hinton’s Forward-Forward algorithm and analysis of performance VS backpropagation | Diego Fiori - Medium]
 <youtube>NWqy_b1OvwQ</youtube>

@@ Line 9: / Line 9: @@
 * [https://www.cs.toronto.edu/~hinton/FFA13.pdf The Forward-Forward Algorithm: Some Prelininary Investigations |] [[Creatives#Geoffry Hinton | Geoffry Hinton]]
-* [[Backpropagation]]   ... [[Feed Forward Neural Network (FF or FFNN)]]   ... [[Forward-Forward]]
+* [[Backpropagation]]    ... [[Feed Forward Neural Network (FF or FFNN)]]    ... [[Forward-Forward]]
 * [[Gradient Descent Optimization & Challenges]]
 * [[Objective vs. Cost vs. Loss vs. Error Function]]

← Older revision	Revision as of 17:54, 26 February 2023
(No difference)