Feed Forward Neural Network (FF or FFNN) - Revision history

BPeat at 02:40, 6 March 2024

2024-03-06T02:40:42Z

BPeat at 18:41, 2 September 2023

2023-09-02T18:41:23Z

BPeat at 06:03, 11 July 2023

2023-07-11T06:03:31Z

BPeat at 05:21, 11 July 2023

2023-07-11T05:21:41Z

BPeat at 01:26, 6 July 2023

2023-07-06T01:26:40Z

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

2023-03-28T17:42:19Z

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

BPeat at 17:58, 26 February 2023

2023-02-26T17:58:32Z

BPeat at 17:58, 26 February 2023

2023-02-26T17:58:08Z

BPeat at 17:55, 26 February 2023

2023-02-26T17:55:25Z

BPeat at 03:36, 3 February 2019

2019-02-03T03:36:19Z

@@ Line 18: / Line 18: @@
 * [[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]]
-* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]]
+* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Train, Validate, and Test]]
 ** [[...predict categories]]
 * [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]

@@ Line 20: / Line 20: @@
 * [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]]
 ** [[...predict categories]]
-* [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
+* [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
 * [https://www.asimovinstitute.org/author/fjodorvanveen/ Neural Network Zoo | Fjodor Van Veen]

@@ Line 17: / Line 17: @@
 [https://www.google.com/search?q=Feed+forward+neural+networks+deep+machine+learning+ML+artificial+intelligence ...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]]
 * [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]]
 ** [[...predict categories]]
 * [https://www.asimovinstitute.org/author/fjodorvanveen/ Neural Network Zoo | Fjodor Van Veen]

@@ Line 17: / Line 17: @@
 [https://www.google.com/search?q=Feed+forward+neural+networks+deep+machine+learning+ML+artificial+intelligence ...Google search]
-* [[AI Solver]]
+* [[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]]
 ** [[...predict categories]]
 * [https://www.asimovinstitute.org/author/fjodorvanveen/ Neural Network Zoo | Fjodor Van Veen]

@@ 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=Feed+forward+neural+networks YouTube search...]
@@ Line 10: / Line 19: @@
 * [[AI Solver]]
 ** [[...predict categories]]
 * [[Backpropagation]]  ...[[Gradient Descent Optimization & Challenges]]  ...[[Feed Forward Neural Network (FF or FFNN)]]  ...[[Forward-Forward]]
 * [https://www.asimovinstitute.org/author/fjodorvanveen/ Neural Network Zoo | Fjodor Van Veen]

@@ 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=Feed+forward+neural+networks YouTube search...]
+[https://www.youtube.com/results?search_query=Feed+forward+neural+networks YouTube search...]
-[http://www.google.com/search?q=Feed+forward+neural+networks+deep+machine+learning+ML+artificial+intelligence ...Google search]
+[https://www.google.com/search?q=Feed+forward+neural+networks+deep+machine+learning+ML+artificial+intelligence ...Google search]
 * [[AI Solver]]
@@ Line 12: / Line 12: @@
 * [[Capabilities]]
 * [[Backpropagation]]  ...[[Gradient Descent Optimization & Challenges]]  ...[[Feed Forward Neural Network (FF or FFNN)]]  ...[[Forward-Forward]]
-* [http://www.asimovinstitute.org/author/fjodorvanveen/ Neural Network Zoo | Fjodor Van Veen]
+* [https://www.asimovinstitute.org/author/fjodorvanveen/ Neural Network Zoo | Fjodor Van Veen]
 Feed forward neural networks (FF or FFNN) and perceptrons (P) are very straight forward, they feed information from the front to the back (input and output, respectively). Neural networks are often described as having layers, where each layer consists of either input, hidden or output cells in parallel. A layer alone never has connections and in general two adjacent layers are fully connected (every neuron form one layer to every neuron to another layer). The simplest somewhat practical network has two input cells and one output cell, which can be used to model logic gates. One usually trains FFNNs through back-propagation, giving the network paired datasets of “what goes in” and “what we want to have coming out”. This is called supervised learning, as opposed to unsupervised learning where we only give it input and let the network fill in the blanks. The error being back-propagated is often some variation of the difference between the input and the output (like MSE or just the linear difference). Given that the network has enough hidden neurons, it can theoretically always model the relationship between the input and output. Practically their use is a lot more limited but they are popularly combined with other networks to form new networks. Rosenblatt, Frank. The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review 65.6 (1958): 386.

← Older revision		Revision as of 03:36, 3 February 2019
Line 1:		Line 1:
−	[http://www.youtube.com/results?search_query=Feed+forward+neural+networks+~~%28FF~~+or+~~FFNN%29~~+ ~~YouTube search~~...]	+	{{#seo:
		+	\|title=PRIMO.ai
		+	\|titlemode=append
		+	\|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
		+	\|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
		+	}}
		+	[http://www.youtube.com/results?search_query=Feed+forward+neural+networks YouTube search...]
		+	[http://www.google.com/search?q=Feed+forward+neural+networks+deep+machine+learning+ML+artificial+intelligence ...Google search]

	* [[AI Solver]]		* [[AI Solver]]