Difference between revisions of "Backpropagation"
m (Text replacement - "http:" to "https:") |
m |
||
| Line 2: | Line 2: | ||
|title=PRIMO.ai | |title=PRIMO.ai | ||
|titlemode=append | |titlemode=append | ||
| − | |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 |
| − | |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | + | |
| + | <!-- Google tag (gtag.js) --> | ||
| + | <script async src="https://www.googletagmanager.com/gtag/js?id=G-4GCWLBVJ7T"></script> | ||
| + | <script> | ||
| + | window.dataLayer = window.dataLayer || []; | ||
| + | function gtag(){dataLayer.push(arguments);} | ||
| + | gtag('js', new Date()); | ||
| + | |||
| + | gtag('config', 'G-4GCWLBVJ7T'); | ||
| + | </script> | ||
}} | }} | ||
[https://www.youtube.com/results?search_query=backpropagation Youtube search...] | [https://www.youtube.com/results?search_query=backpropagation Youtube search...] | ||
| Line 9: | Line 18: | ||
* [[Backpropagation]] ...[[Gradient Descent Optimization & Challenges]] ...[[Feed Forward Neural Network (FF or FFNN)]] ...[[Forward-Forward]] | * [[Backpropagation]] ...[[Gradient Descent Optimization & Challenges]] ...[[Feed Forward Neural Network (FF or FFNN)]] ...[[Forward-Forward]] | ||
| + | * [[Activation Functions]] | ||
* [[Objective vs. Cost vs. Loss vs. Error Function]] | * [[Objective vs. Cost vs. Loss vs. Error Function]] | ||
* [https://en.wikipedia.org/wiki/Backpropagation Wikipedia] | * [https://en.wikipedia.org/wiki/Backpropagation Wikipedia] | ||
Revision as of 00:01, 11 July 2023
Youtube search... ...Google search
- Backpropagation ...Gradient Descent Optimization & Challenges ...Feed Forward Neural Network (FF or FFNN) ...Forward-Forward
- Activation Functions
- Objective vs. Cost vs. Loss vs. Error Function
- Wikipedia
- Manifold Hypothesis
- How the backpropagation algorithm works
- Backpropagation Step by Step
- What is Backpropagation? | Daniel Nelson - Unite.ai
- Other Challenges in Artificial Intelligence
- A Beginner's Guide to Backpropagation in Neural Networks | Chris Nicholson - A.I. Wiki pathmind
The primary algorithm for performing gradient descent on neural networks. First, the output values of each node are calculated (and cached) in a forward pass. Then, the partial derivative of the error with respect to each parameter is calculated in a backward pass through the graph. Machine Learning Glossary | Google
