Difference between revisions of "Deep Learning"
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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 |
| − | |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=ai+Deep+Learning+Technique+Model YouTube] |
| − | [ | + | [https://www.quora.com/search?q=ai%20Deep%20Learning%20Technique%20Model ... Quora] |
| + | [https://www.google.com/search?q=ai+Deep+Learning+Technique+Model ...Google search] | ||
| + | [https://news.google.com/search?q=ai+Deep+Learning+Technique+Model ...Google News] | ||
| + | [https://www.bing.com/news/search?q=ai+Deep+Learning+Technique+Model&qft=interval%3d%228%22 ...Bing News] | ||
| − | * [[Other Challenges]] | + | * [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]] |
| − | * [[Deep Neural Network (DNN)]] | + | * [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing/Copilot]] | [[Microsoft]] ... [[Gemini]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[phind]] ... [[Ernie]] | [[Baidu]] |
| − | * [[(Deep) Convolutional Neural Network (DCNN/CNN)]] | + | * [[Other Challenges]] in Artificial Intelligence |
| − | * [[(Deep) Residual Network (DRN) - ResNet]] | + | * [[Neural Network#Deep Neural Network (DNN)|Deep Neural Network (DNN)]] |
| − | * [[Deep Belief Network (DBN)]] | + | ** [[(Deep) Convolutional Neural Network (DCNN/CNN)]] |
| − | * [[ResNet-50] | + | ** [[(Deep) Residual Network (DRN) - ResNet]] |
| − | + | ** [[Deep Belief Network (DBN)]] | |
| + | ** [[ResNet-50]] | ||
* [[Hierarchical Temporal Memory (HTM)]] | * [[Hierarchical Temporal Memory (HTM)]] | ||
| + | * [[Deep Features]] | ||
| + | * [[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://medium.com/@gokul_uf/the-anatomy-of-deep-learning-frameworks-46e2a7af5e47 The Anatomy of Deep Learning Frameworks | Gokula Krishnan Santhanam] | ||
| + | * [https://pathmind.com/wiki/data-for-deep-learning Data for Deep Learning | Chris Nicholson - A.I. Wiki pathmind] | ||
| + | * [https://neurosciencenews.com/neuroscience-topics/deep-learning/ Neuroscience News - Deep Learning] | ||
| + | |||
| + | |||
| + | <img src="https://adatis.co.uk/wp-content/uploads/ML-vs-DL.gif" width="800"> | ||
| − | |||
| − | Deep learning models are vaguely inspired by information processing and communication patterns in biological nervous systems yet have various differences from the structural and functional properties of biological brains, which make them incompatible with neuroscience evidences. “Deep Learning is an algorithm which has no theoretical limitations of what it can learn; the more data you give and the more computational time you provide, the better it is” [ | + | Deep learning models are vaguely inspired by information processing and [[Agents#Communication | communication]] patterns in biological nervous systems yet have various differences from the structural and functional properties of biological brains, which make them incompatible with neuroscience evidences. “Deep Learning is an algorithm which has no theoretical limitations of what it can learn; the more data you give and the more computational time you provide, the better it is” [https://www.cs.toronto.edu/~hinton/csc321/readings/tics.pdf Learning Multiple Layers of Representation | Geoffrey Hinton] |
| + | <youtube>M8qdcOxDxgA</youtube> | ||
| + | <youtube>53YvP6gdD7U</youtube> | ||
| + | <youtube>N0ER1MC9cqM</youtube> | ||
<youtube>b99UVkWzYTQ</youtube> | <youtube>b99UVkWzYTQ</youtube> | ||
<youtube>P2HPcj8lRJE</youtube> | <youtube>P2HPcj8lRJE</youtube> | ||
<youtube>CEv_0r5huTY</youtube> | <youtube>CEv_0r5huTY</youtube> | ||
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<youtube>Q9Z20HCPnww</youtube> | <youtube>Q9Z20HCPnww</youtube> | ||
<youtube>Yr1mOzC93xs</youtube> | <youtube>Yr1mOzC93xs</youtube> | ||
| + | <youtube>V8qrVleGY5U</youtube> | ||
| + | <youtube>rJmBhnLzjOM</youtube> | ||
| + | <youtube>WTnxE0wjZaM</youtube> | ||
Latest revision as of 10:35, 16 March 2024
YouTube ... Quora ...Google search ...Google News ...Bing News
- Artificial Intelligence (AI) ... Generative AI ... Machine Learning (ML) ... Deep Learning ... Neural Network ... Reinforcement ... Learning Techniques
- Conversational AI ... ChatGPT | OpenAI ... Bing/Copilot | Microsoft ... Gemini | Google ... Claude | Anthropic ... Perplexity ... You ... phind ... Ernie | Baidu
- Other Challenges in Artificial Intelligence
- Deep Neural Network (DNN)
- Hierarchical Temporal Memory (HTM)
- Deep Features
- Backpropagation ... FFNN ... Forward-Forward ... Activation Functions ...Softmax ... Loss ... Boosting ... Gradient Descent ... Hyperparameter ... Manifold Hypothesis ... PCA
- The Anatomy of Deep Learning Frameworks | Gokula Krishnan Santhanam
- Data for Deep Learning | Chris Nicholson - A.I. Wiki pathmind
- Neuroscience News - Deep Learning
Deep learning models are vaguely inspired by information processing and communication patterns in biological nervous systems yet have various differences from the structural and functional properties of biological brains, which make them incompatible with neuroscience evidences. “Deep Learning is an algorithm which has no theoretical limitations of what it can learn; the more data you give and the more computational time you provide, the better it is” Learning Multiple Layers of Representation | Geoffrey Hinton
