Difference between revisions of "Deep Learning"
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* [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative 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]] | ||
| − | * [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing]] | [[Microsoft]] ... [[ | + | * [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing/Copilot]] | [[Microsoft]] ... [[Gemini]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[phind]] ... [[Ernie]] | [[Baidu]] |
* [[Other Challenges]] in Artificial Intelligence | * [[Other Challenges]] in Artificial Intelligence | ||
* [[Neural Network#Deep Neural Network (DNN)|Deep Neural Network (DNN)]] | * [[Neural Network#Deep Neural Network (DNN)|Deep Neural Network (DNN)]] | ||
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
