[New] Concise and Practical AI/ML

Explore

Ref & Glossary

Books

Deep Learning —

https://www.deeplearningbook.org⁠

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G4G ML —

https://www.geeksforgeeks.org/machine-learning⁠

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Libraries

TensorFlow —

https://www.tensorflow.org/tutorials⁠

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Backend for training

Keras —

https://www.tensorflow.org/api_docs/python/tf/keras⁠

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High-level APIs utilising backend

HuggingFace —

https://huggingface.co⁠

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High-level Toolset for training

Trained model cloud storage

Tools

Colab —

https://colab.research.google.com⁠

Models

Gemma —

https://huggingface.co/google⁠

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Kaggle —

https://www.kaggle.com⁠

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Trained model cloud storage

Servers with GPU

Others

Communities

Plairo —

http://plairo.novaeh.com⁠

Blogs

Danzwork —

https://danzwork.wordpress.com⁠

Glossary

https://developers.google.com/machine-learning/glossary⁠

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Neuron — A math-based neuron simulate a little bit of bio-neuron

Layer — A chain of neurons usually function with the same context (input for output of previous layer)

Q-learning — A method of Reinforcement Learning using Bellman q-update (based on Bellman equation), Q-learning can use either q-table or q-network

Q-table — The original knowledge store used in q-learning, huge memory needed

Q-network — The modern knowledge store (params) in q-learning, very much memory efficient, as it learns by param combination instead of slots for all cases

Q-update — The formula for updating Q-value based on Bellman equation, Q += Rate x T, where T is temporal difference.

Q-value — Estimated sum of rewards from a state and taking a specific action

Q-function — The function which returns q-value, can be the q-table or q-network

Policy Network — A network in RL which returns suggested action instead of q-value

AI — Artificial Intelligence, with learning or no learning

ML — Machine Learning, the process of learning new things in AI

DL — Deep Learning, the method of ML using artificial neural network (ANN, neuralnet)

Bias — The param associated with each neuron to shift the separation line defined by weights

Weight — The param in a neuron for modifying input to neuron, and together with other weights and biases it makes the combination to comprehend left-most input

Loss Function — A convex function (parabola pointing downward) to minimize

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