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NOTES ABOUT MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE AI

Training pending to watch/read:
- Basic algebra / vectors: https://youtube.com/playlist?list=PL49CF3715CB9EF31D min25 v2

Notes:
Vectors and matrices are basic for machine learning.

Supervised learning: tagging. http://stanford.io/2nRlxxp
- Trainig with all data, tagging it so it can predict future events. Example: train raspberry pi so it can recognise bird images captured with the camera.
Semi-supervised learning: reinforcement learning.
- it does not require training data. But a lot of Try and Error instead.
Unsupervised learning: Discovering patterns in unlabelled data
- Is all about clustering data and inferring relationships.
- k-Means clustering

Deep Learning (ie: neuronal networks) http://stanford.io/2BsQ91Q
- Layers: Input, Hidden, Output. But also Bias input (poking the hidden layers)

For model complexity
- low: bias (flat line(
- high: a lot of variance (adjust data a lot, not good either

BOOK Oreilly: 'Applied Machine Learning and AI for Engineers' Jeff Proise github « book
- /Documents/PLURALSIGHT/datascience/Applied-Machine-Learning-main
- source /Users/santosj/Documents/PLURALSIGHT/datascience/bin/activate
- jupyter notebook /Users/santosj/Documents/PLURALSIGHT/datascience/Applied-Machine-Learning-main
(buy the book later on): https://github.com/javier-antich/ml4nce/blob/main/UC2/UC2-multivariate-outlier-detection.ipynb

Future reading: Machine Learning for Network and Cloud Engineers External Link
Oreilly: Machine Learning with scikit-learn David Mertz github
- http://localhost:8888/notebooks/WhatIsML.ipynb
Oreilly: Wide overview by Rob Barton, Jerome Henry : ml_fundamentals.pfd.pdf Rob Barton, Jerome « DONE
Oreilly: models and misfits by Dr Mark Fenner : ml_models_misfits_drmarkfenner.pdf ; https://github.com/mfenner1/mlwpy_live « DONE

AMD Instinct MI series
Amazon's Inferentia (for machine learning inference on AWS)
Google's TPUs (Tensor Processing Units, custom hardware for Google’s machine learning tasks)
Intel Gaudi (designed for deep learning training)
NVIDIA GPUs (e.g., A100, H100, used for training and inference in deep learning applications)
NVIDIA Tensor Cores (hardware feature within NVIDIA GPUs, optimized for mixed-precision AI workloads)

Current practical models:

Attention mechanism (just a formula that makes easier for training models)
Transformer architecture (hugging face created it)
- transformers are created in the attention mechanism.
  - precursor was tensorflow-hug

PRACTIAL NOTES ON MODELS:

Models multiply matrices.
Those matrices are multi-dimensionals : tensors
- They are made of weight and bias « When defining a model weight and bias are called, generically, parameters.
- Eg: 100B (all tensor's bias and weights, added together)
HF transformers library is ~different from transformers architecture. HF's is framework for loading, training, fine-tuning, and deploying transformer models across NLP and vision tasks. It provides access to thousands of pretrained models, simplifies workflows with task-specific pipelines, and supports custom training on new datasets. Beyond downloading models, Transformers enables production-ready deployment with optimizations for diverse hardware

models, datasets and prototypes
open-source and open-weight
we can download pre-trained Llama, via ollama and then fine-tune it.
- One of the reason is so it identifies patterns bettwer (tex, images…). This process is called embedding (Embeddings capture the inherent properties and relationships of the original data in a condensed format and are often used in Machine Learning use cases. See Link « Better classification
  - embedding: phrases in » vectors out