network_stuff:machine_learning
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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
- Traing 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)
- Reinforcement Learning: BEYOND SELF SUPERVISION TODO
- Train the model but also
transfer learning: reuse existing models.
- Tips and tricks: http://stanford.io/2MEHwFM
- For model complexity
- low: bias (flat line(
- high: a lot of variance (adjust data a lot, not good either
-
/Documents/PLURALSIGHT/datascience/Applied-Machine-Learning-mainsource /Users/santosj/Documents/PLURALSIGHT/datascience/bin/activatejupyter 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
- 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
- Managed datasets with panda's and scikit-learn
- convolution studies how a shape is modified by another)
- cnn relu cnn relu cnn …
AI HARDWARE - GPUs
- 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)
- 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
HUGGINGFACE
- models, datasets and prototypes
- open-source and open-weight
network_stuff/machine_learning.1730478914.txt.gz · Last modified: by jotasandoku
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