Machine Learning for Neuroscience A Systematic Approach
Auteur : Easttom Chuck
1. Basic Linear Algebra. 2. Overview of Statistics. 3. Introduction to Python Programming. 4. More with Python. 5. General Neuroanatomy and physiology. 6. Cellular neuroscience. 7. Neurological disorders. 8. Introduction to Computational Neuroscience. 9. Overview of machine learning. 10. Artificial Neural Networks. 11. More with ANN. 12. K Means Clustering. 13. K Nearest Neighbors. 14. Self Organizing Maps.
Dr. Chuck Easttom is the author of 32 books. He is an inventor with 22 computer science patents. He holds a Doctor of Science in cybersecurity, a Ph.D. in Nanotechnology, and a Ph.D. in computer science as well as three master’s degrees (one in applied computer science, one in education, and one in systems engineering). He is a senior member of both the IEEE and the ACM. He is also a Distinguished Speaker of the ACM and a Distinguished Visitor of the IEEE. He has been active in the IEEE Brain Computer Interface Standards and is a member of the IEEE Engineering in Medicine and Biology Society.
Date de parution : 07-2023
15.6x23.4 cm
Disponible chez l'éditeur (délai d'approvisionnement : 14 jours).
Prix indicatif 103,03 €
Ajouter au panierThèmes de Machine Learning for Neuroscience :
Mots-clés :
Machine Learning; Neuroscience; Brain Computer Interface; Data mining; Artificial Intelligence; Computational Neuroscience; Import Numpy; CSV File; Unsupervised Machine Learning; Dataset; Spiking Neural Network; Spinal Cord; Python Script; Machine Learning Algorithms; Neural Networks; Artificial Neural Networks; RNNs; Deep Neural Network; DBSCAN; Python Programming; Pil; Boltzmann Machine; Progressive Supranuclear Palsy; Spectral Graph Theory; Som; Supervised Machine Learning; Medulla Oblongata; Progressive Disease