Last updated 7/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.35 GB | Duration: 3h 48m
Spiking Neuromorphic Computing via PyCARL & Wyrm (Python): Understanding Brain Computer Interfacing (BCI) & Tiny ML
What you’ll learn
Brain Computer Interfacing using spiking neural networks
Quantum spiking neural networks for re-wiring human brain
Drills/ Exercises on Brain Computer Interfacing using EEG Signals
How Brain Computer Interfacing is used for neuro-rehabilitation
Recurrent Neural Networks & LSTMs for Brain Computer Interfacing
Brain Computer Interfacing for Medical Imaging (Healthcare IT)
Brain Computer Interfacing- Human Brain on a Chip
Neuromorphic computing and Spiking Networks
Requirements
No requirements
Description
Despite being quite effective in a variety of tasks across industries, deep learning is constantly evolving, proposing new neural network (NN) architectures such as the Spiking Neural Network (SNN). This exciting course introduces you to the next generation of Machine Learning. You would be able to learn about the fundamentals of Spiking Neural Networks and Brain-Computer Interfacing (BCI). This course has the rigour enough to enable you not only to understand BCI but its implementation in spiking neural networks and to apply these concepts to Brain Healthcare (IT) even on edge machines using Tiny ML.TinyML is a field of study in Machine Learning and Embedded Systems that explores the types of models you can run on small, low-powered devices like microcontrollers. It enables low-latency, low power and low bandwidth model inference at edge devices. While a standard consumer CPUs consume between 65 watts and 85 watts and standard consumer GPU consumes anywhere between 200 watts to 500 watts, a typical microcontroller consumes power in the order of milliwatts or microwatts. That is around a thousand times less power consumption.The course contents includes; 1. Introduction to Machine Learning, Deep Learning, and Artificial Intelligence.2. How Quantum Computing is fuelling AI Healthcare Systems including BCIs. 3. Introduction to Recurrent Neural Networks.4. Introduction to LSTMs.5. Introduction to Brain-Computer Interfaces.6. How BCI is used for neuro- rehabilitation.7. Brain-Computer Interfaces for Stress and Mood Regulation.8. Brain-Computer Interfaces for Motor Imagery & EEG Signals. 9. Brain Implants using Brain-Computer Interfacing. 10. BCI for Medical Imaging.11. Introduction to "Brain- on- a Chip.12. Neuromorphic Computing for Brain Computer Interfacing.13. Introduction to Tiny ML.14. Tiny ML for Real Time Applications
Overview
Section 1: Introduction to Brain Computer Interfacing (BCI)
Lecture 1 BCI- An Introduction
Section 2: Introduction to Deep Learning (AI)
Lecture 2 Machine Learning & Deep Learning
Section 3: Introduction to Brain Computer Interfacing
Lecture 3 Brain Computer Interfacing- An Overview
Section 4: Introduction to Spiking Neural Networks
Lecture 4 Spiking Neural Networks for BCI
Section 5: Fundamentals of Neuromorphic Computing
Lecture 5 Neuromorphic Computing in BCI
Section 6: Building an Artificial Brain using SpinNaker
Lecture 6 BCI- Nueromorphic architectures for BCI
Section 7: Deep Learning for Brain EEG Signals- BCI using PyWavelets
Lecture 7 PyWavelets for BCI
Section 8: Introduction to TinyML- Part I
Lecture 8 TinyMl for BCI
Section 9: Introduction to Tiny ML- Part II
Lecture 9 TinyML
Section 10: DeepC for Brain EEG Signals
Lecture 10 DeepC for Brain Computer Interfacing
Section 11: Neuromorphic Computing Mimics Human Brain
Lecture 11 Neuromorphic Computing & BCIs
Section 12: Neuromorphic Computing in Healthcare
Lecture 12 Introduction to Quantum Neural Networks
Section 13: How Human Brain is Interfaced with a Computer?
Lecture 13 BCI Implementation
Section 14: BrainNet- Brain to Brain Interfacing
Lecture 14 BrainNet- Human Brain to Human Brain Interfacing
Section 15: Introduction to RNNs
Lecture 15 LSTMs- An Introduction
Section 16: Deep Neural Optimizers for BCI
Lecture 16 Deep Neural Optimizers
Section 17: Brain Computer Interfaces & Neuromorphic Computing
Lecture 17 BCI- Neuromorphic Computing
Section 18: BCI- Spiking Neural Networks
Lecture 18 BCI- Spiking Neural Networks
Section 19: LOIHI2 & LAVA for Brain Computer Interfacing
Lecture 19 LOIHI 2 for BCI
Section 20: PyCARL & WYRM- Interfacing BCI with Python
Lecture 20 PyCarl- Python Framework for BCI
Section 21: BCI Augmentation using Spiking Neural Networks
Lecture 21 BCI Augmentation
Section 22: BCI- Software Platforms
Lecture 22 BCI Softwares
Lecture 23 BCIPy
Section 23: Design & Implementation of BCI
Lecture 24 Implementation of BCI
Section 24: Implementation of EEG using BCI
Lecture 25 EEG Motor Movements
Section 25: BCI STACK Development Framework
Lecture 26 BCI Stack
Section 26: Deep Neural Networks for Implementing BCI
Lecture 27 BCI implementation using Deep Neural Networks
Section 27: Emotional Intelligence: Temperament Analysis for Regulation Emotions
Lecture 28 Introduction
Section 28: BCI for Stress & Anxiety Management
Lecture 29 Things to do for an optimistic and positive outlook
Lecture 30 Regulating Emotions Through Practice Exercises
Lecture 31 How to avoid biases and Recurrent loops of Negative Thinking
Section 29: Self Management Activity: Practical Exercise
Lecture 32 How to improve Self Awareness?
Lecture 33 How to avoid fears and develop positive thinking
Lecture 34 Self Management Drills
Section 30: Tapping the Potential of Positive Thinking
Lecture 35 Accepting your Emotions
Section 31: Happiness for Everyone through Personality Traits
Lecture 36 Role of self determination in realizing positive thinking
Lecture 37 Happiness for Everyone through Personality Traits
Lecture 38 Unveiling the potential of positive thinking
Section 32: Quantum DeepMind for BCI
Lecture 39 Quantum DeepMind BCI
Beginners curious to learn about Brain Computer Interfacing using deep neural networks,Undergraduate & Graduate students aspire to kick start Human inspired Artificial Intelligence
Homepage
https://www.udemy.com/course/inculcating-positive-thinking-for-happiness-just-in-5-days/
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