8 Tips on How to Choose Neural Network Architecture

Get Certified in Artificial Intelligence. Both tech and Non-Tech can apply!
10% off on AI Certifications. Use Coupon Code - save10
➡️ bit.ly/3vyYMo9
Wondering how to decide neural network architecture? Well, choosing the right neural network architecture is critical to the success of your machine learning project. With so many different types of neural networks available, it can be overwhelming to decide which one to use.
In this video, we'll share eight practical tips on how to select the best neural network architecture for your specific application. We'll discuss the importance of understanding the problem you're trying to solve. We will walk you through each tip with clear examples and easy-to-follow explanations. Whether you're a beginner or an experienced data scientist, you'll gain valuable insights to help you make better decisions when it comes to selecting neural network architectures.
You may also like,
What is Neural Network and How it Works | Neural Network Explained
• What is Neural Network...
ANN vs CNN vs RNN | Difference Between ANN CNN and RNN
• ANN vs CNN vs RNN | Di...
All Major Data Mining Techniques Explained With Examples
• All Major Data Mining ...
What is Data Science Project Life Cycle Explained Step by Step
• What is Data Science P...
So, whether you're building a computer vision application, speech recognition system, or predicting stock prices, this video will provide you with valuable tips to help you make informed decisions when choosing the right neural network architecture for your specific needs.
Make sure to like and subscribe to our channel for more informative videos on machine learning and artificial intelligence. Let's get started!
#neuralnetworks #machinelearning #deeplearning #artificialintelligence #datascience #computervision #hyperparameters #ai

[@mitkosokolov9382]

Which NN is best for EMG data?

[@learnwithwhiteboard]

Electromyography (EMG) data is typically a time-series data that records the electrical activity of muscles. In my opinion, as RNNs are designed to handle time-series data, they are suitable for EMG data analysis and can capture the temporal dependencies in the signal. Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) are popular RNN architectures that have been used in EMG data analysis.

[@mitkosokolov9382]

@@learnwithwhiteboard Does this also apply to EKG and EEG?
And for medical pictures from Ultrasound, x-ray, etc.?

[@learnwithwhiteboard]

@@mitkosokolov9382 Yes, RNNs can also be used for the analysis of EKG and EEG data, which are also time-series data. EKG records the electrical activity of the heart, while EEG records the electrical activity of the brain. Like EMG data, EKG and EEG data are sequential data and require the analysis of temporal dependencies. RNNs, such as LSTM and GRU, can be used to model these dependencies and make predictions based on past observations.
Regarding medical images, such as ultrasound and X-ray, RNNs are not typically used as these are not sequential data. Instead, convolutional neural networks (CNNs) are commonly used for medical image analysis. CNNs are well-suited for this type of data because they are able to detect patterns in the spatial structure of the image. CNNs have been used successfully for tasks such as medical image segmentation, detection, and classification.
Hope this helps!

[@mitkosokolov9382]

@@learnwithwhiteboard Thank you!

[@mitkosokolov9382]

@@learnwithwhiteboard If we need EMG for prosthetic arm is the are anything more specific for the RNN?