processing training data using Neural network (Deep learning) in Python3
$10-30 USD
Completed
Posted about 3 years ago
$10-30 USD
Paid on delivery
I need help with processing training data using NNet (Deep learning) in Python3 from CSV here: [login to view URL]
its a small part of my project that i need help with
Process the Training Database using a NNet
1) Read from CSV the inputs and labels (X,Y) in the training data
2) Display at least one X sample. It is advisable to convert the X values into floats
Make sure that the labels (Y) are one-hot-encoded
3) Isolate a validation data set from the training set.
4) Neural network should be a 3 layered NNet with only 1 hidden network
5) Activation should be sigmoid
6) Use batch gradient descent to solve for weights and biases
At the end of each epoc, use the validation data to report on the accuracy of training
7) When learning has concluded,
display error over all epocs,
display validation accuracy for all epocs,
display accuracy for testing data set.
software: jupiter notebook based on python 3
Hello! Thank you for taking the time to read my application.
I'm a Machine Learning engineer and Python Developer. I have more than 4 years working as a Python Developer and +2 years developing Machine Learning algorithms. I know very well how to work with some of the most popular frameworks out there such as: TensorFlow/Keras, Scikit-learn, Pytorch, among others.
I really believe that I'm the freelancer that you are looking for. Please send me a private message. I guarantee top quality for the best price.