How to build a Simple Neural Network in Python (One Layer) Part I

Надежный VDS
2 Просмотры
Поддержите сайт и поделитесь материалом в соц.сетях:

Издатель
Example is from the book "Machine Learning for Finance" by Jannes Klaas.
In this video we are building a simple one layer Neural Network from scratch in Python. In specific we are setting up the input layer and initialize random weights and feed this data to the activation function (sigmoid).
The output is then compared with the actual output (y) and measured with the binary cross entropy loss.
If you found this interesting I will continue with optimizing the network using Gradient Descent and Parameter update using Backpropagation and provide details on how to proceed.

Link to Book (Not affiliated btw)
https://www.amazon.de/Machine-Learning-Finance-algorithms-financial-ebook/dp/B07BDK6LF9

Mentioned articles:
Bias and weights
https://towardsdatascience.com/whats-the-role-of-weights-and-bias-in-a-neural-network-4cf7e9888a0f

Cross Entropy Loss:
https://towardsdatascience.com/cross-entropy-loss-function-f38c4ec8643e

00:00 - 01:36 Introduction / Resources
01:36 - 02:31 Input Layer and output (y)
02:31 - 04:20 Initialize random weights and bias
04:20 - 04:50 Getting z (Summation + bias)
04:50 - 05:53 Sigmoid function and getting A (Output of layer)
05:53 - 06:16 Comparing layer output with y
06:16 - 10:20 Binary Cross Entropy Loss
10:20 - 10:45 What needs to be done / outlook

#Python #NeuralNetwork
Категория
Other
Комментариев нет.
Kwork.ru - услуги фрилансеров от 500 руб.