
How to Do Business Math & Statistics with Excel – Calculate Mean, Median & Mode
This video from codebasics explains how to do basic statistical analysis in Excel.. Mean, median and mode are basic statistical formulas that businesses use on daily basis to make data-informed decisions. In this video, I will give you a very simple explanation of these concepts using a few business use cases. We will also use […]
Read More
Gradient Descent Explained Simply
- Frank
- August 10, 2021
- ai for beginners
- ai prediction
- artificial intelligence
- average loss
- bias
- cross entropy loss
- epoch
- epochs
- error function
- Gradient Descent
- log loss
- log loss function
- loss function
- loss over epochs
- Machine Learning
- mean
- ML
- ml for beginners
- ml prediction
- Neural Network
- Perceptron
- Python
- python ai
- python artificial intelligence
- python ml
- python programming
- python tutorial
- simple gradient descent
- training loss
- update bias
- update weights
- weights
In this video, learn about Gradient Descent and how we can use it to update the weights and bias of our AI model. Time Stamps 00:00 – what is gradient descent? 00:37 – gradient descent vs perception 01:04 – sigmoid activation function 01:45 – bias and threshold 02:06 – weighted sum – working example 02:37 […]
Read More
Machine Learning for Beginners: Binary Cross Entropy Loss Error Function
- Frank
- June 26, 2021
- AI
- ai for beginners
- artificial intelligence
- artificial intelligence for beginners
- binary
- binary cross entropy
- binary log loss
- cost
- cost function
- cross entropy
- cross entropy loss
- Data Science
- error function
- error term
- input
- label
- log loss
- logarithm
- loss function
- Machine Learning
- machine learning for beginners
- Math
- Mathematics
- mean
- ML
- Perceptron
- Prediction
- Python
- python machine learning
- python programming
- python tutorial
- Statistics
- Target
- weights
- y hat
- y'
In this lesson from Python Simplified learn the binary Log Loss/Cross Entropy Error Function and break it down to the very basic details.
Read More