Today we will create and train a basic artificial neural network using Numpy and Pandas ONLY! 🤖
We will practice the code (and the math 😅) behind all the ML concepts we have learned on this channel, so we can fully understand what we're doing!
We will break down the training algorithm into Python functions and work with random data, which we will generate within seconds!
For this code along, please make sure you watched the 🛑previous tutorials🛑:
ML Episode 1 - Perceptron 🤖:
https://youtu.be/-KLnurhX-Pg
ML Episode 2 - Cross Entropy Loss 🤖:
https://youtu.be/EJRFP3WmS6Q
ML Episode 3 - Gradient Descent 🤖:
https://youtu.be/jwStsp8JUPU
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🛑🛑 UPDATED LINK TO COMPLETE CODE, Feb 2023 🛑🛑
https://github.com/MariyaSha/TrainBasicNN
👾 Clone the complete code from Wayscript: 👾
https://wayscript.com/script/2OiJ6e7-
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📈📊 Plotting Graphs Tutorials: 📊📈
With Wayscript: https://youtu.be/2g-hdk2wwvU
With Google Colab: https://youtu.be/P4F3PzCMrtk
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⭐ SKIP TO TIMESTAMP ⭐
00:00 - Intro
00:41 - Import Numpy and Pandas
00:58 - Generate Random Data with Numpy
04:06 - Random Choices with Numpy
05:03 - Create a Pandas Data Frame
06:36 - Create functions.py File
07:59 - Weighted Sum Function
09:55 - Select DataFrame Row
10:48 - Remove DataFrame Column
11:52 - Sigmoid Activation Function
12:54 - Cross Entropy / Log Loss Function
14:08 - Gradient Descent: Update Weights
15:46 - Gradient Descent: Update Bias
18:04 - Training Function for Basic Artificial Neural Network
21:45 - Plot Training Data
22:22 - Analyze Training Data
