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://www.youtube.com/watch?v=-KLnurhX-Pg ML Episode 2 - Cross Entropy Loss 🤖: https://www.youtube.com/watch?v=EJRFP3WmS6Q ML Episode 3 - Gradient Descent 🤖: https://www.youtube.com/watch?v=jwStsp8JUPU ********************************** 🛑🛑 UPDATED LINK TO COMPLETE CODE, Feb 2023 🛑🛑 https://github.com/MariyaSha/TrainBasicNN 👾 Clone the complete code from Wayscript: 👾 https://wayscript.com/script/2OiJ6e7- ********************************** 📈📊 Plotting Graphs Tutorials: 📊📈 With Wayscript: https://www.youtube.com/watch?v=2g-hdk2wwvU With Google Colab: https://www.youtube.com/watch?v=P4F3PzCMrtk ********************************** ⭐ 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