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[ DevCourseWeb com ] Udemy - Neural Networks in Python from Scratch - Learning by Doing
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01 - Introduction Interpolation & Machine learning
001 Overview of the course.mp4
001 Overview of the course_en.vtt
002 0-Interpolation-template.ipynb
002 1-Addition-template.ipynb
002 2-Sign-template.ipynb
002 3-Number-recognition-template.ipynb
002 Addition-network.png
002 Digit-network.png
002 Sign-network.png
002 Template files for this course.html
003 0-Interpolation-template.ipynb
003 Interpolation (or regression) - The fundamental principle of machine learning.mp4
003 Interpolation (or regression) - The fundamental principle of machine learning_en.vtt
02 - Your first neural network Sum of two numbers
001 Let's get started!.html
002 From interpolation to neural networks.mp4
002 From interpolation to neural networks_en.vtt
003 What are neural networks.mp4
003 What are neural networks_en.vtt
004 1-Addition-template.ipynb
004 Addition-network.png
004 [Project 1] Most simple neural network Sum of two numbers.mp4
004 [Project 1] Most simple neural network Sum of two numbers_en.vtt
005 1-Addition-template.ipynb
005 Addition-network.png
005 Prepare the training and testing data.mp4
005 Prepare the training and testing data_en.vtt
006 Initialize the weights & Calculate the output.mp4
006 Initialize the weights & Calculate the output_en.vtt
007 Accuracy & Error functions.mp4
007 Accuracy & Error functions_en.vtt
008 Gradient of the error function.mp4
008 Gradient of the error function_en.vtt
009 Training the neural network via gradient descent.mp4
009 Training the neural network via gradient descent_en.vtt
010 Using the trained network on the test data.mp4
010 Using the trained network on the test data_en.vtt
03 - Modifying the problem Sign of the sum of two numbers
001 2-Sign-template.ipynb
001 Sign-network.png
001 [Project 2] Complete neural network Sign of the sum of two numbers.mp4
001 [Project 2] Complete neural network Sign of the sum of two numbers_en.vtt
002 2-Sign-template.ipynb
002 Modify input, output & weights.mp4
002 Modify input, output & weights_en.vtt
002 Sign-network.png
003 Add an activation function to the neural network.mp4
003 Add an activation function to the neural network_en.vtt
004 Modify accuracy and error functions.mp4
004 Modify accuracy and error functions_en.vtt
005 Modify gradient of the error function.mp4
005 Modify gradient of the error function_en.vtt
006 Training & Testing the modified neural network.mp4
006 Training & Testing the modified neural network_en.vtt
04 - Same code, different problem Image recognition
001 3-Number-recognition-template.ipynb
001 Digit-network.png
001 [Project 3] Same neural network Applied to recognize hand-written digits.mp4
001 [Project 3] Same neural network Applied to recognize hand-written digits_en.vtt
002 3-Number-recognition-template.ipynb
002 Apply our neural network to the new problem Number recognition.mp4
002 Apply our neural network to the new problem Number recognition_en.vtt
002 Digit-network.png
003 Improve the gradient function.mp4
003 Improve the gradient function_en.vtt
004 Analysis of the trained neural network.mp4
004 Analysis of the trained neural network_en.vtt
05 - Outlook & Goodbye
001 How to improve the network.mp4
001 How to improve the network_en.vtt
002 Outlook Pretrained neural networks & Machine learning in Wolfram Mathematica.mp4
002 Outlook Pretrained neural networks & Machine learning in Wolfram Mathematica_en.vtt
003 Goodbye!.mp4
003 Goodbye!_en.vtt
06 - [Resources]
001 [Installation] Python and Jupyter Notebook via Anaconda.mp4
001 [Installation] Python and Jupyter Notebook via Anaconda_en.vtt
002 0-Interpolation-template.ipynb
002 1-Addition-template.ipynb
002 2-Sign-template.ipynb
002 3-Number-recognition-template.ipynb
002 Addition-network.png
002 Digit-network.png
002 Sign-network.png
002 Template files.html
003 0-Interpolation.ipynb
003 1-Addition-template.ipynb
003 2-Sign.ipynb
003 3-Number-recognition.ipynb
003 Addition-network.png
003 Digit-network.png
003 Finalized jupyter notebooks.html
003 Sign-network.png
Bonus Resources.txt
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