| 001 2-Sign-template.ipynb | 15.8 KB | ||
| 001 3-Number-recognition-template.ipynb | 73.3 KB | ||
| 001 Digit-network.png | 305.4 KB | ||
| 001 How to improve the network.mp4 | 32.7 MB | ||
| 001 How to improve the network_en.vtt | 6.8 KB | ||
| 001 Let's get started!.html | 307.2 B | ||
| 001 Overview of the course.mp4 | 45.9 MB | ||
| 001 Overview of the course_en.vtt | 3.4 KB | ||
| 001 Sign-network.png | 246.8 KB | ||
| 001 [Installation] Python and Jupyter Notebook via Anaconda.mp4 | 42.7 MB | ||
| 001 [Installation] Python and Jupyter Notebook via Anaconda_en.vtt | 10 KB | ||
| 001 [Project 2] Complete neural network Sign of the sum of two numbers.mp4 | 36.2 MB | ||
| 001 [Project 2] Complete neural network Sign of the sum of two numbers_en.vtt | 4.3 KB | ||
| 001 [Project 3] Same neural network Applied to recognize hand-written digits.mp4 | 27.6 MB | ||
| 001 [Project 3] Same neural network Applied to recognize hand-written digits_en.vtt | 3.3 KB | ||
| 002 0-Interpolation-template.ipynb | 12 KB | ||
| 002 1-Addition-template.ipynb | 10.9 KB | ||
| 002 2-Sign-template.ipynb | 15.8 KB | ||
| 002 3-Number-recognition-template.ipynb | 73.3 KB | ||
| 002 Addition-network.png | 92.5 KB | ||
| 002 Apply our neural network to the new problem Number recognition.mp4 | 72 MB | ||
| 002 Apply our neural network to the new problem Number recognition_en.vtt | 12.3 KB | ||
| 002 Digit-network.png | 305.4 KB | ||
| 002 From interpolation to neural networks.mp4 | 46.9 MB | ||
| 002 From interpolation to neural networks_en.vtt | 5 KB | ||
| 002 Modify input, output & weights.mp4 | 89.7 MB | ||
| 002 Modify input, output & weights_en.vtt | 15.4 KB | ||
| 002 Outlook Pretrained neural networks & Machine learning in Wolfram Mathematica.mp4 | 36.3 MB | ||
| 002 Outlook Pretrained neural networks & Machine learning in Wolfram Mathematica_en.vtt | 9.6 KB | ||
| 002 Sign-network.png | 246.8 KB | ||
| 002 Template files for this course.html | 716.8 B | ||
| 002 Template files.html | 102.4 B | ||
| 003 0-Interpolation-template.ipynb | 12 KB | ||
| 003 0-Interpolation.ipynb | 37.6 KB | ||
| 003 1-Addition-template.ipynb | 10.9 KB | ||
| 003 2-Sign.ipynb | 136.7 KB | ||
| 003 3-Number-recognition.ipynb | 361.4 KB | ||
| 003 Add an activation function to the neural network.mp4 | 58 MB | ||
| 003 Add an activation function to the neural network_en.vtt | 10.8 KB | ||
| 003 Addition-network.png | 92.5 KB | ||
| 003 Digit-network.png | 305.4 KB | ||
| 003 Finalized jupyter notebooks.html | 204.8 B | ||
| 003 Goodbye!.mp4 | 24.1 MB | ||
| 003 Goodbye!_en.vtt | 1.6 KB | ||
| 003 Improve the gradient function.mp4 | 65.4 MB | ||
| 003 Improve the gradient function_en.vtt | 9.8 KB | ||
| 003 Interpolation (or regression) - The fundamental principle of machine learning.mp4 | 189.6 MB | ||
| 003 Interpolation (or regression) - The fundamental principle of machine learning_en.vtt | 31.2 KB | ||
| 003 Sign-network.png | 246.8 KB | ||
| 003 What are neural networks.mp4 | 82.6 MB | ||
| 003 What are neural networks_en.vtt | 8.7 KB | ||
| 004 1-Addition-template.ipynb | 10.9 KB | ||
| 004 Addition-network.png | 92.5 KB | ||
| 004 Analysis of the trained neural network.mp4 | 61.5 MB | ||
| 004 Analysis of the trained neural network_en.vtt | 11.4 KB | ||
| 004 Modify accuracy and error functions.mp4 | 46.9 MB | ||
| 004 Modify accuracy and error functions_en.vtt | 9.1 KB | ||
| 004 [Project 1] Most simple neural network Sum of two numbers.mp4 | 29.2 MB | ||
| 004 [Project 1] Most simple neural network Sum of two numbers_en.vtt | 3.5 KB | ||
| 005 1-Addition-template.ipynb | 10.9 KB | ||
| 005 Addition-network.png | 92.5 KB | ||
| 005 Modify gradient of the error function.mp4 | 60.5 MB | ||
| 005 Modify gradient of the error function_en.vtt | 10.9 KB | ||
| 005 Prepare the training and testing data.mp4 | 41 MB | ||
| 005 Prepare the training and testing data_en.vtt | 7.8 KB | ||
| 006 Initialize the weights & Calculate the output.mp4 | 42.7 MB | ||
| 006 Initialize the weights & Calculate the output_en.vtt | 9 KB | ||
| 006 Training & Testing the modified neural network.mp4 | 57.6 MB | ||
| 006 Training & Testing the modified neural network_en.vtt | 9.3 KB | ||
| 007 Accuracy & Error functions.mp4 | 63.7 MB | ||
| 007 Accuracy & Error functions_en.vtt | 12.4 KB | ||
| 008 Gradient of the error function.mp4 | 39.2 MB | ||
| 008 Gradient of the error function_en.vtt | 7.8 KB | ||
| 009 Training the neural network via gradient descent.mp4 | 58.5 MB | ||
| 009 Training the neural network via gradient descent_en.vtt | 11.8 KB | ||
| 010 Using the trained network on the test data.mp4 | 47.9 MB | ||
| 010 Using the trained network on the test data_en.vtt | 7.7 KB | ||
| Bonus Resources.txt | 409.6 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 90 total files | |||
Neural Networks in Python from Scratch: Learning by Doing
https://DevCourseWeb.com
Published 06/2022
Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.36 GB | Duration: 29 lectures • 3h 32m
From intuitive examples to image recognition in 3 hours - Experience neuromorphic computing & machine learning hands-on
What you'll learn
Program neural networks for 3 different problems from scratch in plain Python
Start simple: Understand input layer, output layer, weights, error function, accuracy, training & testing at an intuitive example
Complicate the problem: Introduce hidden layers & activation functions for building more useful networks
Real-life application: Use this network for image recognition
Requirements
Basic programing skills are desired if you want to program along with me. We use Python3 without any advanced modules.
Description
This course is for everyone who wants to learn how neural networks work by hands-on programming!
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| 4 GB | freecoursewb | 6 months | 24 | 10 | |
| 4.1 GB | freecoursewb | 7 months | 0 | 0 | |
| 785.3 MB | freecoursewb | 10 months | 13 | 1 | |
| 3.1 GB | freecoursewb | 2 years | 0 | 0 | |
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Udemy - Deep Learning - Neural Networks In Python Using Case Studies Posted by
freecoursewb in Other
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2.4 GB | freecoursewb | 2 years | 3 | 0 |
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