|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Docker Tutorial||1,224||4 months ago||6||mit||Python|
|Docker 基本教學 - 從無到有 Docker-Beginners-Guide 教你用 Docker 建立 Django + PostgreSQL 📝|
|Neural Networks Demystified||1,161||3 years ago||8||Jupyter Notebook|
|Supporting code for short YouTube series Neural Networks Demystified.|
|Convolutional_neural_network||337||4 years ago||7||Jupyter Notebook|
|This is the code for "Convolutional Neural Networks - The Math of Intelligence (Week 4)" By Siraj Raval on Youtube|
|Make_a_neural_network||196||4 years ago||13||Python|
|This is the code for the "Make a Neural Network" - Intro to Deep Learning #2 by Siraj Raval on Youtube|
|Lstm_networks||176||4 years ago||5||Jupyter Notebook|
|This is the code for "LSTM Networks - The Math of Intelligence (Week 8)" By Siraj Raval on Youtube|
|Recurrent_neural_network||143||4 years ago||2||bsd-2-clause||Jupyter Notebook|
|This is the code for "Recurrent Neural Networks - The Math of Intelligence (Week 5)" By Siraj Raval on Youtube|
|Deep Learning Resources||138||3 years ago||1||mit|
|A Collection of resources I have found useful on my journey finding my way through the world of Deep Learning.|
|Pythonnnexample||126||4 years ago||HTML|
|Annotations for the Sirajology Python NN Example. This code comes from a demo NN program from the YouTube video https://youtu.be/h3l4qz76JhQ. The program creates an neural network that simulates the exclusive OR function with two inputs and one output.|
This is the code for "LSTM Networks - The Math of Intelligence (Week 8)" By Siraj Raval on Youtube
This is the code for this video on Youtube by Siraj Raval as part of the Math of Intelligence course. This is an LSTM (long short term memory) network built using just numpy. LSTM's are an improvement to recurrent networks, able to remember long range dependencies.
Install dependencies using pip
jupyter notebook into terminal and the code will pop up in your browser.
Credits for the code go to kevin-bruhwiler. I've merely created a wrapper to get people started.