Explains the basic concepts of NN like activation functions, forward propagation, backward propagation, gradient descent, finding the optimized weights and bias etc.
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Updated
Jun 8, 2018 - Jupyter Notebook
Explains the basic concepts of NN like activation functions, forward propagation, backward propagation, gradient descent, finding the optimized weights and bias etc.
The code of forward propagation , cost function , backpropagation and visualize the hidden layer.
Implemented Convolutional Neural Network, LSTM Neural Network, and Neural Network From Scratch in Python Language.
Curso Neural Networks and Deep Learning. Primer curso del programa especializado Deep Learning. Este repositorio contiene todos los ejercicios resueltos. https://www.coursera.org/learn/neural-networks-deep-learning
It's a demonstration for implementing NN without using any deep learning library.
A highly modular design and implementation of fully-connected feedforward neural network structured on NumPy matrices
CNN, ANN, Python, Matlab
Python version of Andrew Ng's Machine Learning Course.
Learning about Perceptron and Multi layered perceptron
Deep Learning Specialization (5 Courses) . Course offered by deeplearning.ai and Coursera. Taught by Andrew Ng.
backward_step, a FreeFem++ code which solves the backward step benchmark problem for Navier Stokes flow.
I have implemented some AI projects from scratch implementation without explicit use of the built-in-libraries and thus added to this repo.
Create a Deep Neural Network from Scratch using Python3.
A simple mimicking of TensorFlow, which including forward and backward propogation.
仅使用numpy从头开始实现神经网络,包括反向传播公式推导过程; numpy构建全连接层、卷积层、池化层、Flatten层;以及图像分类案例及精调网络案例等,持续更新中... ...
To build a multilayer perceptron model and to train datas from it
This repository provides the Implementation of logic gates using neural networks.
This is a project to recognize cat using logistic regression with Neural Network concepts of backward and forward propagation from DeepLearning.AI.
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