classify mnist datasets using ridge regression, optimize the algorithem with SGD, stochastic dual coordinate ascent, and mini-batching
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Updated
Jul 18, 2017 - Python
classify mnist datasets using ridge regression, optimize the algorithem with SGD, stochastic dual coordinate ascent, and mini-batching
Linear Regression with TensorFlow 2 (using Mini-Batch Gradient Descent)
This project explored the Tensorflow technology, tested the effects of regularizations and mini-batch training on the performance of deep neural networks
Regression models on Boston Houses dataset
3-layer linear neural network to classify the MNIST dataset using the TensorFlow
Google Street View House Number(SVHN) Dataset, and classifying them through CNN
MNIST Handwritten Digits Classification using 3 Layer Neural Net 98.7% Accuracy
Predicting House Price from Size and Number of Bedrooms using Multivariate Linear Regression in Python from scratch
A basic neural net built from scratch.
Implementation of a support vector machine classifier using primal estimated sub-gradient solver in C++ and CUDA for NVIDIA GPUs
A simplified explanation of gradient descent for linear regression in python using numpy
Curso Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization. Segundo curso del programa especializado Deep Learning. Este repositorio contiene todos los ejercicios resueltos. https://www.coursera.org/learn/neural-networks-deep-learning
[Coursera] Deep Learning Specialization on Coursera
Short description for quick search
Notebook for quick search
The laboratory from CLOUDS Course at EURECOM
Coursera - Deep Learning Specialization - deeplearning.ai
A five-course specialization covering the foundations of Deep Learning, from building CNNs, RNNs & LSTMs to choosing model configurations & paramaters like Adam, Dropout, BatchNorm, Xavier/He initialization, and others.
rede neural totalmente conectada, utilizando mini-batch gradient descent e softmax para classificação no dataset MNIST
Various methods for Deep Learning, SGD and Neural Networks.
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