Some common module used in deep learning and machine learning
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Updated
Feb 23, 2019 - Python
Some common module used in deep learning and machine learning
TVLARS - A Fast Convergence Optimizer for Large Batch Training
This repository focuses on the Neural Networks and deep learning. It is a workbook you can refer it for a reference. I'll Include following content here: Neurons, perceptrons, weight and biases, learning rate, activation form, hyper parameters, RNN, CNN and other popular concepts.
Investigating Gradient Descent behavior in linear regression
Convenience classes/functions for common machine learning tasks
This project involves training a machine learning model and plotting its learning curves to analyze training and testing accuracies, utilizing Java for model execution and Python for data visualization. It includes commands for compiling and running the Java program, generating plots, and sending results via email.
Interactive Learning Rate Scheduler for PyTorch
Building Deep Neural Network for Google Street View Dataset
Neural net solver with auto-tuned hyperparameters (Python 3.5)
This repository contains additional features, extended to the traditional Word2Vec library, launched in 2013
Stock price forecasting using time series data with Sequential model developed on LSTM architecture utilizing optimizer with learning rate
useful function for applying discriminative learning rates to a model children
Some tools for large mini-batch deep learning in standalone and distributed (main) scenarios.
Propulsive Rocket Landing Simulation Using Q - Learning
Univariate linear regression model to predict food truck profits | Multivariate linear regression model to predict housing prices
Customizible neural net constructor.
Analyze the performance of 7 optimizers by varying their learning rates
Реализация алгоритма обратного распространения ошибки для обучения нейронной сети для распознавания рукописных цифр
The main concentration of this project lies on image calssification using traditional CNN(Convolution Neural Networks), and also a couple of "BASE MODELS" such as "RestNet50", "DenseNet121" and "EfficientNetB0" that upgraded the performance of our CNN, followed by the Fully Connected NN, that we are using to train our model on.
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