Deep Learning Machine Learning Templates
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Updated
Jul 24, 2023 - Jupyter Notebook
Deep Learning Machine Learning Templates
A series of documented Jupyter notebooks implementing various neural nets.
A repository of notebooks containing recurrent neural nets engineered for NLP tasks
Accompanying notebook for the Entailment with Tensorflow article.
This repo helps keep track about exercises, Jupyter Notebooks and projects from the Deep Learning Nanodegree Program offered at Udacity.
Learning how to use jupyter notebook, tensorflow, numpy and other librairies for machine learning projects. Following Thibaut Neveu's tutorial.
This repository contains all the notebooks, datasets and projects that have been implemented as a part of my Tensorflow 2 Keras bootcamp.
Google Colab notebooks and sample datasets for the intensive Crash Course in Deep Learning at Kaunas University of Applied Sciences, Kaunas, Lithuania
This project is intended to use Amazon's SageMaker platform, it is assumed to have a working notebook instance which can be used to clone the deployment repository.
Sentiment analysis lets you analyze the sentiment behind a given piece of text. In this notebook, we have done sentimental analysis for amazon shoe reviews, using 2 RNN models(LSTM and GRU)
The repository contains notebooks created for collecting and preprocessing the corpus of diary entries and for experiments on creating models for predicting gender, age groups of authors and the time period of text creation.
In this notebook, I built gradient boosting classifier and LSTM model to classify and predict the mineral scaling potential of formation water in US shale basins.
this is the 3 project of the deep learning nanodegree program of udacity
A series of notebooks to familiarize with some important data processing and analysis pipelines based on PyTorch
This repository contains notebooks from the Coursera specialization Deep Learning.
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
Python Jupyter notebooks for building and evaluating deep learning models using both the FNG values and simple closing prices to determine if the FNG indicator provides a better signal for cryptocurrencies than the normal closing price data.
Notebooks containing examples for different DNN components
Deep Learning Projects using Convolutional, Recurrent, and Generative Adversarial Neural Networks.
Implementation notebooks and scripts of the computer vision nanodegree program from udacity in pytorch.
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