This repository contains mini projects in machine learning in deep learing with notebook files
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Updated
Apr 21, 2024 - Python
This repository contains mini projects in machine learning in deep learing with notebook files
This repository consists of sample notebook which can take you through the basic deep learning excersises in Tensorflow and Pytorch
these are my projects that i submitted for AIML course with great lakes & some good notebooks with great explaination of the topics
Comparison of methods for predicting electricity consumption of a large non-residential building.
This repository houses a learning project focused on predicting stock prices utilizing Long Short-Term Memory (LSTM) networks. The project was developed while exploring Python for data analysis and machine learning within Jupyter Notebooks.
This repo contains my implementaions of notebooks in TensorFlow (not in Trax which is used in the course) of Natural Language Processing Specialization: Course4 (NLP with Attention) by deeplearning.ai on Coursera
Market Trend analysis using LSTM Model. Use Jyputer Notebook .
This notebook builds an artificial recurrent neural network called Long Short Term Memory (LSTM) to predict the adjusted closing price of the GOOGLE. Index by reiterating over the past 60 day stock price
This notebook will show you how to implement a deep leaning algorithm (LSTM) on the Amazon Alexa Reviews dataset
The python codes of vue4logs code converted from Ipython notebooks. These codes are used in creating benchmarks.
An LSTM model is used to predict optimal England and Australian Squads for the next ashes series based on the recent performances of the players. Jupyter notebooks and scraped datasets are attached with this repository.
Time series prediction Notebooks
Project made in Jupyter Notebook with "News Headlines Dataset For Sarcasm Detection" from Kaggle.
A ML Notebook to forecast the electrical consumption of the city Haemstead using time series analysis,recurrent Neural Network(RNN).
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.
This notebook attempts to perform time-series forecasting using ARIMA and LSTM.
The goal of this notebook is to implement and compare different approaches to predict item-level sales at different store locations.
Aplicación de modelos estadísticos al movimiento diario del Bitcoin con Python via Jupyter notebook.
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