This document summarizes some potentially useful papers and code repositories on Sentiment analysis / document classification
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#!/bin/bash | |
################################################################################## | |
# ---------------------------------------------------------------- | |
# THIS SCRIPT WILL HELP YOUR AUTOMATE THE DOCKER INSTALATION STEPS | |
# ---------------------------------------------------------------- | |
# Test was run via aws ec2 instance. | |
# | |
# AUTHOR: | |
# Name: Allie Silver Ubisse |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
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#!/bin/bash | |
################################################################################## | |
# ---------------------------------------------------------------- | |
# THIS SCRIPT WILL HELP YOUR AUTOMATE THE DOCKER INSTALATION STEPS | |
# ---------------------------------------------------------------- | |
# Test was run via aws ec2 instance. | |
# | |
# AUTHOR: | |
# Name: Allie Silver Ubisse |
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1. Introduction | |
What do we understand when we talk about the term Machine-Learning in today’s perspective of Technology? What can we achieve through means of complex algorithms? | |
Simple answer to these questions comes from the need to recognize patterns, make predictions and the ability of a machine to operate over data without having to give static program instructions to it. Machine Learning is the field of computer science that gives machines/computers the ability to learn without being explicitly programmed. It is employed in a range of computing tasks where designing & programming explicit algorithms with great performance is infeasible, this includes email filtering, intruder detection in networks, computer vision, optical character recognition (OCR), etc. | |
Machine learning is considered to be closely related to computational statistics which as we know focuses on prediction-making through the use of computers. It is also conflated with Data mining because of the exploratory data analysis involved in b |
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with mlflow.start_run(experiment_id=1, run_name="top_lever_run") as run: | |
with mlflow.start_run(experiment_id=1, run_name="subrun1",nested=True) as subrun1: | |
mlflow.log_param("p1","red") | |
mlflow.log_metric("m1", 5.1) | |
with mlflow.start_run(experiment_id=1, run_name="subsubrun1",nested=True) as subsubrun1: | |
mlflow.log_param("p3","green") | |
mlflow.log_metric("m3", 5.24) | |
with mlflow.start_run(experiment_id=1, run_name="subsubrun2", nested=True) as subsubrun2: | |
mlflow.log_param("p4","blue") | |
mlflow.log_metric("m5", 3.25) |