Goto

Collaborating Authors

Results


Deep Learning Prerequisites: Linear Regression in Python

#artificialintelligence

This course teaches you about one popular technique used in machine learning, data science and statistics: linear regression. This course teaches you about one popular technique used in machine learning, data science and statistics: linear regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own linear regression module in Python. Linear regression is the simplest machine learning model you can learn, yet there is so much depth that you'll be returning to it for years to come.


Machine Learning & Deep Learning in Python & R

#artificialintelligence

In this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.


Machine Learning & Deep Learning in Python & R

#artificialintelligence

In this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.


2021 Data Science/MachineLearning Project Deployment Mastery

#artificialintelligence

End-to-End Data Science and Machine Learning (Right from learning the basics to building the models to deploying the models) Learn Through More Than 20 Projects and Assignments Master Machine Learning With Python Learn How to Deploy Machine Learning Models Practical Hands-On Data Science Projects Mastery Build And Deploy Machine Learning models On Flask, Heroku, Streamlit, AWS,Google Cloud, Microsoft Azure Create Robust Machine Learning Models with CatBoost,XGBoost, LightGbm Learn Different Machine Learning Algorithms such as Linear And Logistic regression, Naive Bayes,KNN,SVM,K-means, etc. Deal With Data Imbalance (Upsampling/Downsampling/SMOTE) Gain Confidence In Performing Exploratory Data Analysis (EDA) Choose The Right Machine Learning Model For Your Problem Statement Access To Exclusive Community To Learn With Others And Answer Your Queries Learn The Necessary Statistics Master Data Analysis This course is a beginner to advance level course with all the tutorials on the lessons covered in the projects included If you are a complete beginner, you have all the lessons from introduction to python to building projects and deployment. If you already have have the basics, we have more than 20 projects and deployment for you to practice. If you are a complete beginner, you have all the lessons from introduction to python to building projects and deployment. If you already have have the basics, we have more than 20 projects and deployment for you to practice. Then this course is for you!!


Machine Learning & Deep Learning in Python & R

#artificialintelligence

In this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.


Deep Learning Prerequisites: Linear Regression in Python

#artificialintelligence

We show you how one might code their own linear regression module in Python. Linear regression is the simplest machine learning model you can learn, y


Machine Learning & Deep Learning in Python & R

#artificialintelligence

In this section we will learn – What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.


Decision Trees, Random Forests, AdaBoost & XGBoost in Python

#artificialintelligence

In this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.


Machine Learning & Deep Learning in Python & R

#artificialintelligence

In this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.


Training With Data Dependent Dynamic Learning Rates

arXiv.org Artificial Intelligence

Recently many first and second order variants of SGD have been proposed to facilitate training of Deep Neural Networks (DNNs). A common limitation of these works stem from the fact that they use the same learning rate across all instances present in the dataset. This setting is widely adopted under the assumption that loss functions for each instance are similar in nature, and hence, a common learning rate can be used. In this work, we relax this assumption and propose an optimization framework which accounts for difference in loss function characteristics across instances. More specifically, our optimizer learns a dynamic learning rate for each instance present in the dataset. Learning a dynamic learning rate for each instance allows our optimization framework to focus on different modes of training data during optimization. When applied to an image classification task, across different CNN architectures, learning dynamic learning rates leads to consistent gains over standard optimizers. When applied to a dataset containing corrupt instances, our framework reduces the learning rates on noisy instances, and improves over the state-of-the-art. Finally, we show that our optimization framework can be used for personalization of a machine learning model towards a known targeted data distribution.