#### Deep Networks Are Kernel Machines

- Frank
- February 18, 2021
- AI
- artificial intelligence
- Arxiv
- data representations
- Deep Learning
- deep neural networks
- explained
- kernel machines
- kernel trick
- learning theory
- Linear Regression
- Machine Learning
- machine learning theory
- math proof
- nearest neighbor
- Neural Networks
- neural networks gradient descent
- Paper
- pedro domingos
- proof
- representation learning
- representations
- representer theorem
- SGD
- stochastic gradient descent
- Support Vector Machine
- SVM
- what is deep learning

Yannic Kilcher explains the paper “Every Model Learned by Gradient Descent Is Approximately a Kernel Machine.” Deep Neural Networks are often said to discover useful representations of the data. However, this paper challenges this prevailing view and suggest that rather than representing the data, deep neural networks store superpositions of the training data in their […]

Read More#### Principal Component Analysis (PCA) Explained

Principal component analysis (PCA) is a workhorse algorithm in statistics, where dominant correlation patterns are extracted from high-dimensional data. Steve Brunton explains it in this great video.

Read More#### Linear Regression: Concepts and Applications With TensorFlow 2.0

Linear regression is likely the first algorithm that you would learn when starting down a career path in data science or AI, because it’s simple to implement and easy to apply in real-time. Here’s a great primer on how to do linear regression in TensorFlow 2.0. This algorithm is widely used in data science and […]

Read More#### Neural Networks and Linear Regression

- Frank
- March 1, 2020
- Beginner
- Computers
- continuous
- Data
- Data Science
- datascience
- easy
- Education
- Graph
- graphing
- how to
- infer
- input
- intro
- Learning
- linear
- Linear Regression
- Machine
- Machine Learning
- made easy
- Math
- ML
- neural net
- Neural Network
- plot
- predict
- Prediction
- Predictions
- Programming
- Python
- Regression
- relationship
- scatter
- scatterplot
- series
- simple
- simple linear regression
- Tutorial
- variable

On Friday, someone asked me about linear regression with neural networks. I didn’t have a good answer – I knew that you *could* do linear regression but neural networks, but never had actually done it in practice. Promising to learn more, I came across this video by giant_neural_network on YouTube.

Read More#### Stanford Machine Learning Lecture on Linear Classifiers and SGD

Professor Percy Liang, Associate Professor of Computer Science and Statistics, delivers a lecture on Linear Classifiers and SGD.

Read More#### What Can NFL Team Loyalties Teach Us About Linear Regression

In this LinkedIn Live session, I noticed an interesting pattern of NFL team merchandise that acted as an indicator of where in the state was more “Baltimore” or more “DC.”

Read More#### Use ML.NET in Python to Create a Linear Regression Model with NimbusML

Jon Wood demonstrates how to use ML.NET within Python by using the NimbusML package. This video also gives an example of creating a linear regression model. Notebook – https://github.com/jwood803/MLNetExamples/blob/master/MLNetExamples/Notebooks/NimbusML/Regression.ipynb NimbusML documentation – https://docs.microsoft.com/en-us/nimbusml/overview

Read More#### ML and TensorFlow: Linear Regression

Here’s an interesting article on linear regression in TensorFlow. Linear Regression is an important algorithm of supervised learning. In this article, I am going to re-use the following notations that I have referred from [1] (in the References section): Read more www.codeproject.com

Read More