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# Jobil Louis's Page

## Latest Activity Jobil Louis posted a blog post

### Image Processing and Neural Networks Intuition: Part 2

In a previous post I talked about how to preprocess and explore image dataset. In this post, I will talk about how to model image data with neural networks having a single neuron, using sigmoid function. Original version of this blog can be found here. This is equivalent to logistic regression. Only difference is the way we estimate weights(coeffcients) of the the inputs. The traditional way of estimating logistic regression…See More
Jan 22, 2018 Jobil Louis's blog post was featured

### Image Processing and Neural Networks Intuition: Part 2

In a previous post I talked about how to preprocess and explore image dataset. In this post, I will talk about how to model image data with neural networks having a single neuron, using sigmoid function. Original version of this blog can be found here. This is equivalent to logistic regression. Only difference is the way we estimate weights(coeffcients) of the the inputs. The traditional way of estimating logistic regression…See More
Jan 22, 2018 Jobil Louis posted blog posts
Jan 17, 2018 Jobil Louis's blog post was featured

### Image Processing and Neural Networks Intuition: Part 1

In this series, I will talk about training a simple neural network on image data. To give a brief overview, neural networks is a kind of supervised learning. By this I mean, the model needs to train on historical data to understand the relationship between input variables and target variables. Once trained, the model can be used to predict target variable on new input data. In the previous posts, we have written about linear, lasso and ridge regression. All those methods come under supervised…See More
Jan 16, 2018 Jobil Louis posted a blog post

### Optimization techniques: Finding maxima and minima

In the last post, we talked about how to estimate the coefficients or weights of linear regression. We estimated weights which give the minimum error. Essentially it is an optimization problem where we have to find the minimum error(cost) and the corresponding coefficients. In a way, all supervised learning algorithms have optimization at the crux of it where we will…See More
Jan 3, 2018 Jobil Louis's blog post was featured

### Optimization techniques: Finding maxima and minima

In the last post, we talked about how to estimate the coefficients or weights of linear regression. We estimated weights which give the minimum error. Essentially it is an optimization problem where we have to find the minimum error(cost) and the corresponding coefficients. In a way, all supervised learning algorithms have optimization at the crux of it where we will…See More
Jan 3, 2018

## Profile Information

My Web Site Or LinkedIn Profile
http://https://www.linkedin.com/in/jobillouisjoseph/
Professional Status
Technical
Your Job Title:
Graduate Student
Interests:
Finding a new position, Networking, New venture

## Jobil Louis's Blog

### Image Processing and Neural Networks Intuition: Part 2

Posted on January 20, 2018 at 5:00pm

In a previous post I talked about how to preprocess and explore image dataset. In this post, I will talk about how to model image data with neural networks having a single neuron, using sigmoid function. Original version of this blog can be found here. This is equivalent to logistic…

Continue

### Image Processing and Neural Networks Intuition: Part 1

Posted on January 16, 2018 at 8:00pm

In this series, I will talk about training a simple neural network on image data. To give a brief overview, neural networks is a kind of supervised learning. By this I mean, the model needs to train on historical data to understand the relationship between input variables and target variables. Once trained, the model can be used to predict target variable on new input data. In the previous posts, we have written about linear, lasso and ridge regression. All those methods come under…

Continue

### Optimization techniques: Finding maxima and minima

Posted on January 2, 2018 at 3:30pm

In the last post, we talked about how to estimate the coefficients or weights of linear regression. We estimated weights which give the minimum error. Essentially it is an optimization problem where we have to find the minimum error(cost) and the corresponding coefficients. In a way, all supervised learning algorithms have optimization at the crux of it where…

Continue

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