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Lava Kafle liked Jobil Louis's blog post Image Processing and Neural Networks Intuition: Part 1

Nov 22, 2018

Dr S Kotrappa liked Jobil Louis's blog post Optimization techniques: Finding maxima and minima

Jul 12, 2018

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

Artur Kovalchuk liked Jobil Louis's blog post Image Processing and Neural Networks Intuition: Part 1

Jan 22, 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

Duane Baker liked Jobil Louis's blog post Optimization techniques: Finding maxima and minima

Jan 12, 2018

Julian Hatwell liked Jobil Louis's blog post Optimization techniques: Finding maxima and minima

Jan 11, 2018

Samuel T. Christel liked Jobil Louis's blog post Optimization techniques: Finding maxima and minima

Jan 9, 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

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Posted on January 20, 2018 at 5:00pm 0 Comments 0 Likes

Posted on January 16, 2018 at 8:00pm 0 Comments 2 Likes

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…

ContinuePosted on January 2, 2018 at 3:30pm 0 Comments 4 Likes

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…

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