Hi Guys! In this blogs, I will share my knowledge, after reading this research paper, what it is all about! Before I proceed it, I want you to know that I didn’t go and study very extensively. It was only means to understand that

  • What this research paper is all about?
  • How was different than previous state-of-the-art model?
  • What was the result of this novel approach compared to old ones (previous ones)?

So. all of these are written here as a key points.

Inception v2 is the extension of Inception using Factorizing Asymmetric Convolutions and Label Smoothing.

Inception v3 (Inception…


Hi Guys! In this blogs, I will share my knowledge, after reading this research paper, what it is all about! Before I proceed it, I want you to know that I didn’t go and study very extensively. It was only means to understand that

  • What this research paper is all about?
  • How was different than previous state-of-the-art model?
  • What was the result of this novel approach compared to old ones (previous ones)?

So. all of these are written here as a key points.

**It is the extension of GoogLeNet, which introduced Batch Normalization**

Abstract

  1. What was a difficulty till now? Training…

Hi Guys! In this blogs, I will share my knowledge, after reading this research paper, what it is all about! Before I proceed it, I want you to know that I didn’t go and study very extensively. It was only means to understand that

  • What this research paper is all about?
  • How was different than previous state-of-the-art model?
  • What was the result of this novel approach compared to old ones (previous ones)?

So. all of these are written here as a key points.

Abstract

  1. What the purpose? for setting the new state of the art for classification and detection in the…

Hi Guys! In this blog, I will share my points after went through VGG research. Before i proceed it, I want you to know that I didn’t go and study very extensively. It was only means to understand that

  • What this research paper is all about?
  • How was different than previous state-of-the-art model?
  • What was the result of this novel approach compared to old ones (previous ones)?

So. all of these are written here as a key points.

Abstract

  1. What was the contribution in this paper? “Increase depth” using an architecture with very small (3x3) convolution filters.
  2. What did its proven…

Hi Guys! In this blog, I would like to share points on this research paper ‘GloVe: Global Vectors for Word Representation’.

I went all through this research paper, and found that we really need to have a prior knowledge of Maths (Number theory) and Deep Learning concept.

You will really find confusing some of the equations, but these are comes from Number theory. If you have strong intuition with Number theory, you will feel ease. …


Hi Guys! In this blog, I would like to share my knowledge on review this research paper ‘Efficient Estimation of Word Representations in Vector Space’ where it proposed CBOW and Skip-gram novel architecture using Deep Learning.

1. Introduction

Word embedding means representing a word into continuous (or numerical) vector representation.

A simple models, statistical language modelling representation(like Bag-Of-Word (BoW), TF-IDF (Term Frequency-Inverse Document Frequency), N-gram model), has good choice with reason — simplicity, robustness and observation. However, its has certain limitation —

  • Lack of semantics meaning. For example, tasty and delicious will consider different, however, meaning is same.
  • Amount of relevant in-domain…


Hi Guys! This blog contains the summary of all gradient descent optimization techniques.

In this blog, I am not going to focus on more in writing like paragraph and all details about it. I will just use the snapshot/pictures because more people tends to remember/understands in images than textbook.

Notation

List of Notation

I. Gradient Descent (GD)

If you are aware of how all the parameters are updated.


Hi Guys! In this blog, I will explain you intuitive behind how object detection actually works using R-CNN with provided intense well-documented Code from Scratch.

Straightforward Meaning…

R-CNN stands for **Region-Based Convolution Neural Network**. Its means that:

“ First, each image will crop into region of interest (ROI) using any Region-Based Algorithm and then feed these cropped image to CNN Network”

So, In this case Selective Search Algorithm has been chosen as a Region-Based Algorithm. If you want to read in more detail, Kindly go in this as I’ve explained this in very detail and as simplified as possible.

Things that need to be required…

Before going through…


Hi Guys! In this blog, I’m going to explain how selective search algorithm works (in this research paper) with easiest way step by step as possible.

Definition of Segmentation

I hope you are aware of what is Segmentation of an image? It is a process of breaking down all pixels and group them which have similar pixel value to make multiple regions.

Source: https://medium.com/cogitotech/what-is-the-difference-between-image-segmentation-and-classification-in-image-processing-303d1f660626

You can see:

  • Bed have grouped into single pixel.
  • Pillow have grouped into single pixel. etc

Objective: Selective search algorithm used to perform segmentation to get the object localization.

Selective Search

Before proceeding, let us understand some concepts behinds this proposal. First, see…


Hi Guys!

Image to Image translation is one of the computer visions where most of the practical applications are used. For example, I have an image and I want to recreate the same image with different styles (like into sketch drawing).

Introduction

Various researchers perform on a different specific domain (like translate image from daytime to nighttime, translate an original image into the context of artistic style, etc.) and some of the models work perfectly in some domain and may not work in other domains.

The key challenge in getting trained in a model is time and the cost. A lot…

Sahil -

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