Coders

                               As a college student in the tech industry, staying up-to-date with the latest trends and tools is essential to success. Technology is constantly evolving, and new tools and platforms are emerging all the time. In this article, I will be sharing my tips and strategies for staying on top of the latest tech trends and tools as a college student. * Attend Conferences and Workshops: Attending tech conferences and workshops is an excellent way to learn about the latest trends and tools in your field. These events often feature leading experts in the tech industry and provide opportunities to network with other students and professionals. * Follow Industry Leaders on Social Media: Following industry leaders on social media platforms like Twitter, LinkedIn, and Instagram can help you stay informed about the latest tech trends and tools. You can also participate in online discussio...

                                    As a college student pursuing a career in tech, I have been interested in the concept of remote work for a long time. With the COVID-19 pandemic, remote work has become more common than ever, and many developers are now facing the benefits and challenges of working from home. In this article, I will be discussing my personal experiences and observations about remote work as a college student. Benefits: Flexibility : Remote work provides the flexibility to work from anywhere, at any time. This allows college students to balance their coursework with their job without having to commute. Saving time and money: Remote work eliminates the need to commute, saving time and money. This is particularly beneficial for college students who often have limited finances and time. A comfortable work environment: Working from home allows developers to create a work environmen...

As a college student who has been observing the tech industry for quite some time to choose the correct coding language as my primary language, I have witnessed the evolution of coding languages. Over the years, various programming languages have come and gone, but there are a few that have stood the test of time and have become the most used and popular ones. In this article, I will be discussing the latest and most used coding languages that every developer should know.                                    Java: Java has been around for over two decades now and is still one of the most popular and widely used programming languages in the world. It is an object-oriented language that is used to develop a wide range of applications, including desktop, mobile, web, and enterprise applications. Java is known for its security, robustness, and scalability, which is why it is used by companies like Amazo...

  Hi, I'm Prateek kumar Singh . In this blog I am going to share my internship experience which I got at # Oasis InfoByte . This was my first ever internship at # oasis infobyte and I'm happy that I got this opportunity . This was about Web Development and designing and in this internship we have to perform four tasks. These tasks are very beginner friendly but after completing this task you feel satisfaction that you can create website like we use to see everyday on our computer. But the thing that pains me is that despite such a wonderful opportunity to gain more and more skills and grow my self . I couldn't perform no more than 1 task that was portfolio website.  I couldn't avoid the problems that came this month.  This internship was about 1 month long. They gave us 4 tasks to complete within 1 month i.e., 1 task for a week. Generally everyone acquire skill and thought that he/she will get job but before applying job it is very important to know how to use that ski...

  Convolutional Neural Networks What are convolutional neural networks? A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning algorithm which can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image and be able to differentiate one from the other. How do convolutional neural networks work? A CNN takes in a large amount of image data and utilizes multi channeled images. Due to digital color images having red-blue green (RGB) encoding, a convolutional network ingests such images as three separate strata of color stacked one on top of the other. The depth layers in the three layers of colors(RGB) interpreted by CNNs are referred to as channels. Layers in CNN • Convolutional Layer: It is the main building block of CNN. It contains all the filters, parameters of which are to be learned throughout the training. • Pooling Layer: It is used to reduce the number of parameters to learn and the computation performed in th...

 Convolution Neural Network: In  deep learning , a  convolutional neural network  ( CNN , or  ConvNet ) is a class of  artificial neural network , most commonly applied to analyze visual imagery. [1]  They are also known as  shift invariant  or  space invariant artificial neural networks  ( SIANN ), based on the shared-weight architecture of the convolution kernels or filters that slide along input features and provide translation  equivariant  responses known as feature maps.  Counter-intuitively, most convolutional neural networks are only  equivariant , as opposed to  invariant , to translation. This is how a general Convolution neural network works internally . But we in this project will create more complex neural network using more number of nodes , which will result in greater no. of filter checks which  will increase the accuracy rate

  The objective of the proposed system is to classify the brain tumor images using :-   1.         convolutional neural network (CNN) 2.         Support vector machine (SVM) 3.         Deep learning models 4.    Image Clustering  5.    Using transfer learning     Images from the dataset are downsized to reduce computation and some salt noise is added to make model robust and the dataset increases.  The classification process undergoes following steps     1- Input   2-    Data Pre-processing   a. Importing libraries – importing libraries b. Data augmentation – modified version of image c. Import the augmented data d. Convert the images to grayscale e. Removal of noise using dilations and erosions and smoothening of images f. Grab the largest contour. g. Find the extreme points of the contoured image h...