Write mate introduction blog added to _posts

_posts/2023-09-03-writemateblog.md

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+layout : post  
+title : Write-mate 
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+tags : Python ,ML ,RNN, CNN
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+Write-mate 
+--
+Write-mate is a ML model which converts given printed text into handwritten text using deep learning, neural networks like RNN, CNN. We will be working on the project from ground up without using technologies like PyTorch and tensor flow. WriteMate is an innovative machine learning-based project aimed at bridging the gap between digital and analog communication styles. The project focuses on developing a system that can convert printed letters into handwritten letters using advanced machine learning techniques. This technology finds applications in personalization, artistic expression, and enhancing the warmth of digital communication.
+
+---
+
+I am Sanika Kumbhare from EXTC working together with Mohammed Bhadsorawala from CSE -VJTI
+
+---
+Week 1
+--
+We started off with getting familiar with markdown, learning concepts like Linear algebra.We got familiar with the workings of git and GitHub. 
+
+Week 2
+--
+We are currently learning about the neural network and deep learning from Deeplearning.AI and python
+
+- We completed our first course of neural networks and deep learning.
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+It has helped us to know about
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+- Analyse the major trends driving the rise of deep learning, and give examples of where and how it is applied today.
+- Set up a machine learning problem with a neural network mindset and use vectorization to speed up your models
+- Build a neural network with one hidden layer, using forward propagation and backpropagation
+- Analyse the key computations underlying deep learning, then use them to build and train deep neural networks for computer vision tasks.
+- To get practical knowledge of the course, we started implementing a neural network model of number recognition from scratch, without using technologies like tensor flow. It consists of an input layer, a hidden layer and an output layer. 
+- We also started our course 2, Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization. 
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+So far we have learnt to discover and experiment with a variety of different initialization methods, apply L2 regularisation and dropout to avoid model overfitting, then apply gradient checking to identify errors in a fraud detection model.
+
+Week  3
+--
+
+- We learnt more about Jax and its implementation and advantages
+- We successfully completed building the MNIST number detection model using Jax. It is now inputting the MNIST dataset of hand-written numbers and detecting the numbers with an accuracy of 99.6%. The model runs within 3 minutes.
+- Now, we are learning about Convolutional Neural Networks .
+- We are learning about the ways to implement the foundational layers of CNNs (pooling, convolutions) and stack them properly in a deep network to solve multi-class image classification problems.
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+
+---
+Challenges faced
+--
+- The first challenge we experienced was the uploading of git file of markdown notes on GitHub using terminal. We solved it by making an ssh key and saving the key on my laptop. We are thankful to the mentors for correctly guiding us and always readily solving our doubts.
+- The main challenge during this project what we feel we will face is managing travelling, academics and learning this simultaneously. But we am optimistic that we would learn to balance it out and complete out project successfully.
+- We faced some errors while solving the assignments, and also during the implementation of the Mnist model of number recognition.