Update README.md

README.md

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 | 37. [**OpenCV Project I**](https://github.com/ThinamXx/ComputerVision/blob/main/01.%20OpenCV/OCV%20Project%20I.ipynb) | 
 | 38. [**OpenCV Project II**](https://github.com/ThinamXx/ComputerVision/blob/main/01.%20OpenCV/OCV%20Project%20II.ipynb) |
 | 39. [**Convolution**](https://github.com/ThinamXx/ComputerVision/blob/main/02.%20ConvolutionalNeuralNetwork/Convolutions.ipynb) |
+| 40. [**Convolutional Layers**](https://github.com/ThinamXx/ComputerVision/blob/main/02.%20ConvolutionalNeuralNetworks/Convolutional%20Layers.ipynb) |
 
 **Day1 of 300DaysOfData!**
 - **Gradient Descent and Cross Validation**: Gradient Descent is an iterative approach to approximating the Parameters that minimize a Differentiable Loss Function. Cross Validation is a resampling procedure used to evaluate Machine Learning Models on a limited Data sample which has a parameter that splits the data into number of groups. On my Journey of Machine Learning and Deep Learning, Today I have read in brief about the fundamental Topics such as Calculus, Matrices, Matrix Calculus, Random Variables, Density Functions, Distributions, Independence, Maximum Likelihood Estimation and Conditional Probability. I have also read and Implemented about Gradient Descent and Cross Validation. I am starting this Journey from Scratch and I am following the Book:**Machine Learning From Scratch**. I have presented the Implementation of Gradient Descent and Cross Validation here in the Snapshots. I hope you will also spend some time reading the Topics from the Book mentioned above. I am excited about the days to come!!