Day 128 Progress made

README.md

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 ![Image](https://github.com/ThinamXx/300Days__MachineLearningDeepLearning/blob/main/Images/Day%20127a.PNG)
 ![Image](https://github.com/ThinamXx/300Days__MachineLearningDeepLearning/blob/main/Images/Day%20127b.PNG)
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+**Day128 of 300DaysOfData!**
+- **Stochastic Gradient Descent**: Stochastic Gradient Descent is an iterative method for optimizing an objective function with suitable differentiable properties. It is a variation of the gradient descent algorithm that calculates the error and updates the model. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about The Momentum Method, Stochastic Gradient Descent, Leaky Averages, Variance, Accelerated Gradient, An Ill Conditioned Problem and Convergence, Effective Sample Weight, Practical Experiments, Implementation of Momentum with SGD, Theoretical Analysis, Quadratic Convex Functions, Scalar Functions and few more topics related to the same from here. I have presented the implementation of Momentum Method, Effective Sample Weight and Scalar Functions using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead!!
+- Book:
+  - [**Dive into Deep Learning**](https://d2l.ai/index.html)
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+![Image](https://github.com/ThinamXx/300Days__MachineLearningDeepLearning/blob/main/Images/Day%20128a.PNG)
+![Image](https://github.com/ThinamXx/300Days__MachineLearningDeepLearning/blob/main/Images/Day%20128b.PNG)