Day 9: Linear Regression from scratch using NumPy.
Linear regression implementation from scratch using NumPy and Scikit Learn both.
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Day 8: What is Linear Regression with Derivation.
Introduction to Linear Regression. Derivation of Ordinary Least Square method.
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Day 7: Perceptrons from scratch using NumPy.
Perceptron implementation from scratch using NumPy and Matplotlib. Dataset via Scikit Learn.
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Day 6: Matrix Maths and PyTorch Tensor.
Review the Matrix maths needed with PyTorch Tensor to understand Neural Networks.
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Day 5: Perceptrons in Python from Scratch. (Code)
Perceptron implementation in Python from scratch using PyTorch and SkLearn.
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Day 4: What are Perceptrons?
Perceptrons are the building blocks of Neural Networks. How do Perceptrons work?
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Day 3: McCulloch-Pitts Neurons. World's First Neural Networks.
I uncover the maths behind McCulloch-Pitts Neuron and how it works with Logical functions.
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Day 2: What are Neural Networks?
What are the main components of a Neural Network? How does a neural network work?
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Day 1: What is Deep Learning?
What are the types and use cases of deep learning? What are basic components of a neural network?
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Day 0: Announcement and Plan
This is the very first post of 100 Days Of Deep Learning. It contains how I am taking up this challenge.
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