6. Capstone Project: Build a Data science project from scratch: After completing all the above courses take Harvard’s data science capstone project. Assess your skills in data visualization, statistics, data wrangling and machine learning.
@sumanth_077
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Machine Learning Fundamentals: Overfitting, Modeling, and Recommendations
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5. Machine Learning This course will teach you the basics of machine learning, techniques to mitigate overfitting, supervised and unsupervised modelling approaches, and recommendation systems.
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Data Pre-Processing: Preparing Data for Machine Learning Models
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3. Data Pre-Processing: This will teach you to prepare data and convert it into a format that is easily digestible by machine learning models.
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Neural Network Training Method: Forward Pass, Backward Pass, Weight Updates
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Now a method to train the neural network using both the forward and backward passes. The function will run for specified no of epochs, calculating: 1. The Forward Pass
2. Backward Pass
3. Updating the Weights -
Update Weights Learning Rate Hyperparameter Optimization Neural Networks
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Now Update the Weights: Here learning rate is the hyper parameter! A low learning rate can cause the model getting caught in local optima, while the high learning rate can cause the model to overshoot the general solution W1 += learning_rate * d_W1
b1 += learning_rate * d_b1 -
Calculate Gradients dW1, db2, db1 for Neural Network Backpropagation
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Similarly calculate d_W1, d_b2 & d_b1 dW1: Gradient of the loss function wrt W1 d_b2: Gradient of the loss function wrt b2(bias of neuron in output layer) d_b1: Gradient of the loss function wrt b1(bias of neuron in hidden layer)
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Backward Pass: Computing Output Layer Gradients and Weight Updates
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Backward Pass: First compute the gradients of the output layer. Loss = (y – output) Gradient of Loss = (y – output) * sigmoid_derivative(output) Now calculate d_W2 which is gradient of the loss function with respect to W2. d_W2 = hidden_output.T • Gradient of Loss
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Forward Pass in Neural Networks: Hidden Layer Calculation and Activation
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Forward Pass: Here the input data is passed through the neural network to obtain the predicted output. In forward pass, First calculate the output of the hidden layer. hidden_output = X•W1 + b1 Then apply the sigmoid activation to the output. output = sigmoid( (X•W1) + b1)
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Building a Simple 2-Layer Neural Network with Sigmoid Activation
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Below is the simple Neural Network consists of 2 layers: – Hidden Layer
– Output Layer First Initialize the size of layers along with the weights & biases. And also define the sigmoid activation function & it's derivative which is really key to introduce non-linearity. -
Neural Network Implementation from Scratch in Python
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Neural Network implemented from scratch in Python (step-by-step explanation with code):