A-First-Course-on-Deep-Learning-Notes

This is detailed week wise notes for PadhAI onefourthlabs course “A First Course on Deep Learning”.

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Name		Name	Last commit message	Last commit date
Latest commit History 7 Commits
README.md		README.md
Week 1 - Expert Systems and MachineLearning.pdf		Week 1 - Expert Systems and MachineLearning.pdf
Week 10 - Information Theory and Gradient Descent of SigmoidNeuron.pdf		Week 10 - Information Theory and Gradient Descent of SigmoidNeuron.pdf
Week 11 - Power of functions.pdf		Week 11 - Power of functions.pdf
Week 12 - Feed-forward NeuralNetwork.pdf		Week 12 - Feed-forward NeuralNetwork.pdf
Week 13 - Back Propagation.pdf		Week 13 - Back Propagation.pdf
Week 2 - MachineLearningA birds eyeview.pdf		Week 2 - MachineLearningA birds eyeview.pdf
Week 3 - Vectors andMatrices.pdf		Week 3 - Vectors andMatrices.pdf
Week 4 -McCulloch-Pitts Neuron.pdf		Week 4 -McCulloch-Pitts Neuron.pdf
Week 5 - Perceptron.pdf		Week 5 - Perceptron.pdf
Week 6 - Toy Sample Dataset Sample Using Perceptron Model.pdf		Week 6 - Toy Sample Dataset Sample Using Perceptron Model.pdf
Week 7 - Sigmoid Neuron.pdf		Week 7 - Sigmoid Neuron.pdf
Week 8 - Math Part of SigmoidNeuron.pdf		Week 8 - Math Part of SigmoidNeuron.pdf
Week 9 - Probability Part of SigmoidNeuron.pdf		Week 9 - Probability Part of SigmoidNeuron.pdf
Week-14-Back Propagation(Vectorised or Heavy-Math part).pdf		Week-14-Back Propagation(Vectorised or Heavy-Math part).pdf