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Data Science & Machine Learning

Before starting to learn ML, make sure you have a good hold of Python Programming and Data Handling using Python.

Overview Part 1 Overview Part 2
Random Variables and Probability Distributions Part 1
Expectation, Moments and CLT Part 1
Bayes' Theorem Part 1
Naïve Bayes, Gaussian Naive Bayes, Bayes' Optimal Classifier Part 1
Regression vs Classification Part 1
Bayesian Parameter Estimation Part 1
Maximum Likelihood Estimation Part 1
Maximum A Posteriori Estimation Part 1
Gradient Descent & Regularisation Part 1
Linear Regression: Normal Equation & Regularisation Part 1 Linear Regression: Normal Equation & Regularisation Part 2
Logistic Regression Concepts Part 1
Logistic Regression: Multiclass Classification Part 1
SVM with Linear Kernel Part 1
SVM: Slack Variables and Nonlinear Kernels Part 1
Decision Trees Part 1
Random Forest + Comparison Part 1
Artificial Neural Networks : Introduction Part 1
Artificial Neural Networks : Backpropagation Part 1
ANN: Activation, Loss & Optimization Part 1
Solving ODEs using ANN Part 1
Deep Learning Intro Part 1
Generative Vs. Discriminative Models Part 1
Probabilistic Graphical Models Part 1
Expectation Maximization Part 1
K-Means clustering Part 1
Gaussian Mixture Models Part 1
PCA Part 1
PCA Part 2
Reinforcement Learning Part 1
Big data technologies Part 1
Emerging Issues Part 1

Artificial Neural Networks : Introduction Part 1

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What You Will Learn

  • ANN introduction

Content Covered

Introduction to Artificial Neural Networks.

Resources

  • Universal Approximation Theorem
  • TDSC How many layers
  • TDSC Brain vs ANN 1
  • Medium Brain vs ANN 2
  • TDSC Perceptron Algo
  • GeeksforGeeks Weight Init
  • DataScience Enthusiast Weight Init
  • TDSC Reg for ANN
  • TDSC Dropout 1
  • Youtube Dropout 2
  • Medium Embedding vs Dense
  • PyTorch
  • House Price Prediction using ANN
  • Image recognition using ANN (nextjournal)
  • Multi-Class classification (keras)
  • Dropout (ML Mastery)
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