| Day1 ( Machine Learning vs Deep Learning ) |
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| Day2 ( Types of Neural Network ) |
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| Day3 ( What is Perceptron & Perceptron vs Neuron ) |
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| Day4 ( Perceptron Trick ) |
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| Day5 ( Perceptron Loss Function & It's Limitation ) |
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| Day6 ( Multilayer Perceptrons ) |
Explanation Link |
| Day7 ( Forward Propagation ) |
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| Day8 ( Customer Churn #Prediction Using ANN ) |
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| Day9 ( Handwritten-Digit-Classification using ANN ) |
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| Day10 ( Admission Prediction using ANN ) |
Explanation Link |
| Day11 ( Loss Function ) |
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| Day12 ( BackPropagation ) |
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| Day13 ( Multilayer Perceptron Memoization ) |
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| Day14 ( Gradient Descent & Vectorization ) |
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| Day15 ( Vanishing & Exploding Gradient Problem ) |
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| Day16 ( Early Stopping in Neural Network ) |
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| Day17 ( Data Scaling in Neural Network ) |
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| Day18 ( Dropout Layer 🧬 ) |
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| Day19 ( Regularization ) |
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| Day20 ( Activation Function ) |
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| Day21 ( Weight Initialization ) |
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| Day23 ( Optimizer 🎚️ ) |
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| Day24 ( Exponentially Weighted Moving Average EWMA ) |
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| Day25 ( Stochastic Gradient Descent (SGD) with Momentum ) |
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| Day26 ( Nesterov Accelerated Gradient (NAG) Optimizer ) |
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| Day27 ( AdaGrad Optimization ) |
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| Day28 ( RMSProp Optimizer & Adam Optimizer ) |
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| Day29 ( Keras Tuner & Hyperparameter Tuning ) |
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| Day30 ( Convolutional Neural Network ) |
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| Day31 ( Convolution Operation ) |
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| Day32 ( Padding & Strides ) |
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| Day33 ( Pooling Layer ) |
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| Day34 ( CNN Architecture & CNN vs ANN ) |
Explanation Link |
| Day35 ( Backpropagation in CNN ) |
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| Day36 ( Data Augmentation ) |
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| Day37 ( Pre-trained Model in CNN ) |
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| Day38 ( Visualize Filters and Feature Maps in CNN ) |
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| Day39 ( Transfer Learning ) |
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| Day 40 ( Keras Functional Model ) |
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| Day 41 ( Recurrent Neural Network (RNN) ) |
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| Day 42 ( RNN Architecture & Forward Propagation ) |
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| Day 43 ( Backpropagation in RNN & Problems with RNN ) |
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| Day 44 ( Understanding LSTM Networks ) |
Explanation Link |
| Day 45 ( Gated Recurrent Unit (GRU) ) |
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