100 Days of Deep Learning (Hindi) (Part 1)

   Friday, June 26, 2026   

Starting a new Lecture Notes Series on 100 Days of Deep Learning

Youtube Lecture Playlist Credits 

Channel Name: CampusX

So Let Us Start to This Journey of Learning 

100 Days of Deep Learning By Lecture Notes together!

Lecture 1: 100 Days of Deep Learning | Course Announcement

Lecture 2: What is Deep Learning? Deep Learning Vs Machine Learning | Complete Deep Learning Course

Lecture 3: Types of Neural Networks | History of Deep Learning | Applications of Deep Learning

Lecture 4: What is a Perceptron? Perceptron Vs Neuron | Perceptron Geometric Intuition

Lecture 5: Perceptron Trick | How to train a Perceptron | Perceptron Part 2 | Deep Learning Full Course

Lecture 6: Perceptron Loss Function | Hinge Loss | Binary Cross Entropy | Sigmoid Function

Lecture 7: Problem with Perceptron

Lecture 8: MLP Notation

Lecture 9: Multi Layer Perceptron | MLP Intuition

Lecture 10: Forward Propagation | How a neural network predicts output?

Lecture 11: Customer Churn Prediction using ANN | Keras and Tensorflow | Deep Learning Classification

Lecture 12: Handwritten Digit Classification using ANN | MNIST Dataset

Lecture 13: Graduate Admission Prediction using ANN

Lecture 14: Loss Functions in Deep Learning | Deep Learning | CampusX

Lecture 15: Loss Functions in Deep Learning | Deep Learning | CampusX

Lecture 16: Backpropagation Part 2 | The How | Complete Deep Learning Playlist

Lecture 17: Backpropagation Part 3 | The Why | Complete Deep Learning Playlist

Lecture 18: Vanishing Gradient Problem in ANN | Exploding Gradient Problem | Code Example

Lecture 19: MLP Memoization | Complete Deep Learning Playlist

Lecture 20: Gradient Descent in Neural Networks | Batch vs Stochastics vs Mini Batch Gradient Descent

Lecture 21: How to Improve the Performance of a Neural Network

Lecture 22: Early Stopping In Neural Networks | End to End Deep Learning Course

Lecture 23: Data Scaling in Neural Network | Feature Scaling in ANN | End to End Deep Learning Course

Lecture 24: Dropout Layer in Deep Learning | Dropouts in ANN | End to End Deep Learning

Lecture 25: Dropout Layers in ANN | Code Example | Regression | Classification

Lecture 26: Regularization in Deep Learning | L2 Regularization in ANN | L1 Regularization | Weight Decay in ANN

Lecture 27: Activation Functions in Deep Learning | Sigmoid, Tanh and Relu Activation Function

Lecture 28: Relu Variants Explained | Leaky Relu | Parametric Relu | Elu | Selu | Activation Functions Part 2

Lecture 29: Weight Initialization Techniques | What not to do? | Deep Learning

Lecture 30: Xavier/Glorat And He Weight Initialization in Deep Learning

Lecture 31: Batch Normalization in Deep Learning | Batch Learning in Keras

Lecture 32: Optimizers in Deep Learning | Part 1 | Complete Deep Learning Course

Lecture 33: Exponentially Weighted Moving Average or Exponential Weighted Average | Deep Learning

Lecture 34: SGD with Momentum Explained in Detail with Animations | Optimizers in Deep Learning Part 2

Lecture 35: Nesterov Accelerated Gradient (NAG) Explained in Detail | Animations | Optimizers in Deep Learning

Lecture 36: AdaGrad Explained in Detail with Animations | Optimizers in Deep Learning Part 4

Lecture 37: RMSProp Explained in Detail with Animations | Optimizers in Deep Learning Part 5

Lecture 38: Adam Optimizer Explained in Detail with Animations | Optimizers in Deep Learning Part 5

Lecture 39: Keras Tuner | Hyperparameter Tuning a Neural Network

Lecture 40: What is Convolutional Neural Network (CNN) | CNN Intution

Lecture 41: CNN Vs Visual Cortex | The Famous Cat Experiment | History of CNN

Lecture 42: CNN Part 3 | Convolution Operation