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100 Days of Deep Learning in Hindi (CampusX)

By CampusX

Course audio
Course audio: Hindi
Level
Intermediate
Length
84 lessons · 52.2 hours

What you'll be able to do

  • Explain perceptrons, loss functions and backpropagation
  • Build classification and regression networks in Keras
  • Use CNNs, RNNs and LSTMs for images and sequences
  • Understand attention and the transformer architecture

About this course

CampusX's deep learning series in Hindi: 84 lessons and about 52 hours. It builds neural networks from the perceptron upward, with the perceptron trick, loss functions, multi-layer perceptrons and forward propagation, then three careful lessons on backpropagation and the vanishing gradient problem. Along the way it builds Keras projects such as customer churn, MNIST digits and graduate admission prediction, and later covers optimisers, CNNs, RNNs and LSTMs, before finishing with attention, transformer encoder and decoder architecture and inference. Ideal for students who finished a machine learning course and want deep learning explained step by step.

Before you start: Machine learning basics and Python

Syllabus

84 lessons · 52:12:19 in total

  1. 1100 Days of Deep Learning | Course Announcement18:32
  2. 2What is Deep Learning? Deep Learning Vs Machine Learning | Complete Deep Learning Course1:06:58
  3. 3Types of Neural Networks | History of Deep Learning | Applications of Deep Learning33:16
  4. 4What is a Perceptron? Perceptron Vs Neuron | Perceptron Geometric Intuition38:34
  5. 5Perceptron Trick | How to train a Perceptron | Perceptron Part 2 | Deep Learning Full Course51:45
  6. 6Perceptron Loss Function | Hinge Loss | Binary Cross Entropy | Sigmoid Function59:13
  7. 7Problem with Perceptron7:39
  8. 8MLP Notation13:24
  9. 9Multi Layer Perceptron | MLP Intuition37:46
  10. 10Forward Propagation | How a neural network predicts output?15:31
  11. 11Customer Churn Prediction using ANN | Keras and Tensorflow | Deep Learning Classification35:23
  12. 12Handwritten Digit Classification using ANN | MNIST Dataset28:40
  13. 13Graduate Admission Prediction using ANN17:43
  14. 14Loss Functions in Deep Learning | Deep Learning | CampusX59:56
  15. 15Backpropagation in Deep Learning | Part 1 | The What?54:19
  16. 16Backpropagation Part 2 | The How | Complete Deep Learning Playlist59:56
  17. 17Backpropagation Part 3 | The Why | Complete Deep Learning Playlist40:21
  18. 18Vanishing Gradient Problem in ANN | Exploding Gradient Problem | Code Example32:16
  19. 19MLP Memoization | Complete Deep Learning Playlist25:24
  20. 20Gradient Descent in Neural Networks | Batch vs Stochastics vs Mini Batch Gradient Descent37:53
  21. 21How to Improve the Performance of a Neural Network30:24
  22. 22Early Stopping In Neural Networks | End to End Deep Learning Course12:00
  23. 23Data Scaling in Neural Network | Feature Scaling in ANN | End to End Deep Learning Course16:55
  24. 24Dropout Layer in Deep Learning | Dropouts in ANN | End to End Deep Learning27:51
  25. 25Dropout Layers in ANN | Code Example | Regression | Classification19:17
  26. 26Regularization in Deep Learning | L2 Regularization in ANN | L1 Regularization | Weight Decay in ANN35:57
  27. 27Activation Functions in Deep Learning | Sigmoid, Tanh and Relu Activation Function44:52
  28. 28Relu Variants Explained | Leaky Relu | Parametric Relu | Elu | Selu | Activation Functions Part 233:25
  29. 29Weight Initialization Techniques | What not to do? | Deep Learning49:24
  30. 30Xavier/Glorat And He Weight Initialization in Deep Learning21:07
Show all 84 lessons
  1. 31Batch Normalization in Deep Learning | Batch Learning in Keras43:39
  2. 32Optimizers in Deep Learning | Part 1 | Complete Deep Learning Course22:34
  3. 33Exponentially Weighted Moving Average or Exponential Weighted Average | Deep Learning18:51
  4. 34SGD with Momentum Explained in Detail with Animations | Optimizers in Deep Learning Part 238:25
  5. 35Nesterov Accelerated Gradient (NAG) Explained in Detail | Animations | Optimizers in Deep Learning27:50
  6. 36AdaGrad Explained in Detail with Animations | Optimizers in Deep Learning Part 426:29
  7. 37RMSProp Explained in Detail with Animations | Optimizers in Deep Learning Part 512:38
  8. 38Adam Optimizer Explained in Detail with Animations | Optimizers in Deep Learning Part 512:39
  9. 39Keras Tuner | Hyperparameter Tuning a Neural Network1:05:34
  10. 40What is Convolutional Neural Network (CNN) | CNN Intution27:10
  11. 41CNN Vs Visual Cortex | The Famous Cat Experiment | History of CNN15:02
  12. 42CNN Part 3 | Convolution Operation29:14
  13. 43Padding & Strides in CNN | CNN Lecture 4 | Deep Learning24:26
  14. 44Pooling Layer in CNN | MaxPooling in Convolutional Neural Network27:54
  15. 45CNN Architecture | LeNet -5 Architecture20:00
  16. 46Comparing CNN Vs ANN | CampusX17:42
  17. 47Backpropagation in CNN | Part 1 | Deep Learning36:21
  18. 48CNN Backpropagation Part 2 | How Backpropagation works on Convolution, Maxpooling and Flatten Layers43:27
  19. 49Cat Vs Dog Image Classification Project | Deep Learning Project | CNN Project27:29
  20. 50Data Augmentation in Deep Learning | CNN26:49
  21. 51Pretrained models in CNN | ImageNET Dataset | ILSVRC | Keras Code24:28
  22. 52What does a CNN see? | Visualizing CNN Filters and Feature Maps | CampusX13:03
  23. 53What is Transfer Learning? Transfer Learning in Keras | Fine Tuning Vs Feature Extraction33:53
  24. 54Keras Functional Model | How to build non-linear Neural Networks?25:38
  25. 55Why RNNs are needed | RNNs Vs ANNs | RNN Part 130:19
  26. 56Recurrent Neural Network | Forward Propagation | Architecture41:44
  27. 57RNN Sentiment Analysis | RNN Code Example in Keras | CampusX36:57
  28. 58Types of RNN | Many to Many | One to Many | Many to One RNNs22:20
  29. 59How Backpropagation works in RNN | Backpropagation Through Time33:58
  30. 60Problems with RNN | 100 Days of Deep Learning32:18
  31. 61LSTM | Long Short Term Memory | Part 1 | The What? | CampusX42:18
  32. 62LSTM Architecture | Part 2 | The How? | CampusX1:10:13
  33. 63LSTM | Part 3 | Next Word Predictor Using | CampusX1:00:05
  34. 64Gated Recurrent Unit | Deep Learning | GRU | CampusX1:26:22
  35. 65Deep RNNs | Stacked RNNs | Stacked LSTMs | Stacked GRUs | CampusX45:08
  36. 66Bidirectional RNN | BiLSTM | Bidirectional LSTM | Bidirectional GRU25:41
  37. 67The Epic History of Large Language Models (LLMs) | From LSTMs to ChatGPT | CampusX1:27:06
  38. 68Encoder Decoder | Sequence-to-Sequence Architecture | Deep Learning | CampusX1:13:42
  39. 69Attention Mechanism in 1 video | Seq2Seq Networks | Encoder Decoder Architecture41:24
  40. 70Bahdanau Attention Vs Luong Attention52:33
  41. 71Introduction to Transformers | Transformers Part 11:00:05
  42. 72What is Self Attention | Transformers Part 2 | CampusX23:21
  43. 73Self Attention in Transformers | Deep Learning | Simple Explanation with Code!1:23:24
  44. 74Scaled Dot Product Attention | Why do we scale Self Attention?50:42
  45. 75Self Attention Geometric Intuition | How to Visualize Self Attention | CampusX20:52
  46. 76Why is Self Attention called "Self"? | Self Attention Vs Luong Attention in Depth Lecture | CampusX22:35
  47. 77What is Multi-head Attention in Transformers | Multi-head Attention v Self Attention | Deep Learning38:27
  48. 78Positional Encoding in Transformers | Deep Learning | CampusX1:13:15
  49. 79Layer Normalization in Transformers | Layer Norm Vs Batch Norm46:57
  50. 80Transformer Architecture | Part 1 Encoder Architecture | CampusX54:58
  51. 81Masked Self Attention | Masked Multi-head Attention in Transformer | Transformer Decoder1:00:54
  52. 82Cross Attention in Transformers | 100 Days Of Deep Learning | CampusX34:07
  53. 83Transformer Decoder Architecture | Deep Learning | CampusX48:26
  54. 84Transformer Inference | How Inference is done in Transformer? | Deep Learning | CampusX45:12

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Lessons last checked Oct 2026. Course summary and lesson structure by Sabaq.

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