Playlist Duration

Playlist · CampusX

100 Days of Deep Learning

by CampusX · 84 videos

Total watch time

2d 4h 12m

at 1× speed · exactly 2 days, 4 hours, 12 minutes, 19 seconds at 1×

At an hour a day you'd finish in 53 days

The shape of the course

84 videos in blocks of 10 · each column is a block's total, to scale

5h 43m
6h 32m
4h 51m
4h 56m
4h 28m
4h 55m
9h 45m
7h 55m
3h 9m

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Videos (84)

2d 4h 12m in total · tick what you've watched

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Videos to
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1 100 Days of Deep Learning | Course Announcement 18:32 2022-02-15
2 What is Deep Learning? Deep Learning Vs Machine Learning | Complete Deep Learning Course 1:06:58 2022-02-17
3 Types of Neural Networks | History of Deep Learning | Applications of Deep Learning 33:16 2022-02-19
4 What is a Perceptron? Perceptron Vs Neuron | Perceptron Geometric Intuition 38:34 2022-02-21
5 Perceptron Trick | How to train a Perceptron | Perceptron Part 2 | Deep Learning Full Course 51:45 2022-02-23
6 Perceptron Loss Function | Hinge Loss | Binary Cross Entropy | Sigmoid Function 59:13 2022-02-25
7 Problem with Perceptron 7:39 2022-02-27
8 MLP Notation 13:24 2022-03-03
9 Multi Layer Perceptron | MLP Intuition 37:46 2022-03-01
10 Forward Propagation | How a neural network predicts output? 15:31 2022-03-05
11 Customer Churn Prediction using ANN | Keras and Tensorflow | Deep Learning Classification 35:23 2022-03-07
12 Handwritten Digit Classification using ANN | MNIST Dataset 28:40 2022-03-09
13 Graduate Admission Prediction using ANN 17:43 2022-03-11
14 Loss Functions in Deep Learning | Deep Learning | CampusX 59:56 2022-03-23
15 Backpropagation in Deep Learning | Part 1 | The What? 54:19 2022-03-30
16 Backpropagation Part 2 | The How | Complete Deep Learning Playlist 59:56 2022-04-03
17 Backpropagation Part 3 | The Why | Complete Deep Learning Playlist 40:21 2022-04-05
18 Vanishing Gradient Problem in ANN | Exploding Gradient Problem | Code Example 32:16 2022-04-17
19 MLP Memoization | Complete Deep Learning Playlist 25:24 2022-04-09
20 Gradient Descent in Neural Networks | Batch vs Stochastics vs Mini Batch Gradient Descent 37:53 2022-04-14
21 How to Improve the Performance of a Neural Network 30:24 2022-04-29
22 Early Stopping In Neural Networks | End to End Deep Learning Course 12:00 2022-05-02
23 Data Scaling in Neural Network | Feature Scaling in ANN | End to End Deep Learning Course 16:55 2022-05-05
24 Dropout Layer in Deep Learning | Dropouts in ANN | End to End Deep Learning 27:51 2022-05-12
25 Dropout Layers in ANN | Code Example | Regression | Classification 19:17 2022-05-15
26 Regularization in Deep Learning | L2 Regularization in ANN | L1 Regularization | Weight Decay in ANN 35:57 2022-05-20
27 Activation Functions in Deep Learning | Sigmoid, Tanh and Relu Activation Function 44:52 2022-06-01
28 Relu Variants Explained | Leaky Relu | Parametric Relu | Elu | Selu | Activation Functions Part 2 33:25 2022-06-09
29 Weight Initialization Techniques | What not to do? | Deep Learning 49:24 2022-06-23
30 Xavier/Glorat And He Weight Initialization in Deep Learning 21:07 2022-06-25
31 Batch Normalization in Deep Learning | Batch Learning in Keras 43:39 2022-07-01
32 Optimizers in Deep Learning | Part 1 | Complete Deep Learning Course 22:34 2022-07-06
33 Exponentially Weighted Moving Average or Exponential Weighted Average | Deep Learning 18:51 2022-07-18
34 SGD with Momentum Explained in Detail with Animations | Optimizers in Deep Learning Part 2 38:25 2022-07-20
35 Nesterov Accelerated Gradient (NAG) Explained in Detail | Animations | Optimizers in Deep Learning 27:50 2022-07-26
36 AdaGrad Explained in Detail with Animations | Optimizers in Deep Learning Part 4 26:29 2022-08-03
37 RMSProp Explained in Detail with Animations | Optimizers in Deep Learning Part 5 12:38 2022-08-05
38 Adam Optimizer Explained in Detail with Animations | Optimizers in Deep Learning Part 5 12:39 2022-08-07
39 Keras Tuner | Hyperparameter Tuning a Neural Network 1:05:34 2022-08-11
40 What is Convolutional Neural Network (CNN) | CNN Intution 27:10 2022-08-17
41 CNN Vs Visual Cortex | The Famous Cat Experiment | History of CNN 15:02 2022-08-19
42 CNN Part 3 | Convolution Operation 29:14 2022-08-23
43 Padding & Strides in CNN | CNN Lecture 4 | Deep Learning 24:26 2022-08-27
44 Pooling Layer in CNN | MaxPooling in Convolutional Neural Network 27:54 2022-09-02
45 CNN Architecture | LeNet -5 Architecture 20:00 2022-09-04
46 Comparing CNN Vs ANN | CampusX 17:42 2022-09-08
47 Backpropagation in CNN | Part 1 | Deep Learning 36:21 2022-09-10
48 CNN Backpropagation Part 2 | How Backpropagation works on Convolution, Maxpooling and Flatten Layers 43:27 2022-09-15
49 Cat Vs Dog Image Classification Project | Deep Learning Project | CNN Project 27:29 2022-09-19
50 Data Augmentation in Deep Learning | CNN 26:49 2022-09-21
51 Pretrained models in CNN | ImageNET Dataset | ILSVRC | Keras Code 24:28 2022-10-03
52 What does a CNN see? | Visualizing CNN Filters and Feature Maps | CampusX 13:03 2022-10-06
53 What is Transfer Learning? Transfer Learning in Keras | Fine Tuning Vs Feature Extraction 33:53 2022-10-10
54 Keras Functional Model | How to build non-linear Neural Networks? 25:38 2022-10-14
55 Why RNNs are needed | RNNs Vs ANNs | RNN Part 1 30:19 2022-10-23
56 Recurrent Neural Network | Forward Propagation | Architecture 41:44 2022-10-29
57 RNN Sentiment Analysis | RNN Code Example in Keras | CampusX 36:57 2022-11-09
58 Types of RNN | Many to Many | One to Many | Many to One RNNs 22:20 2022-11-17
59 How Backpropagation works in RNN | Backpropagation Through Time 33:58 2022-12-01
60 Problems with RNN | 100 Days of Deep Learning 32:18 2022-12-19
61 LSTM | Long Short Term Memory | Part 1 | The What? | CampusX 42:18 2023-08-21
62 LSTM Architecture | Part 2 | The How? | CampusX 1:10:13 2023-08-30
63 LSTM | Part 3 | Next Word Predictor Using | CampusX 1:00:05 2023-09-12
64 Gated Recurrent Unit | Deep Learning | GRU | CampusX 1:26:22 2023-10-05
65 Deep RNNs | Stacked RNNs | Stacked LSTMs | Stacked GRUs | CampusX 45:08 2023-10-18
66 Bidirectional RNN | BiLSTM | Bidirectional LSTM | Bidirectional GRU 25:41 2023-10-27
67 The Epic History of Large Language Models (LLMs) | From LSTMs to ChatGPT | CampusX 1:27:06 2023-11-22
68 Encoder Decoder | Sequence-to-Sequence Architecture | Deep Learning | CampusX 1:13:42 2023-12-10
69 Attention Mechanism in 1 video | Seq2Seq Networks | Encoder Decoder Architecture 41:24 2023-12-21
70 Bahdanau Attention Vs Luong Attention 52:33 2024-01-17
71 Introduction to Transformers | Transformers Part 1 1:00:05 2024-01-28
72 What is Self Attention | Transformers Part 2 | CampusX 23:21 2024-02-05
73 Self Attention in Transformers | Deep Learning | Simple Explanation with Code! 1:23:24 2024-02-09
74 Scaled Dot Product Attention | Why do we scale Self Attention? 50:42 2024-03-01
75 Self Attention Geometric Intuition | How to Visualize Self Attention | CampusX 20:52 2024-03-09
76 Why is Self Attention called "Self"? | Self Attention Vs Luong Attention in Depth Lecture | CampusX 22:35 2024-03-13
77 What is Multi-head Attention in Transformers | Multi-head Attention v Self Attention | Deep Learning 38:27 2024-04-15
78 Positional Encoding in Transformers | Deep Learning | CampusX 1:13:15 2024-05-24
79 Layer Normalization in Transformers | Layer Norm Vs Batch Norm 46:57 2024-06-07
80 Transformer Architecture | Part 1 Encoder Architecture | CampusX 54:58 2024-07-10
81 Masked Self Attention | Masked Multi-head Attention in Transformer | Transformer Decoder 1:00:54 2024-07-26
82 Cross Attention in Transformers | 100 Days Of Deep Learning | CampusX 34:07 2024-08-13
83 Transformer Decoder Architecture | Deep Learning | CampusX 48:26 2024-08-22
84 Transformer Inference | How Inference is done in Transformer? | Deep Learning | CampusX 45:12 2024-09-04
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