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Deep Learning Notes (v 1.0)
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Day 1
100 Days of Deep Learning | Course Announcement
01_Course_Announcementocr
Day 2
What is Deep Learning? Deep Learning Vs Machine Learning
02_What is Deep Learning_Deep Learning Vs Machine Learningocr
Day 3
Types of Neural Networks | History of Deep Learning | Applications of Deep Learning
03_Types of Neural Networks History of Deep Learningocr
Day 4
What is a Perceptron? Perceptron Vs Neuron | Perceptron Geometric Intuition
04_What_is_perceptron Perceptron vs Neuron Perceptron Geometric Intuitionocr
Day 5
Perceptron Trick | How to train a Perceptron | Perceptron Part 2 | Deep Learning Full Course
05_Perceptron Trick How to train a perceptron Part 2ocr
Day 6
Perceptron Loss Function | Hinge Loss | Binary Cross Entropy | Sigmoid Function
06_Perceptron Loss Function Hinge Loss Binary Cross Entropy Sigmoid Functionocr
Day 7
Problem with Perceptron
07_Problem with perceptronocr
Day 8
MLP Notation
08_MLP Notationocr
Day 9
Multi Layer Perceptron | MLP Intuition
09_Multi Layer Perceptron MLP Intuitionocr
Day 10
Forward Propagation | How a neural network predicts output?
10_Forward Propagation How a neural network predicts outputocr
Day 11
Customer Churn Prediction using ANN | Keras and Tensorflow | Deep Learning Classification
11_Customer Churn Prediction using ANN Keras and Tensorflow Deep Learning Classificationocr
Day 12
Handwritten Digit Classification using ANN | MNIST Dataset
12_Handwritten Digit Classification using ANN MNIST Datasetocr
Day 13
Graduate Admission Prediction using ANN
13_Graduate Admission Prediction using ANNocr
Day 14
Loss Functions in Deep Learning | Deep Learning | CampusX
14_Loss Functions in Deep Learning Deep Learning CampusXocr
Day 15
Backpropagation in Deep Learning | Part 1 | The What?
15_Backpropagation in Deep Learning Part 1 The Whatocr
Day 16
Backpropagation Part 2 | The How | Complete Deep Learning Playlist
16_Backpropagation Part 2 The How Complete Deep Learning Playlistocr
Day 17
Backpropagation Part 3 | The Why | Complete Deep Learning Playlist
17_Backpropagation Part 3 The Why Complete Deep Learning Playlistocr
Day 18
MLP Memoization | Complete Deep Learning Playlist
18_MLP Memoization Complete Deep Learning Playlistocr
Day 19
Gradient Descent in Neural Networks | Batch vs Stochastics vs Mini Batch Gradient Descent
19_Gradient_Descent_in_Neural_Network Batch_vs_Stochastics_vs_Mini_Batchocr
Day 20
Vanishing Gradient Problem in ANN | Exploding Gradient Problem | Code Example
20_Vanishing Gradient Problem in ANN Exploding Gradient Problem Code Exampleocr
Day 21
How to Improve the Performance of a Neural Network
21_How to Improve the Performance of a Neural Networkocr
Day 22
Early Stopping In Neural Networks | End to End Deep Learning Course
22_Early Stopping In Neural Networks End to End Deep Learning Courseocr
Day 23
Data Scaling in Neural Network | Feature Scaling in ANN | End to End Deep Learning Course
23_Data Scaling in Neural Network Feature Scaling in ANN End to End Deep Learning Courseocr
Day 24
Dropout Layer in Deep Learning | Dropouts in ANN | End to End Deep Learning
24_Dropout Layer in Deep Learning Dropouts in ANN End to End Deep Learningocr
Day 25
Dropout Layers in ANN | Code Example | Regression | Classification
25_Dropout Layers in ANN Code Example Regression Classificationocr
Day 26
Regularization in Deep Learning | L2 Regularization in ANN | L1 Regularization | Weight Decay in ANN
26_Regularization in Deep Learning L2 Regularization in ANN L1 Regularization Weight Decay in ANNocr
Day 27
Activation Functions in Deep Learning | Sigmoid, Tanh and Relu Activation Function
27_Activation Functions in Deep Learning Sigmoid, Tanh and Relu Activation Functionocr
Day 28
Relu Variants Explained | Leaky Relu | Parametric Relu | Elu | Selu | Activation Functions Part 2
28_Relu Variants Explained Elu Selu Activation Functions Part 2ocr
Day 29
Weight Initialization Techniques | What not to do? | Deep Learning
29_Weight Initialization Techniques What not to do Deep Learningocr
Day 30
Xavier/Glorat And He Weight Initialization in Deep Learning
30_Xavier Glorat And He Weight Initialization in Deep Learningocr
Day 31
Batch Normalization in Deep Learning | Batch Learning in Keras
31_Batch Normalization in Deep Learning Batch Learning in Kerasocr
Day 32
Optimizers in Deep Learning | Part 1 | Complete Deep Learning Course
32_Optimizers in Deep Learning Part 1 Complete Deep Learning Courseocr
Day 33
Exponentially Weighted Moving Average or Exponential Weighted Average | Deep Learning
33_Exponentially Weighted Moving Average or Exponential Weighted Average Deep Learningocr
Day 34
SGD with Momentum Explained in Detail with Animations | Optimizers in Deep Learning Part 2
34_SGD with Momentum Explained in Detail with Animations Optimizers in Deep Learning Part 2ocr
Day 35
Nesterov Accelerated Gradient (NAG) Explained in Detail | Animations | Optimizers in Deep Learning
35_Nesterov Accelerated Gradient (NAG) Explained in Detail Animations Optimizers in Deep Learningocr
Day 36
AdaGrad Explained in Detail with Animations | Optimizers in Deep Learning Part 4
36_AdaGrad Explained in Detail with Animations Optimizers in Deep Learning Part 4ocr
Day 37
RMSProp Explained in Detail with Animations | Optimizers in Deep Learning Part 5
37_RMSProp Explained in Detail with Animations Optimizers in Deep Learning Part 5ocr
Day 38
Adam Optimizer Explained in Detail with Animations | Optimizers in Deep Learning Part 5
38_Adam Optimizer Explained in Detail with Animations Optimizers in Deep Learning Part 5ocr
Day 39
Keras Tuner | Hyperparameter Tuning a Neural Network
39_Keras Tuner Hyperparameter Tuning a Neural Networkocr
Day 40
What is Convolutional Neural Network (CNN) | CNN Intution
40_What is Convolutional Neural Network (CNN) CNN Intutionocr
Day 41
CNN Vs Visual Cortex | The Famous Cat Experiment | History of CNN
41_CNN_Vs_Visual_Cortex_The_Famous_Cat_Experiment_History_of_CNNocr
Day 42
CNN Part 3 | Convolution Operation
42_CNN_Part_3_Convolution_Operationocr
Day 43
Padding & Strides in CNN | CNN Lecture 4 | Deep Learning
43_Padding & Strides in CNN CNN Lecture 4 Deep Learningocr
Day 44
Pooling Layer in CNN | MaxPooling in Convolutional Neural Network
44_Pooling Layer in CNN MaxPooling in Convolutional Neural Networkocr
Day 45
CNN Architecture | LeNet -5 Architecture
45_CNN Architecture LeNet -5 Architectureocr
Day 46
Comparing CNN Vs ANN | CampusX
46_Comparing CNNVs_ANN_CampusXocr
Day 47
Backpropagation in CNN | Part 1 | Deep Learning
47_Backpropagation in CNN Part 1 Deep Learningocr
Day 48
CNN Backpropagation Part 2 | How Backpropagation works on Convolution, Maxpooling and Flatten Layers
48_CNN Backpropagation Part 2 How Backpropagation works on Convolution, Maxpooling and Flatten Layersocr
Day 49
Cat Vs Dog Image Classification Project | Deep Learning Project | CNN Project
49_Cat Vs Dog Image Classification ProjectDeep Learning Project CNN Projectocr
Day 50
Data Augmentation in Deep Learning | CNN
50_Data Augmentation in Deep Learning CNNocr
Day 51
Pretrained models in CNN | ImageNET Dataset | ILSVRC | Keras Code
51_Pretrained models in CNN ImageNET Dataset ILSVRC Keras Codeocr
Day 52
What does a CNN see? | Visualizing CNN Filters and Feature Maps | CampusX
52_What does a CNN see Visualizing CNN Filters and Feature Maps CampusXocr
Day 53
What is Transfer Learning? Transfer Learning in Keras | Fine Tuning Vs Feature Extraction
53_What is Transfer Learning_Transfer Learning in Keras Fine Tuning_Vs_Feature_Extractionocr
Day 54
Keras Functional Model | How to build non-linear Neural Networks?
54ocr
Day 55
Why RNNs are needed | RNNs Vs ANNs | RNN Part 1
55_Why RNNs are needed RNNs Vs ANNs RNN Part 1ocr
Day 56
Recurrent Neural Network | Forward Propagation | Architecture
56_Recurrent Neural Network Forward Propagation Architectureocr
Day 57
RNN Sentiment Analysis | RNN Code Example in Keras | CampusX
57_RNN Sentiment Analysis RNN Code Example in Keras CampusXocr
Day 58
Types of RNN | Many to Many | One to Many | Many to One RNNs
58_ Types of RNN Many to Many One to Many Many to One RNNsocr
Day 59
How Backpropagation works in RNN | Backpropagation Through Time
59_How Backpropagation works in RNN Backpropagation Through Timeocr
Day 60
Problems with RNN | 100 Days of Deep Learning
60_Problems with RNN 100 Days of Deep Learningocr
Day 61
LSTM | Long Short Term Memory | Part 1 | The What? | CampusX
61_LSTM Long Short Term Memory Part 1 The WhatCampusXocr
Day 62
LSTM Architecture | Part 2 | The How? | CampusX
62_LSTM ArchitecturePart 2 The How_CampusXocr
Day 63
LSTM | Part 3 | Next Word Predictor Using | CampusX
63_LSTM Part 3 Next Word Predictor Using CampusXocr
Day 64
Gated Recurrent Unit | Deep Learning | GRU | CampusX
64_Deep RNNs Stacked RNNs Stacked LSTMs Stacked GRUs CampusXocr
Day 65
Deep RNNs | Stacked RNNs | Stacked LSTMs | Stacked GRUs | CampusX
65_Gated Recurrent Unit Deep Learning GRU CampusXocr
Day 66
Bidirectional RNN | BiLSTM | Bidirectional LSTM | Bidirectional GRU
66_Bidirectional RNN BiLSTM Bidirectional LSTM Bidirectional GRUocr
Day 67
The Epic History of Large Language Models (LLMs) | From LSTMs to ChatGPT | CampusX
67_The Epic History of Large Language Models (LLMs) From LSTMs to ChatGPT_CampusXocr
Day 68
Encoder Decoder | Sequence-to-Sequence Architecture | Deep Learning | CampusX
68_Encoder_Decoder_Sequence_to_Sequence_Architecture_Deep_Learning_CampusXocr
Day 69
Attention Mechanism in 1 video | Seq2Seq Networks | Encoder Decoder Architecture
69_Attention Mechanism in 1 video_Seq2Seq Networks Encoder Decoder Architectureocr
Day 70
Bahdanau Attention Vs Luong Attention
70_Bahdanau Attention Vs Luong Attentionocr
Day 71
Introduction to Transformers | Transformers Part 1
71_Introduction to Transformers Transformers Part 1ocr
Day 72
What is Self Attention | Transformers Part 2 | CampusX
72_What is Self Attention Transformers Part 2 CampusXocr
Day 73
Self Attention in Transformers | Deep Learning | Simple Explanation with Code!
73_Self Attention in Transformers Deep Learning Simple Explanation with Codeocr
Day 74
Scaled Dot Product Attention | Why do we scale Self Attention?
74_Scaled Dot Product Attention Why do we scale Self Attentionocr
Day 75
Self Attention Geometric Intuition | How to Visualize Self Attention | CampusX
75_Self Attention Geometric Intuition How to Visualize Self Attention CampusXocr
Day 76
Why is Self Attention called "Self"? | Self Attention Vs Luong Attention in Depth Lecture | CampusX
76_Why is Self Attention calledSelf Attention Vs Luong Attention in Depth Lecture CampusXocr
Day 77
What is Multi-head Attention in Transformers | Multi-head Attention v Self Attention | Deep Learning
77_What is Multi-head Attention in Transformers Multi-head Attention v Self Attention Deep Learningocr
Day 78
Positional Encoding in Transformers | Deep Learning | CampusX
78_Positional Encoding in Transformers Deep Learning CampusXocr
Day 79
Layer Normalization in Transformers | Layer Norm Vs Batch Norm
79_Layer Normalization in Transformers Layer Norm Vs Batch Normocr
Day 80
Transformer Architecture | Part 1 Encoder Architecture | CampusX
80_Transformer Architecture Part 1 Encoder Architecture CampusXocr
Day 81
Masked Self Attention | Masked Multi-head Attention in Transformer | Transformer Decoder
81_Masked Self Attention Masked Multi-head Attention in Transformer Transformer Decoderocr
Day 82
Cross Attention in Transformers | 100 Days Of Deep Learning | CampusX
82_Cross Attention in Transformers 100 Days Of Deep Learning CampusXocr
Day 83
Transformer Decoder Architecture | Deep Learning | CampusX
83_Transformer Decoder Architecture Deep Learning CampusXocr
Day 84
Transformer Inference | How Inference is done in Transformer? | Deep Learning | CampusX
84_Transformer Inference How Inference is done in Transformer Deep Learning CampusXocr