Name: 100 Days of Deep Learning [YouTube]
Brand: CampusX
SKU: 6818549cc1a70a79d992169f
Price: 499 INR
Availability: InStock

100 Days of Deep Learning [YouTube]

Master deep learning in just 100 days!

Instructor: CampusX

Validity Period: 1095 days

₹499 including 18% GST

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Description
Course Content
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Description:

A comprehensive deep learning course spread across 100 days, covering a wide range of topics from basics to advanced concepts. Ideal for beginners and enthusiasts looking to enhance their understanding of deep learning models and techniques.

Key Highlights:

Daily deep learning tutorials
Hands-on projects and exercises
Progressive learning curve

What you will learn:

Enhanced Understanding
Gain a strong foundation in deep learning concepts and algorithms.
Practical Experience
Apply your knowledge with real-world projects and exercises.
Progressive Learning
Gradually advance from basics to advanced topics over 100 days.

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Important
	Readme
	100 Days of DL Complete Notes (959 pages)
Day 1
	100 Days of Deep Learning \| Course Announcement 19:00	Preview
	01_Course_Announcementocr (7 pages)	Preview
Day 2
	What is Deep Learning? Deep Learning Vs Machine Learning 67:00	Preview
	02_What is Deep Learning_Deep Learning Vs Machine Learningocr (22 pages)	Preview
Day 3
	Types of Neural Networks \| History of Deep Learning \| Applications of Deep Learning 33:00	Preview
	03_Types of Neural Networks History of Deep Learningocr (24 pages)	Preview
Day 4
	What is a Perceptron? Perceptron Vs Neuron \| Perceptron Geometric Intuition 39:00	Preview
	04_What_is_perceptron Perceptron vs Neuron Perceptron Geometric Intuitionocr (7 pages)	Preview
Day 5
	Perceptron Trick \| How to train a Perceptron \| Perceptron Part 2 \| Deep Learning Full Course 52:00	Preview
	05_Perceptron Trick How to train a perceptron Part 2ocr (7 pages)	Preview
Day 6
	Perceptron Loss Function \| Hinge Loss \| Binary Cross Entropy \| Sigmoid Function 59:00
	06_Perceptron Loss Function Hinge Loss Binary Cross Entropy Sigmoid Functionocr (9 pages)
Day 7
	Problem with Perceptron 8:00
	07_Problem with perceptronocr (8 pages)
Day 8
	MLP Notation 13:00
	08_MLP Notationocr (8 pages)
Day 9
	Multi Layer Perceptron \| MLP Intuition 38:00
	09_Multi Layer Perceptron MLP Intuitionocr (11 pages)
Day 10
	Forward Propagation \| How a neural network predicts output? 16:00
	10_Forward Propagation How a neural network predicts outputocr (5 pages)
Day 11
	Customer Churn Prediction using ANN \| Keras and Tensorflow \| Deep Learning Classification 35:00
	11_Customer Churn Prediction using ANN Keras and Tensorflow Deep Learning Classificationocr (9 pages)
Day 12
	Handwritten Digit Classification using ANN \| MNIST Dataset 29:00
	12_Handwritten Digit Classification using ANN MNIST Datasetocr (11 pages)
Day 13
	Graduate Admission Prediction using ANN 18:00
	13_Graduate Admission Prediction using ANNocr (7 pages)
Day 14
	Loss Functions in Deep Learning \| Deep Learning \| CampusX 60:00
	14_Loss Functions in Deep Learning Deep Learning CampusXocr (8 pages)
Day 15
	Backpropagation in Deep Learning \| Part 1 \| The What? 54:00
	15_Backpropagation in Deep Learning Part 1 The Whatocr (7 pages)
Day 16
	Backpropagation Part 2 \| The How \| Complete Deep Learning Playlist 60:00
	16_Backpropagation Part 2 The How Complete Deep Learning Playlistocr (8 pages)
Day 17
	Backpropagation Part 3 \| The Why \| Complete Deep Learning Playlist 40:00
	17_Backpropagation Part 3 The Why Complete Deep Learning Playlistocr (10 pages)
Day 18
	MLP Memoization \| Complete Deep Learning Playlist 25:00
	18_MLP Memoization Complete Deep Learning Playlistocr (6 pages)
Day 19
	Gradient Descent in Neural Networks \| Batch vs Stochastics vs Mini Batch Gradient Descent 38:00
	19_Gradient_Descent_in_Neural_Network Batch_vs_Stochastics_vs_Mini_Batchocr (10 pages)
Day 20
	Vanishing Gradient Problem in ANN \| Exploding Gradient Problem \| Code Example 32:00
	20_Vanishing Gradient Problem in ANN Exploding Gradient Problem Code Exampleocr (9 pages)
Day 21
	How to Improve the Performance of a Neural Network 30:00
	21_How to Improve the Performance of a Neural Networkocr (9 pages)
Day 22
	Early Stopping In Neural Networks \| End to End Deep Learning Course 12:00
	22_Early Stopping In Neural Networks End to End Deep Learning Courseocr (8 pages)
Day 23
	Data Scaling in Neural Network \| Feature Scaling in ANN \| End to End Deep Learning Course 17:00
	23_Data Scaling in Neural Network Feature Scaling in ANN End to End Deep Learning Courseocr (6 pages)
Day 24
	Dropout Layer in Deep Learning \| Dropouts in ANN \| End to End Deep Learning 28:00
	24_Dropout Layer in Deep Learning Dropouts in ANN End to End Deep Learningocr (6 pages)
Day 25
	Dropout Layers in ANN \| Code Example \| Regression \| Classification 19:00
	25_Dropout Layers in ANN Code Example Regression Classificationocr (9 pages)
Day 26
	Regularization in Deep Learning \| L2 Regularization in ANN \| L1 Regularization \| Weight Decay in ANN 36:00
	26_Regularization in Deep Learning L2 Regularization in ANN L1 Regularization Weight Decay in ANNocr (7 pages)
Day 27
	Activation Functions in Deep Learning \| Sigmoid, Tanh and Relu Activation Function 45:00
	27_Activation Functions in Deep Learning Sigmoid, Tanh and Relu Activation Functionocr (14 pages)
Day 28
	Relu Variants Explained \| Leaky Relu \| Parametric Relu \| Elu \| Selu \| Activation Functions Part 2 33:00
	28_Relu Variants Explained Elu Selu Activation Functions Part 2ocr (28 pages)
Day 29
	Weight Initialization Techniques \| What not to do? \| Deep Learning 49:00
	29_Weight Initialization Techniques What not to do Deep Learningocr (12 pages)
Day 30
	Xavier/Glorat And He Weight Initialization in Deep Learning 21:00
	30_Xavier Glorat And He Weight Initialization in Deep Learningocr (8 pages)
Day 31
	Batch Normalization in Deep Learning \| Batch Learning in Keras 44:00
	31_Batch Normalization in Deep Learning Batch Learning in Kerasocr (15 pages)
Day 32
	Optimizers in Deep Learning \| Part 1 \| Complete Deep Learning Course 23:00
	32_Optimizers in Deep Learning Part 1 Complete Deep Learning Courseocr (7 pages)
Day 33
	Exponentially Weighted Moving Average or Exponential Weighted Average \| Deep Learning 19:00
	33_Exponentially Weighted Moving Average or Exponential Weighted Average Deep Learningocr (15 pages)
Day 34
	SGD with Momentum Explained in Detail with Animations \| Optimizers in Deep Learning Part 2 38:00
	34_SGD with Momentum Explained in Detail with Animations Optimizers in Deep Learning Part 2ocr (13 pages)
Day 35
	Nesterov Accelerated Gradient (NAG) Explained in Detail \| Animations \| Optimizers in Deep Learning 28:00
	35_Nesterov Accelerated Gradient (NAG) Explained in Detail Animations Optimizers in Deep Learningocr (8 pages)
Day 36
	AdaGrad Explained in Detail with Animations \| Optimizers in Deep Learning Part 4 26:00
	36_AdaGrad Explained in Detail with Animations Optimizers in Deep Learning Part 4ocr (7 pages)
Day 37
	RMSProp Explained in Detail with Animations \| Optimizers in Deep Learning Part 5 13:00
	37_RMSProp Explained in Detail with Animations Optimizers in Deep Learning Part 5ocr (10 pages)
Day 38
	Adam Optimizer Explained in Detail with Animations \| Optimizers in Deep Learning Part 5 13:00
	38_Adam Optimizer Explained in Detail with Animations Optimizers in Deep Learning Part 5ocr (8 pages)
Day 39
	Keras Tuner \| Hyperparameter Tuning a Neural Network 66:00
	39_Keras Tuner Hyperparameter Tuning a Neural Networkocr (10 pages)
Day 40
	What is Convolutional Neural Network (CNN) \| CNN Intution 27:00
	40_What is Convolutional Neural Network (CNN) CNN Intutionocr (22 pages)
Day 41
	CNN Vs Visual Cortex \| The Famous Cat Experiment \| History of CNN 15:00
	41_CNN_Vs_Visual_Cortex_The_Famous_Cat_Experiment_History_of_CNNocr (8 pages)
Day 42
	CNN Part 3 \| Convolution Operation 29:00
	42_CNN_Part_3_Convolution_Operationocr (18 pages)
Day 43
	Padding & Strides in CNN \| CNN Lecture 4 \| Deep Learning 24:00
	43_Padding & Strides in CNN CNN Lecture 4 Deep Learningocr (15 pages)
Day 44
	Pooling Layer in CNN \| MaxPooling in Convolutional Neural Network 28:00
	44_Pooling Layer in CNN MaxPooling in Convolutional Neural Networkocr (20 pages)
Day 45
	CNN Architecture \| LeNet -5 Architecture 20:00
	45_CNN Architecture LeNet -5 Architectureocr (9 pages)
Day 46
	Comparing CNN Vs ANN \| CampusX 18:00
	46_Comparing CNNVs_ANN_CampusXocr (7 pages)
Day 47
	Backpropagation in CNN \| Part 1 \| Deep Learning 36:00
	47_Backpropagation in CNN Part 1 Deep Learningocr (11 pages)
Day 48
	CNN Backpropagation Part 2 \| How Backpropagation works on Convolution, Maxpooling and Flatten Layers 43:00
	48_CNN Backpropagation Part 2 How Backpropagation works on Convolution, Maxpooling and Flatten Layersocr (8 pages)
Day 49
	Cat Vs Dog Image Classification Project \| Deep Learning Project \| CNN Project 27:00
	49_Cat Vs Dog Image Classification ProjectDeep Learning Project CNN Projectocr (6 pages)
Day 50
	Data Augmentation in Deep Learning \| CNN 27:00
	50_Data Augmentation in Deep Learning CNNocr (5 pages)
Day 51
	Pretrained models in CNN \| ImageNET Dataset \| ILSVRC \| Keras Code 24:00
	51_Pretrained models in CNN ImageNET Dataset ILSVRC Keras Codeocr (9 pages)
Day 52
	What does a CNN see? \| Visualizing CNN Filters and Feature Maps \| CampusX 13:00
	52_What does a CNN see Visualizing CNN Filters and Feature Maps CampusXocr (5 pages)
Day 53
	What is Transfer Learning? Transfer Learning in Keras \| Fine Tuning Vs Feature Extraction 34:00
	53_What is Transfer Learning_Transfer Learning in Keras Fine Tuning_Vs_Feature_Extractionocr (17 pages)
Day 54
	Keras Functional Model \| How to build non-linear Neural Networks? 26:00
	54ocr (5 pages)
Day 55
	Why RNNs are needed \| RNNs Vs ANNs \| RNN Part 1 30:00
	55_Why RNNs are needed RNNs Vs ANNs RNN Part 1ocr (13 pages)
Day 56
	Recurrent Neural Network \| Forward Propagation \| Architecture 42:00
	56_Recurrent Neural Network Forward Propagation Architectureocr (21 pages)
Day 57
	RNN Sentiment Analysis \| RNN Code Example in Keras \| CampusX 37:00
	57_RNN Sentiment Analysis RNN Code Example in Keras CampusXocr (7 pages)
Day 58
	Types of RNN \| Many to Many \| One to Many \| Many to One RNNs 22:00
	58_ Types of RNN Many to Many One to Many Many to One RNNsocr (7 pages)
Day 59
	How Backpropagation works in RNN \| Backpropagation Through Time 34:00
	59_How Backpropagation works in RNN Backpropagation Through Timeocr (5 pages)
Day 60
	Problems with RNN \| 100 Days of Deep Learning 32:00
	60_Problems with RNN 100 Days of Deep Learningocr (9 pages)
Day 61
	LSTM \| Long Short Term Memory \| Part 1 \| The What? \| CampusX 42:00
	61_LSTM Long Short Term Memory Part 1 The WhatCampusXocr (12 pages)
Day 62
	LSTM Architecture \| Part 2 \| The How? \| CampusX 70:00
	62_LSTM ArchitecturePart 2 The How_CampusXocr (12 pages)
Day 63
	LSTM \| Part 3 \| Next Word Predictor Using \| CampusX 60:00
	63_LSTM Part 3 Next Word Predictor Using CampusXocr (11 pages)
Day 64
	Gated Recurrent Unit \| Deep Learning \| GRU \| CampusX 86:00
	64_Deep RNNs Stacked RNNs Stacked LSTMs Stacked GRUs CampusXocr (12 pages)
Day 65
	Deep RNNs \| Stacked RNNs \| Stacked LSTMs \| Stacked GRUs \| CampusX 45:00
	65_Gated Recurrent Unit Deep Learning GRU CampusXocr (10 pages)
Day 66
	Bidirectional RNN \| BiLSTM \| Bidirectional LSTM \| Bidirectional GRU 26:00
	66_Bidirectional RNN BiLSTM Bidirectional LSTM Bidirectional GRUocr (7 pages)
Day 67
	The Epic History of Large Language Models (LLMs) \| From LSTMs to ChatGPT \| CampusX 87:00
	67_The Epic History of Large Language Models (LLMs) From LSTMs to ChatGPT_CampusXocr (21 pages)
Day 68
	Encoder Decoder \| Sequence-to-Sequence Architecture \| Deep Learning \| CampusX 74:00
	68_Encoder_Decoder_Sequence_to_Sequence_Architecture_Deep_Learning_CampusXocr (29 pages)
Day 69
	Attention Mechanism in 1 video \| Seq2Seq Networks \| Encoder Decoder Architecture 41:00
	69_Attention Mechanism in 1 video_Seq2Seq Networks Encoder Decoder Architectureocr (13 pages)
Day 70
	Bahdanau Attention Vs Luong Attention 53:00
	70_Bahdanau Attention Vs Luong Attentionocr (18 pages)
Day 71
	Introduction to Transformers \| Transformers Part 1 60:00
	71_Introduction to Transformers Transformers Part 1ocr (24 pages)
Day 72
	What is Self Attention \| Transformers Part 2 \| CampusX 23:00
	72_What is Self Attention Transformers Part 2 CampusXocr (8 pages)
Day 73
	Self Attention in Transformers \| Deep Learning \| Simple Explanation with Code! 83:00
	73_Self Attention in Transformers Deep Learning Simple Explanation with Codeocr (21 pages)
Day 74
	Scaled Dot Product Attention \| Why do we scale Self Attention? 51:00
	74_Scaled Dot Product Attention Why do we scale Self Attentionocr (7 pages)
Day 75
	Self Attention Geometric Intuition \| How to Visualize Self Attention \| CampusX 21:00
	75_Self Attention Geometric Intuition How to Visualize Self Attention CampusXocr (9 pages)
Day 76
	Why is Self Attention called "Self"? \| Self Attention Vs Luong Attention in Depth Lecture \| CampusX 23:00
	76_Why is Self Attention calledSelf Attention Vs Luong Attention in Depth Lecture CampusXocr (7 pages)
Day 77
	What is Multi-head Attention in Transformers \| Multi-head Attention v Self Attention \| Deep Learning 38:00
	77_What is Multi-head Attention in Transformers Multi-head Attention v Self Attention Deep Learningocr (12 pages)
Day 78
	Positional Encoding in Transformers \| Deep Learning \| CampusX 73:00
	78_Positional Encoding in Transformers Deep Learning CampusXocr (24 pages)
Day 79
	Layer Normalization in Transformers \| Layer Norm Vs Batch Norm 47:00
	79_Layer Normalization in Transformers Layer Norm Vs Batch Normocr (13 pages)
Day 80
	Transformer Architecture \| Part 1 Encoder Architecture \| CampusX 55:00
	80_Transformer Architecture Part 1 Encoder Architecture CampusXocr (21 pages)
Day 81
	Masked Self Attention \| Masked Multi-head Attention in Transformer \| Transformer Decoder 61:00
	81_Masked Self Attention Masked Multi-head Attention in Transformer Transformer Decoderocr (15 pages)
Day 82
	Cross Attention in Transformers \| 100 Days Of Deep Learning \| CampusX 34:00
	82_Cross Attention in Transformers 100 Days Of Deep Learning CampusXocr (4 pages)
Day 83
	Transformer Decoder Architecture \| Deep Learning \| CampusX 48:00
	83_Transformer Decoder Architecture Deep Learning CampusXocr (10 pages)
Day 84
	Transformer Inference \| How Inference is done in Transformer? \| Deep Learning \| CampusX 45:00
	84_Transformer Inference How Inference is done in Transformer Deep Learning CampusXocr

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100 Days of Deep Learning [YouTube]

₹499 including 18% GST