CampusX 100 Days of Deep Learning: study plan and how long it takes

Day 1 · 1h 59m

• 1100 Days of Deep Learning | Course Announcement19m
• 2What is Deep Learning? Deep Learning Vs Machine Learning | Complete Deep Learning Course1h 7m
• 3Types of Neural Networks | History of Deep Learning | Applications of Deep Learning33m

Day 2 · 1h 30m

• 4What is a Perceptron? Perceptron Vs Neuron | Perceptron Geometric Intuition39m
• 5Perceptron Trick | How to train a Perceptron | Perceptron Part 2 | Deep Learning Full Course52m

Day 3 · 1h 58m

• 6Perceptron Loss Function | Hinge Loss | Binary Cross Entropy | Sigmoid Function59m
• 7Problem with Perceptron8m
• 8MLP Notation13m
• 9Multi Layer Perceptron | MLP Intuition38m

Day 4 · 1h 37m

• 10Forward Propagation | How a neural network predicts output?16m
• 11Customer Churn Prediction using ANN | Keras and Tensorflow | Deep Learning Classification35m
• 12Handwritten Digit Classification using ANN | MNIST Dataset29m
• 13Graduate Admission Prediction using ANN18m

Day 5 · 1h 54m

• 14Loss Functions in Deep Learning | Deep Learning | CampusX1h
• 15Backpropagation in Deep Learning | Part 1 | The What?54m

Day 6 · 1h 40m

• 16Backpropagation Part 2 | The How | Complete Deep Learning Playlist1h
• 17Backpropagation Part 3 | The Why | Complete Deep Learning Playlist40m

Day 7 · 1h 36m

• 18Vanishing Gradient Problem in ANN | Exploding Gradient Problem | Code Example32m
• 19MLP Memoization | Complete Deep Learning Playlist25m
• 20Gradient Descent in Neural Networks | Batch vs Stochastics vs Mini Batch Gradient Descent38m

Day 8 · 1h 46m

• 21How to Improve the Performance of a Neural Network30m
• 22Early Stopping In Neural Networks | End to End Deep Learning Course12m
• 23Data Scaling in Neural Network | Feature Scaling in ANN | End to End Deep Learning Course17m
• 24Dropout Layer in Deep Learning | Dropouts in ANN | End to End Deep Learning28m
• 25Dropout Layers in ANN | Code Example | Regression | Classification19m

Day 9 · 1h 54m

• 26Regularization in Deep Learning | L2 Regularization in ANN | L1 Regularization | Weight Decay in ANN36m
• 27Activation Functions in Deep Learning | Sigmoid, Tanh and Relu Activation Function45m
• 28Relu Variants Explained | Leaky Relu | Parametric Relu | Elu | Selu | Activation Functions Part 233m

Day 10 · 1h 54m

• 29Weight Initialization Techniques | What not to do? | Deep Learning49m
• 30Xavier/Glorat And He Weight Initialization in Deep Learning21m
• 31Batch Normalization in Deep Learning | Batch Learning in Keras44m

Day 11 · 1h 48m

• 32Optimizers in Deep Learning | Part 1 | Complete Deep Learning Course23m
• 33Exponentially Weighted Moving Average or Exponential Weighted Average | Deep Learning19m
• 34SGD with Momentum Explained in Detail with Animations | Optimizers in Deep Learning Part 238m
• 35Nesterov Accelerated Gradient (NAG) Explained in Detail | Animations | Optimizers in Deep Learning28m

Day 12 · 1h 57m

• 36AdaGrad Explained in Detail with Animations | Optimizers in Deep Learning Part 426m
• 37RMSProp Explained in Detail with Animations | Optimizers in Deep Learning Part 513m
• 38Adam Optimizer Explained in Detail with Animations | Optimizers in Deep Learning Part 513m
• 39Keras Tuner | Hyperparameter Tuning a Neural Network1h 6m

Day 13 · 1h 36m

• 40What is Convolutional Neural Network (CNN) | CNN Intution27m
• 41CNN Vs Visual Cortex | The Famous Cat Experiment | History of CNN15m
• 42CNN Part 3 | Convolution Operation29m
• 43Padding & Strides in CNN | CNN Lecture 4 | Deep Learning24m

Day 14 · 1h 42m

• 44Pooling Layer in CNN | MaxPooling in Convolutional Neural Network28m
• 45CNN Architecture | LeNet -5 Architecture20m
• 46Comparing CNN Vs ANN | CampusX18m
• 47Backpropagation in CNN | Part 1 | Deep Learning36m

Day 15 · 1h 38m

• 48CNN Backpropagation Part 2 | How Backpropagation works on Convolution, Maxpooling and Flatten Layers43m
• 49Cat Vs Dog Image Classification Project | Deep Learning Project | CNN Project27m
• 50Data Augmentation in Deep Learning | CNN27m

Day 16 · 1h 37m

• 51Pretrained models in CNN | ImageNET Dataset | ILSVRC | Keras Code24m
• 52What does a CNN see? | Visualizing CNN Filters and Feature Maps | CampusX13m
• 53What is Transfer Learning? Transfer Learning in Keras | Fine Tuning Vs Feature Extraction34m
• 54Keras Functional Model | How to build non-linear Neural Networks?26m

Day 17 · 1h 49m

• 55Why RNNs are needed | RNNs Vs ANNs | RNN Part 130m
• 56Recurrent Neural Network | Forward Propagation | Architecture42m
• 57RNN Sentiment Analysis | RNN Code Example in Keras | CampusX37m

Day 18 · 1h 29m

• 58Types of RNN | Many to Many | One to Many | Many to One RNNs22m
• 59How Backpropagation works in RNN | Backpropagation Through Time34m
• 60Problems with RNN | 100 Days of Deep Learning32m

Day 19 · 1h 53m

• 61LSTM | Long Short Term Memory | Part 1 | The What? | CampusX42m
• 62LSTM Architecture | Part 2 | The How? | CampusX1h 10m

Day 20 · 1h

• 63LSTM | Part 3 | Next Word Predictor Using | CampusX1h

Day 21 · 1h 26m

• 64Gated Recurrent Unit | Deep Learning | GRU | CampusX1h 26m

Day 22 · 1h 11m

• 65Deep RNNs | Stacked RNNs | Stacked LSTMs | Stacked GRUs | CampusX45m
• 66Bidirectional RNN | BiLSTM | Bidirectional LSTM | Bidirectional GRU26m

Day 23 · 1h 27m

• 67The Epic History of Large Language Models (LLMs) | From LSTMs to ChatGPT | CampusX1h 27m

Day 24 · 1h 55m

• 68Encoder Decoder | Sequence-to-Sequence Architecture | Deep Learning | CampusX1h 14m
• 69Attention Mechanism in 1 video | Seq2Seq Networks | Encoder Decoder Architecture41m

Day 25 · 1h 53m

• 70Bahdanau Attention Vs Luong Attention53m
• 71Introduction to Transformers | Transformers Part 11h

Day 26 · 1h 47m

• 72What is Self Attention | Transformers Part 2 | CampusX23m
• 73Self Attention in Transformers | Deep Learning | Simple Explanation with Code!1h 23m

Day 27 · 1h 34m

• 74Scaled Dot Product Attention | Why do we scale Self Attention?51m
• 75Self Attention Geometric Intuition | How to Visualize Self Attention | CampusX21m
• 76Why is Self Attention called "Self"? | Self Attention Vs Luong Attention in Depth Lecture | CampusX23m

Day 28 · 1h 52m

• 77What is Multi-head Attention in Transformers | Multi-head Attention v Self Attention | Deep Learning38m
• 78Positional Encoding in Transformers | Deep Learning | CampusX1h 13m

Day 29 · 1h 42m

• 79Layer Normalization in Transformers | Layer Norm Vs Batch Norm47m
• 80Transformer Architecture | Part 1 Encoder Architecture | CampusX55m

Day 30 · 1h 35m

• 81Masked Self Attention | Masked Multi-head Attention in Transformer | Transformer Decoder1h 1m
• 82Cross Attention in Transformers | 100 Days Of Deep Learning | CampusX34m

Day 31 · 1h 34m

• 83Transformer Decoder Architecture | Deep Learning | CampusX48m
• 84Transformer Inference | How Inference is done in Transformer? | Deep Learning | CampusX45m