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