100 Days of Deep Learning

A complete end-to-end playlist on Deep Learning where topics like ANN, CNN, and RNN are covered.

Curated by: CampusX (84 videos)

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Tracks in this Playlist

• 100 Days of Deep Learning | Course Announcement
• What is Deep Learning? Deep Learning Vs Machine Learning | Complete Deep Learning Course
• Types of Neural Networks | History of Deep Learning | Applications of Deep Learning
• What is a Perceptron? Perceptron Vs Neuron | Perceptron Geometric Intuition
• Perceptron Trick | How to train a Perceptron | Perceptron Part 2 | Deep Learning Full Course
• Perceptron Loss Function | Hinge Loss | Binary Cross Entropy | Sigmoid Function
• Problem with Perceptron
• MLP Notation
• Multi Layer Perceptron | MLP Intuition
• Forward Propagation | How a neural network predicts output?
• Customer Churn Prediction using ANN | Keras and Tensorflow | Deep Learning Classification
• Handwritten Digit Classification using ANN | MNIST Dataset
• Graduate Admission Prediction using ANN
• Loss Functions in Deep Learning | Deep Learning | CampusX
• Backpropagation in Deep Learning | Part 1 | The What?
• Backpropagation Part 2 | The How | Complete Deep Learning Playlist
• Backpropagation Part 3 | The Why | Complete Deep Learning Playlist
• Vanishing Gradient Problem in ANN | Exploding Gradient Problem | Code Example
• MLP Memoization | Complete Deep Learning Playlist
• Gradient Descent in Neural Networks | Batch vs Stochastics vs Mini Batch Gradient Descent
• How to Improve the Performance of a Neural Network
• Early Stopping In Neural Networks | End to End Deep Learning Course
• Data Scaling in Neural Network | Feature Scaling in ANN | End to End Deep Learning Course
• Dropout Layer in Deep Learning | Dropouts in ANN | End to End Deep Learning
• Dropout Layers in ANN | Code Example | Regression | Classification
• Regularization in Deep Learning | L2 Regularization in ANN | L1 Regularization | Weight Decay in ANN
• Activation Functions in Deep Learning | Sigmoid, Tanh and Relu Activation Function
• Relu Variants Explained | Leaky Relu | Parametric Relu | Elu | Selu | Activation Functions Part 2
• Weight Initialization Techniques | What not to do? | Deep Learning
• Xavier/Glorat And He Weight Initialization in Deep Learning
• Batch Normalization in Deep Learning | Batch Learning in Keras
• Optimizers in Deep Learning | Part 1 | Complete Deep Learning Course
• Exponentially Weighted Moving Average or Exponential Weighted Average | Deep Learning
• SGD with Momentum Explained in Detail with Animations | Optimizers in Deep Learning Part 2
• Nesterov Accelerated Gradient (NAG) Explained in Detail | Animations | Optimizers in Deep Learning
• AdaGrad Explained in Detail with Animations | Optimizers in Deep Learning Part 4
• RMSProp Explained in Detail with Animations | Optimizers in Deep Learning Part 5
• Adam Optimizer Explained in Detail with Animations | Optimizers in Deep Learning Part 5
• Keras Tuner | Hyperparameter Tuning a Neural Network
• What is Convolutional Neural Network (CNN) | CNN Intution
• CNN Vs Visual Cortex | The Famous Cat Experiment | History of CNN
• CNN Part 3 | Convolution Operation
• Padding & Strides in CNN | CNN Lecture 4 | Deep Learning
• Pooling Layer in CNN | MaxPooling in Convolutional Neural Network
• CNN Architecture | LeNet -5 Architecture
• Comparing CNN Vs ANN | CampusX
• Backpropagation in CNN | Part 1 | Deep Learning
• CNN Backpropagation Part 2 | How Backpropagation works on Convolution, Maxpooling and Flatten Layers
• Cat Vs Dog Image Classification Project | Deep Learning Project | CNN Project
• Data Augmentation in Deep Learning | CNN
• Pretrained models in CNN | ImageNET Dataset | ILSVRC | Keras Code
• What does a CNN see? | Visualizing CNN Filters and Feature Maps | CampusX
• What is Transfer Learning? Transfer Learning in Keras | Fine Tuning Vs Feature Extraction
• Keras Functional Model | How to build non-linear Neural Networks?
• Why RNNs are needed | RNNs Vs ANNs | RNN Part 1
• Recurrent Neural Network | Forward Propagation | Architecture
• RNN Sentiment Analysis | RNN Code Example in Keras | CampusX
• Types of RNN | Many to Many | One to Many | Many to One RNNs
• How Backpropagation works in RNN | Backpropagation Through Time
• Problems with RNN | 100 Days of Deep Learning
• LSTM | Long Short Term Memory | Part 1 | The What? | CampusX
• LSTM Architecture | Part 2 | The How? | CampusX
• LSTM | Part 3 | Next Word Predictor Using | CampusX
• Gated Recurrent Unit | Deep Learning | GRU | CampusX
• Deep RNNs | Stacked RNNs | Stacked LSTMs | Stacked GRUs | CampusX
• Bidirectional RNN | BiLSTM | Bidirectional LSTM | Bidirectional GRU
• The Epic History of Large Language Models (LLMs) | From LSTMs to ChatGPT | CampusX
• Encoder Decoder | Sequence-to-Sequence Architecture | Deep Learning | CampusX
• Attention Mechanism in 1 video | Seq2Seq Networks | Encoder Decoder Architecture
• Bahdanau Attention Vs Luong Attention
• Introduction to Transformers | Transformers Part 1
• What is Self Attention | Transformers Part 2 | CampusX
• Self Attention in Transformers | Deep Learning | Simple Explanation with Code!
• Scaled Dot Product Attention | Why do we scale Self Attention?
• Self Attention Geometric Intuition | How to Visualize Self Attention | CampusX
• Why is Self Attention called "Self"? | Self Attention Vs Luong Attention in Depth Lecture | CampusX
• What is Multi-head Attention in Transformers | Multi-head Attention v Self Attention | Deep Learning
• Positional Encoding in Transformers | Deep Learning | CampusX
• Layer Normalization in Transformers | Layer Norm Vs Batch Norm
• Transformer Architecture | Part 1 Encoder Architecture | CampusX
• Masked Self Attention | Masked Multi-head Attention in Transformer | Transformer Decoder
• Cross Attention in Transformers | 100 Days Of Deep Learning | CampusX
• Transformer Decoder Architecture | Deep Learning | CampusX
• Transformer Inference | How Inference is done in Transformer? | Deep Learning | CampusX