Deep Learning
- Neuron and Neural Network
- Artificial Neuron
- Perceptron
- Adaline
- Limitation of Linear Models
- Neural Networks
- Multi-Layer Perceptron (MLP)
- Recurrent Neural Networks
- History of Neural Networks
- Multilayered Perceptron and Error Backpropagation Algorithms
- Vanishing Gradient Problem
- ReLU Function
- Weight Initialization
- Overfitting Problem
- Breakthrough in Deep Learning
- Convolutional Neural Networks
- LeNet
- AlexNet
- VGGNet
- GoogleNet
- ResNet
- ResNext
- DenseNet
- DPN
- Generative Models (stacked RBM, Deep Belief Network)
- Generative Models in Deep learning
- Probability Graphical Model
- Boltzmann Machine
- Restricted Boltzmann Machine (RBM)
- Deep Belief Network (DBN)
- Deep Boltzmann Machine
- Generative Adversarial Network (GAN)
- Deep Boltzmann Machine
- DCGAN
- BiGAN
- Recurrent Neural Networks
- Recurrent Neural Networks (RNN)
- Learning Algorithms for RNN Models
- Back Propagation Through Time (BPTT)
- Vanishing Gradient and Exploding Gradient Problems
- RNN with Relu Function
- Long Short Time Memory (LSTM) RNN
- Gated Recurrent Unit (GRU) RNN
- Extension of RNN
- Deep RNN
- Deep Bidirectional RNN
- Deep RNN with Residual Connection
- Application of RNN
- Autoencoders
- Training of Autoencoder
- Denoising Autoencoder
- Sparse Autoencoder
- Variational Autoencoder (VAE)
- End-to-End Learning
- Encoder-Decoder Network
- Attention Model
- Memory-augmented models
- Neural Turing Machine
- Differentiable Neural Computer
- Others Model
- Memory Network
- End-to-End Memory Network
- Dynamic Memory Network
- Deep Learning Application
- Natural Language Processing (NLP)
- Word2Vec
- CBOW Model
- Skip-gram Model
- Hierarchical Softmax
- Computer Vision (CV)
- Category Recognition
- ImageNet Large Scale Visual Recognition Challenge (ILSRVRC)
- Object Location Detection and Recognition
- Speech Recognition
- GMM-HMM
- DNN-GMM-HMM
- End-to-End DNN
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