Lecture 2: Introduction continued..
Lecture 14: ResNet
Lecture 35: Preparing text as input to neural networks
Strided Convolution (in Hindi)
Lecture 12: LeNet, AlexNet, VGG, Inception Network
Lecture 8: Feed forward neural network continued..
Lecture 25: ResNet continued and Inception Network
Lecture 15: Minimax -- Alpha beta pruning
K-means distortion function (in Hindi)
Lecture 42: Bi-directional RNNs and Introduction to Language Modeling
Lecture 9: Multiclass classification and softmax
Changing variable bindings in Python (in Hindi)
Lecture 11: Parameter sharing and sparsity of connections in CNNs
K-means algorithm (in Hindi)
Lecture 6: UCS and greedy strategy
Lecture 10: n-gram Language Model
word2vec: Choosing words for negative sampling (in Hindi)
word2vec: Computational Issues (in Hindi)
Lecture 17: Equations governing RNNs
Lecture 12: Visualizing gradients for descent
Lecture 33: Concluding face detection