1:02:09 TDLS - Classics: SMOTE, Synthetic Minority Over-sampling Technique (algorithm) LLMs Explained - Aggregate…
28:28 SMOTE, Synthetic Minority Over-sampling Technique (discussions) | AISC Foundational LLMs Explained - Aggregate…
28:25 [original backprop paper] Learning representations by back-propagating errors (part1) | AISC LLMs Explained - Aggregate…
15:38 [original backprop paper] Learning representations by back-propagating errors (part2) | AISC LLMs Explained - Aggregate…
1:19:50 [Variational Autoencoder] Auto-Encoding Variational Bayes | AISC Foundational LLMs Explained - Aggregate…
56:33 Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities | AISC LLMs Explained - Aggregate…
1:03:43 Eve: A Gradient Based Optimization Method with Locally and Globally Adaptive Learning Rates | TDLS LLMs Explained - Aggregate…
44:58 Connectionist Temporal Classification, Labelling Unsegmented Sequence Data with RNN | TDLS LLMs Explained - Aggregate…
19:26 EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis LLMs Explained - Aggregate…
59:07 How Can We Be So Dense? The Benefits of Using Highly Sparse Representations | AISC LLMs Explained - Aggregate…
1:02:48 [WeightWatcher] Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory LLMs Explained - Aggregate…
