L11.5-2: Sequence-to-Sequence Learning, using a Transformer encoder/decoder
13:17
L11.5-2: Sequence-to-Sequence Learning, using a Transformer encoder/decoder
L11.5-1: Sequence-to-Sequence Learning, using RNN encoder/decoder
28:44
L11.5-1: Sequence-to-Sequence Learning, using RNN encoder/decoder
L11.4.3-4: Transformer Architecture: Implementing and adding positional encoding
26:52
L11.4.3-4: Transformer Architecture: Implementing and adding positional encoding
L11.4.1-2: Transformer Architecture: Neural attention and the MultiHead layer
26:56
L11.4.1-2: Transformer Architecture: Neural attention and the MultiHead layer
L11.3.3: Two Approaches for Representing Groups of Words: Sequence models and word embeddings
35:01
L11.3.3: Two Approaches for Representing Groups of Words: Sequence models and word embeddings
L11.3.2: Two Approaches for Representing Groups of Words: Bag-of-Word Models
23:38
L11.3.2: Two Approaches for Representing Groups of Words: Bag-of-Word Models
L11.1-2: NLP and Preparing Text Data
29:51
L11.1-2: NLP and Preparing Text Data
L09.4: Interpreting what Convnets Learn
28:19
L09.4: Interpreting what Convnets Learn
L10.4: Advanced Use of Recurrent Neural Networks
18:17
L10.4: Advanced Use of Recurrent Neural Networks
L10.3: Understanding Recurrent Neural Networks
21:52
L10.3: Understanding Recurrent Neural Networks
L10.1-2: Timeseries Tasks and a Temperature Forecasting Example
33:37
L10.1-2: Timeseries Tasks and a Temperature Forecasting Example
L09.3.5: A Mini Xception-like Model Example
12:00
L09.3.5: A Mini Xception-like Model Example
L09.3: Modern Convnet Architecture Patterns
27:32
L09.3: Modern Convnet Architecture Patterns
L09.1-2: Computer Vision Tasks and an Image Segmentation Example
21:14
L09.1-2: Computer Vision Tasks and an Image Segmentation Example
L08.3: Leveraging a Pretrained Model
26:44
L08.3: Leveraging a Pretrained Model
L08.2: Training a Convnet from Scratch on a Small Dataset
28:41
L08.2: Training a Convnet from Scratch on a Small Dataset
L08.1: Introduction to Convnets
28:56
L08.1: Introduction to Convnets
L07.4: Writing your own Training and Evaluation Loops
14:55
L07.4: Writing your own Training and Evaluation Loops
L07.3: Metrics, Callbacks and TensorBoard
22:36
L07.3: Metrics, Callbacks and TensorBoard
L07.2: Keras APIs for Building Models
27:18
L07.2: Keras APIs for Building Models
L05.4: Improving Model Generalization
20:57
L05.4: Improving Model Generalization
L05.3: Improving Model Fit
14:16
L05.3: Improving Model Fit
L05.2: Evaluating Machine Learning Models
18:44
L05.2: Evaluating Machine Learning Models
L05.1: Generalization: The goal of machine learning
25:35
L05.1: Generalization: The goal of machine learning
L04.3: Predicting House Prices: Regression
19:42
L04.3: Predicting House Prices: Regression
L04.2: Classifying News Articles: Multiclass Classification
17:59
L04.2: Classifying News Articles: Multiclass Classification
L04.1: Classifying Movie Reviews: Binary Classification
24:53
L04.1: Classifying Movie Reviews: Binary Classification
CSci 560 Help Session / Tuesday 06/03/2025
41:13
CSci 560 Help Session / Tuesday 06/03/2025
C03: Setting up NVidia GPU using class DevContainers
27:05
C03: Setting up NVidia GPU using class DevContainers
C02: JupyterHub / Python DevContainer Assignment Workflow
31:24
C02: JupyterHub / Python DevContainer Assignment Workflow