56:09

Prof. Simo Särkkä - Parallel/distributed methods for state-space models

01:12:37

Dr. Hamed R. Tavakoli - Compact and Efficient Neural Networks

21:36

What is Data ?

01:45:13

School Logistics. Exercises.

30:05

Model Validation, Selection and Regularization

23:10

Model Training

40:37

CS-EJ3211 Model Validation and Selection

01:44:26

CS-C3240 Lecture (Regularization and ML at Helsinki)

23:41

CS-EJ3211 Linear Regression - MSE, MAE and Huber

01:03:33

CS-EJ3211 Three Components of Machine Learning with Python

33:21

Python Intro

06:11

CS-C3240 - A Glimpse on Model Regularization

27:12

CS-C3240 - Model Validation and Selection

01:30:35

CS-C3240. Empirical Risk Minimization.

01:37:15

CS-C3240 Course Logistics. Data-Model-Loss.

02:26

Machine Learning Water Melon

03:49

Logistic Regression in Python - Part 2

05:09

Logistic Regression in Python - Part I

09:54

Linear and Polynomial Regression with Python

05:06

Spectral Clustering

03:59

Loading Datapoints in Python using Pandas Library

01:25

Machine Learning to Ski

19:56

Machine Learning for Taksi Helsinki

03:10

How to Edit and Submit Coding Assignments

25:36

Generative Adversarial Networks

01:05

Collecting Datapoints

02:24

How to Fetch and Re-Fetch Notebooks

44:45

Natural Language Processing with Python

59:30

Deep Learning for Remote Sensing and GIS

07:29

Data Pipelines in TensorFlow

37:43

Regularization - Data Augmentation and Transfer Learning

53:22

Decoding Electromagnetic Brain Activity

48:46

Convolutional Neural Networks

51:32

Aerial Inspection RetinaNet for Land Search and Rescue

52:24

Gradient Based Learning

07:45

Loss is for Tuning. Metric is for Selling Deep Learning.

20:20

Semi-Supervised Learning for Infant Motility Classification

01:06:38

Artificial Neural Networks with Keras

26:12

Elements of Deep Learning.

29:38

Elements of Deep Learning.

01:21:56

Deep Learning with Python.

46:33

Image Processing Labs with Jupyter

13:12

Federated Learning from Big Data over Networks

04:56

CS-C3240 Student Project - Tommi Salmi

01:37

Building a Personalized Face Mask Detector in One Minute

59:53

Feature Learning

46:23

Connectivity-based Clustering

01:02:09

Soft Clustering

53:05

Hard Clustering with k-means

00:10

How fast is k-means?

01:31:46

Model Regularization and Data Augmentation in a Spreadsheet

20:29

Regularization and Data Augmentation - Intro

48:13

Diagnosing ML in a Spreadsheet

01:42:38

Model Selection in Machine Learning

01:55

Ski Heaven Helsinki

01:22:01

Model Validation, Selection and Regularization

01:30:54

Feature Maps and Classification Loss

01:21:13

Three Components of ML: Data, Model and Loss

06:35

Creating NBGraded Assignments on JupyterHub

00:15

Machine Learning Searches a Good Hypothesis By Minimizing Loss