The post Graph Analytics vs Graph ML appeared first on ML Conference.

The post AI, Machine Learning & Data Science Essentials in Python Part 2 appeared first on ML Conference.

Computer Vision has a long history of industries, and recent advances in deep learning have provided significant improvements in the ability to understand visual content. As a result of these research advances on problems such as object classification, object detection, and image segmentation, there has been a rapid increase in the adoption of Computer Vision in industry. This workshop aims to present different ML and DL methods in industrial video and image applications. Also different computer vision categories, namely, object classification, object detection, and image/video segmentation are covered in details from A to Z with practical examples. 

The workshop discussions will establish close connections between researchers in machine learning and computer vision communities and engineers in industry, and to benefit both academic researchers as well as industrial practitioners. At the end, the workshop aims to discuss the next steps in developing efficient feature representations from three aspects: energy efficient, label efficient, and sample efficient.

The post Deep Learning in Image and Video Applications Part 2 appeared first on ML Conference.

Computer Vision has a long history of industries, and recent advances in deep learning have provided significant improvements in the ability to understand visual content. As a result of these research advances on problems such as object classification, object detection, and image segmentation, there has been a rapid increase in the adoption of Computer Vision in industry. This workshop aims to present different ML and DL methods in industrial video and image applications. Also different computer vision categories, namely, object classification, object detection, and image/video segmentation are covered in details from A to Z with practical examples. 

The workshop discussions will establish close connections between researchers in machine learning and computer vision communities and engineers in industry, and to benefit both academic researchers as well as industrial practitioners. At the end, the workshop aims to discuss the next steps in developing efficient feature representations from three aspects: energy efficient, label efficient, and sample efficient.

The post Deep Learning in Image and Video Applications Part 1 appeared first on ML Conference.

The post AI, Machine Learning & Data Science Essentials in Python Part 1 appeared first on ML Conference.

Machine Learning is done in Python, right? All leading ML frameworks, such as TensorFlow and Pytorch, offer their functionality primarily in Python. This state of things is not ideal, however. There are practical problems such as the late discovery of dimensionality errors. More importantly, Python obscures the essentials of Machine Learning: The indirect programming model with graphs is difficult to understand, the focus on numerical arrays prevents generalization, the underlying algorithms are complicated and restrictive. In a powerful functional language such as Haskell, Deep Learning looks completely different: The clunky graph model disappears, as does the restriction on numerical arrays. Instead, the essentials become visible – functions and their properties, optimization and differentiation. Moreover, this approach generates efficient code, even without elaborate C++-based graph machinerie. 

This talk will give an overview of the approach and its way of thinking.

The post Programming Languages for Deep Learning appeared first on ML Conference.

– Time Series Analysis
– Correlation Analysis
– Text Mining
– Anomaly Detection

The post Practical ML with Power BI Desktop appeared first on ML Conference.

The post Feature Engineering and feature Selection Part 1 appeared first on ML Conference.

The post Integrated Reinforcement learning and imitation learning using deep transfer learning Workshop: Part 2 appeared first on ML Conference.