Nowadays industries operating with big facts have recognized the price of the ML getting to know the era. Once you cross deeper, greater expertise of like linear algebra can be needed. So, the preceding programming enjoy is beneficial but it isn't always a mandate.

Machine learning sure is an umbrella word for many methodologies and tools but one must be clear about the fact that it is not an umbrella word for all the solutions. No one can deny that machine learning has revolutionised the way data can be squeezed in for discoveries. What one should care about is that the enhancement of any technology also depends on a relentless introspective approach in attacking the shortcomings. The rise in popularity sure lures every amateur into believing that they have reached their destination. With tools and frameworks being open-sourced, everyone can play with data, experiment with MNIST datasets and get really good accuracy scores.

For many people, speaking off the cuff to a large audience does not come easily. But without professional feedback, rehearsing speeches and presentations can be a tough process. A psychologist, a management scientist and an IT specialist have developed an online training tool that uses artificial intelligence to evaluate users' speaking skills and personal characteristics. The team has now established the start-up Retorio at the Technical University of Munich (TUM) to launch the software on the market. It's a scenario many people can relate to – standing all alone in front of an audience, clutching a microphone with clammy hands and finding one's mouth has gone dry. Whether it's a job interview or a wedding speech: for many people, the idea of speaking in public is associated with anxiety and uncertainty.

Recent years have witnessed a widespread increase of interest in network representation learning (NRL). By far most research efforts have focused on NRL for homogeneous networks like social networks where vertices are of the same type, or heterogeneous networks like knowledge graphs where vertices (and/or edges) are of different types. There has been relatively little research dedicated to NRL for bipartite networks. Arguably, generic network embedding methods like node2vec and LINE can also be applied to learn vertex embeddings for bipartite networks by ignoring the vertex type information. However, these methods are suboptimal in doing so, since real-world bipartite networks concern the relationship between two types of entities, which usually exhibit different properties and patterns from other types of network data. For example, E-Commerce recommender systems need to capture the collaborative filtering patterns between customers and products, and search engines need to consider the matching signals between queries and webpages. This work addresses the research gap of learning vertex representations for bipartite networks. We present a new solution BiNE, short for Bipartite Network Embedding}, which accounts for two special properties of bipartite networks: long-tail distribution of vertex degrees and implicit connectivity relations between vertices of the same type. Technically speaking, we make three contributions: (1) We design a biased random walk generator to generate vertex sequences that preserve the long-tail distribution of vertices; (2) We propose a new optimization framework by simultaneously modeling the explicit relations (i.e., observed links) and implicit relations (i.e., unobserved but transitive links); (3) We explore the theoretical foundations of BiNE to shed light on how it works, proving that BiNE can be interpreted as factorizing multiple matrices.

To investigate the detection of students' behavioral engagement (On-Task vs. Off-Task), we propose a two-phase approach in this study. In Phase 1, contextual logs (URLs) are utilized to assess active usage of the content platform. If there is active use, the appearance information is utilized in Phase 2 to infer behavioral engagement. Incorporating the contextual information improved the overall F1-scores from 0.77 to 0.82. Our cross-classroom and cross-platform experiments showed the proposed generic and multi-modal behavioral engagement models' applicability to a different set of students or different subject areas.

In this work, we investigate the feasibility and effectiveness of employing deep learning algorithms for automatic recognition of the modulation type of received wireless communication signals from subsampled data. Recent work considered a GNU radio-based data set that mimics the imperfections in a real wireless channel and uses 10 different modulation types. A Convolutional Neural Network (CNN) architecture was then developed and shown to achieve performance that exceeds that of expert-based approaches. Here, we continue this line of work and investigate deep neural network architectures that deliver high classification accuracy. We identify three architectures - namely, a Convolutional Long Short-term Deep Neural Network (CLDNN), a Long Short-Term Memory neural network (LSTM), and a deep Residual Network (ResNet) - that lead to typical classification accuracy values around 90% at high SNR. We then study algorithms to reduce the training time by minimizing the size of the training data set, while incurring a minimal loss in classification accuracy. To this end, we demonstrate the performance of Principal Component Analysis in significantly reducing the training time, while maintaining good performance at low SNR. We also investigate subsampling techniques that further reduce the training time, and pave the way for online classification at high SNR. Finally, we identify representative SNR values for training each of the candidate architectures, and consequently, realize drastic reductions of the training time, with negligible loss in classification accuracy.

We propose a multimodal approach for detection of students' behavioral engagement states (i.e., On-Task vs. Off-Task), based on three unobtrusive modalities: Appearance, Context-Performance, and Mouse. Final behavioral engagement states are achieved by fusing modality-specific classifiers at the decision level. Various experiments were conducted on a student dataset collected in an authentic classroom.

This video is a part of a free online course that provides introduction to practical deep learning methods using MATLAB. In addition to short engaging videos, the course also contains interactive, in-browser MATLAB projects. For a 14-hour comprehensive course covering the theory and practice of deep learning using real-world image and sequence data, see: http://bit.ly/2DjaTdh

As we see artificial intelligence impacting the real world, it's no longer a niche computer science, technical field. Policymakers, business leaders, educators, social scientists--they all need to take part and guide the future of A.I. Also, as a technical field, A.I. thoroughly lacks diversity. It lacks women and underrepresented minorities. We're committed to diversity, especially starting with high school students. It's unthinkable that such an important technology that will influence humanity has such an imbalance in terms of the representation of people taking part. A.I. doesn't belong to a niche group of people.

It can be quite hard to find a specific dataset to use for a variety of machine learning problems or to even experiment on. The list below does not only contain great datasets for experimentation but also contains a description, usage examples and in some cases the algorithm code to solve the machine learning problem associated with that dataset. This is one of my favourite dataset locations. Each dataset is a small community where you can have a discussion about data, find some public code or create your own projects in Kernels. They contain a numerous amount of real-life datasets of all shapes and sizes and in many different formats. You can also see "Kernels" associated with each dataset where many different data scientists have provided notebooks to analyze the dataset.