Our post on the 100,000 registered customers milestone this summer included an infographic of sample use cases being explored by BigML users, which naturally span many different sectors and industries. Today, we'd like to start a series of posts that further highlight a subset of those business problems to give our readers some clues on how a comprehensive platform as ours can be utilized in different business contexts in case they're considering new Machine Learning solutions. There are many ways to organize use cases, e.g., by industry, function, geography. In this post, we will focus on startups and SMBs as we give you a glimpse of the motivation behind solving each reference use case. Startups and SMBs have good reasons to prefer the BigML platform because it lets them to affordably step into Machine Learning with ample room to further scale efforts as data volumes and the number of use cases implemented grow over time.

While optimistic about the future, Steve Wozniak is not ready to turn over his identity (nor his Tesla) to artificial intelligence anytime soon. At a conference in Budapest I attended, he referenced deleting his Facebook account because of privacy concerns, and that he no longer believes that a totally autonomous car will happen in his lifetime. But Wozniak retains the passion and enthusiasm for technology and innovation that made him a household name as Apple's co-founder. When he and Steve Jobs started Apple, they were trying to develop a new kind of computer that would improve the user experience beyond what was available at the time. Today, "The Woz" is a brilliant engineer, who keeps his eye on what is happening in technology, digital transformation and entrepreneurship.

Humanity's rich history has left behind an enormous number of historical documents and artifacts. However, virtually none of these documents, containing stories and recorded experiences essential to our cultural heritage, can be understood by non-experts due to language and writing changes over time. For instance, archaeologist have unearthed tens of thousands of clay tablets from ancient Babylon [1], yet only a few hundred specially trained scholars can translate them. The vast majority of these documents have never been read, even if they were uncovered in the 1800s. To give a further illustration of the challenge posed by this scale, a tablet from the Tale of Gilgamesh was collected in an expedition in 1851, but its significance was not brought to light until 1872.

Deep reinforcement learning has been shown to solve challenging tasks where large amounts of training experience is available, usually obtained online while learning the task. Robotics is a significant potential application domain for many of these algorithms, but generating robot experience in the real world is expensive, especially when each task requires a lengthy online training procedure. Off-policy algorithms can in principle learn arbitrary tasks from a diverse enough fixed dataset. In this work, we evaluate popular exploration methods by generating robotics datasets for the purpose of learning to solve tasks completely offline without any further interaction in the real world. We present results on three popular continuous control tasks in simulation, as well as continuous control of a high-dimensional real robot arm. Code documenting all algorithms, experiments, and hyper-parameters is available at https://github.com/qutrobotlearning/batchlearning.

Graph representation learning is to learn universal node representations that preserve both node attributes and structural information. The derived node representations can be used to serve various downstream tasks, such as node classification and node clustering. When a graph is heterogeneous, the problem becomes more challenging than the homogeneous graph node learning problem. Inspired by the emerging information theoretic-based learning algorithm, in this paper we propose an unsupervised graph neural network Heterogeneous Deep Graph Infomax (HDGI) for heterogeneous graph representation learning. We use the meta-path structure to analyze the connections involving semantics in heterogeneous graphs and utilize graph convolution module and semantic-level attention mechanism to capture local representations. By maximizing local-global mutual information, HDGI effectively learns high-level node representations that can be utilized in downstream graph-related tasks. Experiment results show that HDGI remarkably outperforms state-of-the-art unsupervised graph representation learning methods on both classification and clustering tasks. By feeding the learned representations into a parametric model, such as logistic regression, we even achieve comparable performance in node classification tasks when comparing with state-of-the-art supervised end-to-end GNN models.

Probably the most important problem in machine learning is the preliminary biasing of a learner's hypothesis space so that it is small enough to ensure good generalisation from reasonable training sets, yet large enough that it contains a good solution to the problem being learnt. In this paper a mechanism for {\em automatically} learning or biasing the learner's hypothesis space is introduced. It works by first learning an appropriate {\em internal representation} for a learning environment and then using that representation to bias the learner's hypothesis space for the learning of future tasks drawn from the same environment. An internal representation must be learnt by sampling from {\em many similar tasks}, not just a single task as occurs in ordinary machine learning. It is proved that the number of examples $m$ {\em per task} required to ensure good generalisation from a representation learner obeys $m = O(a+b/n)$ where $n$ is the number of tasks being learnt and $a$ and $b$ are constants. If the tasks are learnt independently ({\em i.e.} without a common representation) then $m=O(a+b)$. It is argued that for learning environments such as speech and character recognition $b\gg a$ and hence representation learning in these environments can potentially yield a drastic reduction in the number of examples required per task. It is also proved that if $n = O(b)$ (with $m=O(a+b/n)$) then the representation learnt will be good for learning novel tasks from the same environment, and that the number of examples required to generalise well on a novel task will be reduced to $O(a)$ (as opposed to $O(a+b)$ if no representation is used). It is shown that gradient descent can be used to train neural network representations and experiment results are reported providing strong qualitative support for the theoretical results.

Multi-label learning studies the problem where an instance is associated with a set of labels. By treating single-label learning problem as one task, the multi-label learning problem can be casted as solving multiple related tasks simultaneously. In this paper, we propose a novel Multi-task Gradient Descent (MGD) algorithm to solve a group of related tasks simultaneously. In the proposed algorithm, each task minimizes its individual cost function using reformative gradient descent, where the relations among the tasks are facilitated through effectively transferring model parameter values across multiple tasks. Theoretical analysis shows that the proposed algorithm is convergent with a proper transfer mechanism. Compared with the existing approaches, MGD is easy to implement, has less requirement on the training model, can achieve seamless asymmetric transformation such that negative transfer is mitigated, and can benefit from parallel computing when the number of tasks is large. The competitive experimental results on multi-label learning datasets validate the effectiveness of the proposed algorithm.

In India, healthcare is an issue not because it's hard for people of lower economic status to afford. Often times, it's hard to even find a doctor or hospital in a remote rural area. That's why this particular piece of new technology could be incalculably valuable. Shivanshu Mathur and Raghav Jain, two students pursuing their BTech in Computer Science Engineering at Lovely Professional University, have won the second prize at the NEC India Hackathon 2019 organized by HackerEarth. They received a cash prize of Rs. 1.5 lakh for developing a software they call Medikare.

As early as 1997, McKinsey coined the concept "war for talent" and identified it as a pressing challenge facing workplaces. To be sure, the war for talent has only intensified in recent years. The supply of top-tier artificial intelligence (AI) talent is in short supply. And, with the likes of Facebook and Google vying for top-notch talent, recruiting efforts can prove incredibly challenging. Fortunately, by embracing some key strategies, companies can effectively compete with even today's most sought-after employers.

This repository contains my personal notes and summaries on DeepLearning.ai I've enjoyed every little bit of the course hope you enjoy my notes too. This is by far the best course series on deep learning that I've taken. If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech.