Attached are the first fourteen chapters of my book, Machine Learning Yearning. In these chapters, you will learn how to align on ML strategies in a team setting, as well as how to set up development (dev) sets and test sets. Recommendations for how to set up dev/test sets have been changing as Machine Learning is moving toward bigger datasets, and this explains how you should do it for modern ML projects.
I hope you find this useful, and will send more chapters next week!
Andrew Ng Machine Learning 2018
HR function traditionally has been geared to create and implement HR policies aimed at smooth functioning of the business with a long-term perspective. As a result, all its activities around HR administration, training, recruitment and other employee life cycle are mostly designed for steady-state business. With the onset of the digital era and the business dynamics resulting in the need for agility and quick adaptation, HR function needs to redefine its functioning and orientation to business needs. The fundamental premise around customers, customer needs, business model, competition landscape and offerings is undergoing changes in the context of digital transformation and hence HR function too needs to become agile and supportive of the dynamic needs of the business. Building a culture of flexibility and shift in the focus towards delivering solutions has become important in order to be counted as reliable partners to the business.
Just as businesses are recognising that customer experience is central to their success, delivering delightful employee experience has to become the core of HR function. As a partner to business, HR function has to play an important role in propelling the organisation to digital workplace. Use of appropriate digital technology required for its own function should be thought through carefully including the use of digital tools such as AI and robotic process automation to deliver superior employee experience. Learning support has to go beyond the current approach towards learning or content management system.
Artificial boundaries prescribed for learning should cease to exist and static content for learning should be replaced with dynamic and personalised content, based on customised learning paths, encouraging continuous learning by providing access to innumerable learning resource inventories that could be curated on the digital platform from multiple avenues.
With talent continuing to be the key differentiator for businesses, the tasks related to acquiring, grooming and retaining quality talent for growth and sustenance of the business would have to be managed with extreme care as resources would expect personalisation and customised employee journeys during their association with the organisation.
HR heads have to therefore be willing to revisit all dimensions of the traditional HR model and using design thinking approach, visualise the new-age employee journey. What this may lead to is the necessity to relook at systems that may be working in silos to facilitate seamless functioning of individuals and functions.
IT systems in HR have traditionally mirrored the processes followed prior to automation. Most benefits of automation for HR have been on account of reducing labourious work and time in administrative processes such as payroll, compensation and benefits and attendance management. HR function should embrace digital tools empowering HR professionals and the employees and help design customised services as per the preferred mode of access with respect to place and time of their choice.
Organisations can respond to queries of employees with speed and accuracy with the help of AI bots, have the opportunity to proactively address employee concerns and or initiate timely steps regarding motivational issues or likely attrition in critical roles. Employee life cycle and their journeys throughout different phases in the organisation need to be considered as a continuum and an integrated approach is required for serving and supporting employees such that the success of the individual and the business are synchronised. Developing analytical capabilities within HR function and developing useful insights juxtaposed with business parameters would enable HR function to actively contribute to the strategic trajectories of the business.
The insights drawn from analytics could go a long way in deciding effective spend on employee engagement as well as in building linkages with learning and development for maximising performance management. HR function also has an important role to play in fostering innovation in the organisation. Building communities of practice within the organisation to nurture best practices, new ideas and innovation and connect them with the expertise available in academic institutions and other organisations in the ecosystem would be essential for sustaining its competitive advantage in the marketplace.
The writer is CEO, Global Talent Track, a corporate training solutions company
The full article on nature:https://www.nature.com/articles/nature24270Published Oct 2017A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo’s own move selections and also the winner of AlphaGo’s games. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. Starting tabula rasa, our new program AlphaGo Zero achieved superhuman performance, winning 100–0 against the previously published, champion-defeating AlphaGo
Many people confused over the three concepts. In a nutshell, Deep Learning is one kind of machine learning and machine learning is just on kind of Artificial intelligence.
This figure clearly spells out the relationship among these concepts.
three types of machine learning:
- Supervised learning, such as regression and classification
- advertising popularity prediciton
- weather forecasting
- Market forecasting
- estimating life expectancy
- population growth prediction
- identity fraud detection
- image classification
- customer retention
- Unsupervised learning, such as dimensionality reduction and clustering
- dimensionality reduction
- big data visualisation
- meaningful compression
- structure discovery
- feature elicitation
- recommender systerms
- targettd marketing
- customer segment
- Reinforcement learning
- real-time decision
- robot negotiation
- skill acquisiton