What is Pruning in ML/AI? - Analytics India Magazine

Pruning as a concept was originally introduced to the field of deep learning by Yann LeCun in an eerie titled paper “Optimal Brain Damage”.

Read more: https://analyticsindiamag.com/what-is-pruning-in-ml-ai/

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AI & Data Science India Salary Study - 2021

The annual Analytics India Salary report presented by AIM and AnalytixLabs is the only annual study in India that delves into salary trends and provides a comprehensive view of the changing landscape of analytics salaries. The report, now in its seventh year, look at the distribution of average salaries across several categories including years of experience, metropolitan regions, industries, education levels, gender, tools, and skills.

The Data Analytics function is experiencing significant growth and development in terms of skills, capabilities, and funding. Last year, despite the pandemic, the Indian start-up industry witnessed $836.3 million investment, almost a 10% (9.7%) increase than the previous year. Also, more than one in five (21%) analytics teams across firms in India witnessed a growth in the last 12 months and the post-pandemic job market saw an upswing of data science jobs. The development of the data science domain is evidenced by the high salaries drawn by analytics professionals across the organization, with Analytics professionals doing relatively well in spite of the pandemic.

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Uber’s Success Is Deeply Tied To Its Success In India: Shirish Andhare

India is currently in a vital phase of its infrastructure, energy, and mobility development, which nicely sets the stage to leapfrog current or existing practices. According to sources, an estimated 40% of its population will be living in urban areas by 2025, and they will account for over 60% of the consumption of resources.

Moreover, transportation in India is highly fragmented, disorganised across modes with poor infrastructure, congestion and low public transport density. Riders and drivers have to undertake multiple challenges daily such as lack of availability, reliability, quality, consistent pricing, safety etc.

To know more about the current space and transportation in India, Analytics India Magazine caught up with Shirish Andhare, Director, Program Management, Uber India and South Asia.

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“Our goal is to change the Indian mindset and help people replace their car with their phone by offering a range of mobility options — whether cars, bikes, autos or public transport — all in the Uber app. By putting more people in fewer cars, we have the potential to build smarter and more liveable cities,” said Andhare.

Using technology, Uber India has been trying to transform the mobility landscape and change how people move around in the country by playing a transformational role in addressing pain points for riders and adding efficiency into the system.

With its multi-modal vision for mobility in India, Uber wants to make a variety of options available to help commuters get where they want to go at a price point that works for them. To that end, Uber has announced partnerships across airports and Metros in Delhi and Hyderabad to provide last-mile connectivity.

Transformation of Uber India

Andhare said that about seven years ago, Uber launched in Bangalore with just three employees. Today, Uber India has tech teams across Bangalore and Hyderabad. It continues its exponential growth journey, focusing on facilitating affordable, reliable and convenient transportation to millions of riders and livelihood opportunities for hundreds of thousands of driver-partners.

The company has doubled its engineering team in India this year. The R&D teams located in Hyderabad and Bangalore continue to grow and currently host over a dozen global charters including rider, maps, customer obsession, infrastructure, money, and eats. These teams are driving global impact for Uber based on several India-first product innovations.

Andhare said, “With over a billion trips in India and South Asia and counting, along with a large driver-partner base, we are focused on winning hearts and minds in the market. We plan to do this by doubling down on products that can solve for low network connectivity, congestion and pollution, as well as enable multiple price points with a varied set of offerings. Uber’s success is deeply tied to our success in India, we are in a strong position in India, and we are committed to serving the market.”

He added, “As we gear up to deliver the next billion rides in the region, we remain focused on providing convenient, affordable rides to millions of riders and stable and sustainable earning opportunities to driver-partners.”

Deep Tech At Uber

Andhare stated that technology provides an incredible opportunity to improve road safety in new and innovative ways before, during and after every ride. At every step, Uber is maximising the usage of technology to bring transparency and accountability through features such as two-way feedback and ratings, telematics and GPS, among others. These will have a positive impact on furthering trust and empathy between riders and driver-partners.

Uber’s Engineering Centre in Bangalore and Hyderabad are engaged in cutting-edge basic and applied technology solutions in areas that include rider growth, driver growth, digital payments, mapping, telematics, vehicle tracking/safety and fleet management, and the Uber core experience.

Some of the India-first innovations include the in-app emergency feature, arrears handling, driver inbound phone support, cash trips, Uber Rentals for longer trips and UberGO. The company is investing heavily in research and resources.

Some of the technologies used at Uber include computer vision, automation, Machine Learning(ML), Optical character recognition (OCR), and Artificial Intelligence (AI) techniques, NLP etc. These technologies are used in areas such as onboarding restaurant menus onto Uber marketplace, enabling earnings opportunities and more. It is also crucial to perform other tasks such as better routing, matching, fraud detection, document processing, maps editing, machine translations, customer support, and more.

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What is Pruning in ML/AI? - Analytics India Magazine

Pruning as a concept was originally introduced to the field of deep learning by Yann LeCun in an eerie titled paper “Optimal Brain Damage”.

Read more: https://analyticsindiamag.com/what-is-pruning-in-ml-ai/

#ml #ai

Measuring Crop Health Using Deep Learning – Notes From Tiger Analytics

Agrochemical companies manufacture a range of offerings for yield maximisation, pest resistance, hardiness, water quality and availability and other challenges facing farmers. These companies need to measure the efficacy of their products in real-world conditions, not just controlled experimental environments. Single-crop farms are divided into plots and a specific intervention performed in each. For example, hybrid seeds are sown in one plot while another is treated with fertilisers, and so on. The relative performance of each treatment is assessed by tracking the plants’ health in the plot where that treatment was administered.

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Learning AI/ML: The Hard Way

The Wave and the Curve

Data science, Artificial Intelligence (AI), and Machine Learning (ML), since last five to six years these phrases have made their places in Gartner’s hype cycle curve. Gradually they have crossed the peak and moving toward the plateau. The curve also has few related terms such as Deep Neural Network, Cognitive AutoML etc. This shows that, there is an emerging technology trend around AI/ML which is going to prevail over the software industry during the coming years. Few of their predecessors such as Business Intelligence, Data Mining and Data Warehousing were there even before these years.

Finding the Crystal Ball in the Jungle

Prediction and forecasting being my favorite topics, I started finding a way to get into this world of data and algorithms back in early 2019. Another driving force for me to learn AI/ML was my fascination on neural networks that was haunting me since I started learning about computer science. I collected few books, learned some python skills to dive into the crystal ball.

While I was going through the online articles, videos and books, I discovered lots of readily available tools, libraries and APIs for AI/ML. It was like someone who is trying to learn cycling and given a car to drive. Due to my interest in neural networks, I got attracted to most the most interesting sub-set of AI/ML, Deep Learning, which deals with deep neural networks. I couldn’t stop myself from directly jumping into Google Tensorflow (a free Google ML tool) and got overwhelmed by a huge collection of its APIs. I could follow the documentation, write code and even made it work. But there was a problem, I was unable understand why I am doing what I am doing. I was completely drowning with the terms like bios, variance, parameters, feature selection, feature scaling, drop out etc. That’s when I took a break, rewind and learn about the internals of AI/ML rather than just using the APIs and Libs blindly. So, I took the hard way.

On one side, I was allured by the readily available smart AI/ML tools and on the other side, my fascination on neural networks was attracting me to learn it from scratch. Meanwhile, I have spent around a month or two just looking for a path to enter the subject. A huge pool of internet resources made me thoroughly confused in identifying the doorway to the heart of puzzle. I realized, why it is a hard nut for people to learn. Janakiram MSV pointed out the reasons correctly in his article.

However, some were very useful, such as an Introduction to Machine Learning by Prof. Grimson from MIT OpenCourseWare. Though its little long but helpful.

#machine learning #ai #artificial intelligence (ai) #ml #ai guide #ai roadmap