Robert Leshner and Jesse Walden: Deep Dive on Decentralization

Jesse Walden, a former a16z investment partner and Mediachain cofounder, and Robert Leshner, founder and CEO of Compound, do a “Deep Dive on Decentralization.” Walden starts with a playbook for progressive decentralization - the process by which crypto project creators build a useful product, create a community around that product, and then gradually hand over control of the maturing network to the community. This process is in keeping with the cooperative model of crypto networks, which drives rapid, compounding innovation through better alignment of incentives and open participation. Leshner follows with a case study of his experiences at Compound, an automated money market for crypto assets in which lenders and borrowers can come together to transact without the involvement of third parties. Compound, one of the first crypto projects to move through the full progressive decentralization model, built a thriving community of third-party application developers, who have set up shop on top of Compound’s smart-contract protocol. The Compound team has gradually brought this community further into the protocol’s inner workings; in the final stages before handoff to the community, the dividend team made changes transparently, with greater reliance on the community’s input, and created a sandbox for experimentation to test governance mechanisms. Decentralizing “allows the protocol to live forever,” Leshner says, which fosters innovation because developers can trust the protocol with their businesses and livelihood. Andreessen Horowitz’s Crypto Startup School brought together 45 participants from around the US and overseas in a seven-week course to learn how to build crypto companies. Andreessen Horowitz is partnering with TechCrunch to release the online version of the course over the next few weeks

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Robert Leshner and Jesse Walden: Deep Dive on Decentralization

Jesse Walden, a former a16z investment partner and Mediachain cofounder, and Robert Leshner, founder and CEO of Compound, do a “Deep Dive on Decentralization.” Walden starts with a playbook for progressive decentralization - the process by which crypto project creators build a useful product, create a community around that product, and then gradually hand over control of the maturing network to the community. This process is in keeping with the cooperative model of crypto networks, which drives rapid, compounding innovation through better alignment of incentives and open participation. Leshner follows with a case study of his experiences at Compound, an automated money market for crypto assets in which lenders and borrowers can come together to transact without the involvement of third parties. Compound, one of the first crypto projects to move through the full progressive decentralization model, built a thriving community of third-party application developers, who have set up shop on top of Compound’s smart-contract protocol. The Compound team has gradually brought this community further into the protocol’s inner workings; in the final stages before handoff to the community, the dividend team made changes transparently, with greater reliance on the community’s input, and created a sandbox for experimentation to test governance mechanisms. Decentralizing “allows the protocol to live forever,” Leshner says, which fosters innovation because developers can trust the protocol with their businesses and livelihood. Andreessen Horowitz’s Crypto Startup School brought together 45 participants from around the US and overseas in a seven-week course to learn how to build crypto companies. Andreessen Horowitz is partnering with TechCrunch to release the online version of the course over the next few weeks

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Top 10 Deep Learning Sessions To Look Forward To At DVDC 2020

The Deep Learning DevCon 2020, DLDC 2020, has exciting talks and sessions around the latest developments in the field of deep learning, that will not only be interesting for professionals of this field but also for the enthusiasts who are willing to make a career in the field of deep learning. The two-day conference scheduled for 29th and 30th October will host paper presentations, tech talks, workshops that will uncover some interesting developments as well as the latest research and advancement of this area. Further to this, with deep learning gaining massive traction, this conference will highlight some fascinating use cases across the world.

Here are ten interesting talks and sessions of DLDC 2020 that one should definitely attend:

Also Read: Why Deep Learning DevCon Comes At The Right Time

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Adversarial Robustness in Deep Learning

By Dipanjan Sarkar

**About: **Adversarial Robustness in Deep Learning is a session presented by Dipanjan Sarkar, a Data Science Lead at Applied Materials, as well as a Google Developer Expert in Machine Learning. In this session, he will focus on the adversarial robustness in the field of deep learning, where he talks about its importance, different types of adversarial attacks, and will showcase some ways to train the neural networks with adversarial realisation. Considering abstract deep learning has brought us tremendous achievements in the fields of computer vision and natural language processing, this talk will be really interesting for people working in this area. With this session, the attendees will have a comprehensive understanding of adversarial perturbations in the field of deep learning and ways to deal with them with common recipes.

Read an interview with Dipanjan Sarkar.

Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER

By Divye Singh

**About: **Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER is a paper presentation by Divye Singh, who has a masters in technology degree in Mathematical Modeling and Simulation and has the interest to research in the field of artificial intelligence, learning-based systems, machine learning, etc. In this paper presentation, he will talk about the common problem of class imbalance in medical diagnosis and anomaly detection, and how the problem can be solved with a deep learning framework. The talk focuses on the paper, where he has proposed a synergistic over-sampling method generating informative synthetic minority class data by filtering the noise from the over-sampled examples. Further, he will also showcase the experimental results on several real-life imbalanced datasets to prove the effectiveness of the proposed method for binary classification problems.

Default Rate Prediction Models for Self-Employment in Korea using Ridge, Random Forest & Deep Neural Network

By Dongsuk Hong

About: This is a paper presentation given by Dongsuk Hong, who is a PhD in Computer Science, and works in the big data centre of Korea Credit Information Services. This talk will introduce the attendees with machine learning and deep learning models for predicting self-employment default rates using credit information. He will talk about the study, where the DNN model is implemented for two purposes — a sub-model for the selection of credit information variables; and works for cascading to the final model that predicts default rates. Hong’s main research area is data analysis of credit information, where she is particularly interested in evaluating the performance of prediction models based on machine learning and deep learning. This talk will be interesting for the deep learning practitioners who are willing to make a career in this field.

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