The Fifth Elephant 2015
2015-07-16..2015-07-18
Bangalore, India
[The Fifth Elephant 2013, The Fifth Elephant 2015, The Fifth Elephant 2016, The Fifth Elephant 2018, The Fifth Elephant 2019, The Fifth Elephant 2020, The Fifth Elephant 2023 Monsoon, The Fifth Elephant 2025 Winter Edition, The Fifth Elephant Pune edition 2026]
| title | author | length |
|---|---|---|
| Keeping Moore's law alive: Neuromorphic computing | Anand Chandrasekaran | 43m |
Claim: custom neuromorphic silicon buys orders-of-magnitude power savings versus GPU simulation, aiming to keep Moore’s-law gains by changing architecture not… | ||
| Deep Learning for Natural Language Processing | Devashish Shankar | 45m |
Demonstrates using CNN with word embeddings to classify short texts, replacing heavy feature engineering and achieving large gains: reported jump from 85% to… | ||
| The many ways of parallel computing with Julia | Viral B Shah | 47m |
Overview lists concrete primitives and demos: pmap @parallel remotecall spawn fetch plus shared and distributed arrays, ScaLapack and MPI work are shown… | ||
| Towards “Thinking Machines” | Shailesh Kumar | 01h 01m |
Ever since the dawn of civilization humans have been trying to “extend” themselves through “machines” - tools to hunt animals stronger than us, telescopes to… | ||
| Visualising Multi Dimensional Data | Amit Kapoor | 34m |
Demonstrates concrete techniques: a five-step pipeline for creating charts, ggplot2/d3 style grammar of graphics implementations, and working code on a GitHub… | ||
| Building a E commerce search engine: Challenges, insights and approaches | Vinodh Kumar | 37m |
In this talk, we go into the details of what goes behind the scenes in building a state-of-the-art E-Commerce search engine. Over the last decade, there is a… | ||
| Recommendation System beyond traditional Collaborative filtering | Gagandeep Juneja | 43m |
Though Collaborative filtering works quite well for companies like NetFlix but here in Snapdeal we are catering 12M huge product catalog and more than 100… | ||
| Two Years Wiser: The Nilenso Experiment | Steven Deobald | 01h 00m |
nilenso claims a workers cooperative can run real client work and survive two years of experiments; concrete artifacts include a one‑month Rails product wiken… | ||
| Using Modes for Time Series Classification | Rohit Chatterjee | 19m |
Uses the sample mode as the primary statistic and builds a compact signature from mode strength, run ratio and GT ratio after optionally applying one order of… | ||
| Hardware Accelerated Big Data Processing | Reetinder Sidhu | 17m |
Conference demonstrates concrete use of FPGAs for streaming big data with measurable results: a PCIe FPGA board implementation of FPGA-accelerated XML schema… | ||
| Escher: democratizing beautiful visualizations | Shashi Gowda | 28m |
Escher.jl is a Julia library for building web user interfaces that combine plots, infographics, rich text and TeX-style layouts with live updates as underlying… | ||
| Harnessing the power of the Erlang VM | Abhijit Pratap Singh | 16m |
Ports of Ruby APIs to Elixir reduce instance count dramatically and exploit the Erlang VM for concurrency and low latency, claiming dropped from ~10–11 Ruby… | ||
| Building tiered data stores using Aesop to bridge SQL and NoSQL systems | Regunath Balasubramanian | 41m |
Large scale internet systems often use a combination of relational (SQL) and non-relational (NoSQL) data stores. Contrary to product claims, it is hard to find… | ||
| Processing large data with Apache Spark | Venkata Naga Ravi | 44m |
Claims concrete speedups and real deployments: Apache Spark emphasizes in‑memory caching and shows 100x in-memory speed over MapReduce in the slides, with a… | ||
| Building Recommender system | Swaroop Krothapalli | 20m |
Recommender Systems solves matrix sparsity problem. And this idea of predicting sparse values can be applied for various problems across domains. I have used… | ||
| Call me maybe Jepsen and flaky networks | Shalin Shekhar Mangar | 41m |
In the big data world, our data stores communicate over an asynchronous, unreliable network to provide a facade of consistency. However, to really understand… | ||
| Deploying Batch and Streaming Architectures on AWS | Russell Nash | 39m |
Shows how Amazon EMR, Amazon Kinesis, Amazon Redshift and AWS Lambda combine into a Lambda architecture with concrete numbers: Kinesis shard = 1,000… | ||
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