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Episode 2: Becoming a Deep Learning Expert with Deep Narain Singh
Show Notes:
- (1:58) Deep gives a brief background of his career.
- (3:42) Deep explains the discipline of civil engineering.
- (4:24) Deep talks about his first job out of college as a programmer analyst at Cognizant in Pune, India.
- (5:26) Deep then became a senior software engineer at the investment bank Citigroup in Mumbai, India.
- (7:30) Deep discusses his next role as a senior associate for the e-commerce company Sapient in Bangalore, India.
- (8:57) Deep then went to Barclays Investment Bank back in Pune and worked as an analyst, in which he learned a lot about big data technologies.
- (10:07) Deep talks about the diverse Indian cultures as he had experience working in many different regions in India.
- (12:22) Deep recounts the story that propelled him to explore study opportunities in the United States.
- (16:10) Deep gives practical advice to international students who want to pursue a graduate degree in the US.
- (17:53) Deep talks about the Machine Learning and Deep Learning classes he took while at Galvanize.
- (19:27) As a graduate student researcher, Deep built an image captioning system for a startup named FoxType, which helps non-native English speaker write better emails. He goes over the challenges in building this system.
- (24:55) As a deep learning research intern for Netomi.com, Deep built a system with memory using neural network to solve the shortcoming of understanding long-term conversation and context for conversational agent. He discusses the process he went through.
- (27:59) Deep talks about his machine learning project in which he built a sentiment analysis engine using the 300k reviews from Trip Advisor.
- (31:33) Deep talks about another data engineering project in which he built an end-to-end real-time mapping of geo-locations in San Francisco for Uber Price Surge.
- (37:32) Deep recommend the big data tools and technologies that all data scientists should know about.
- (39:27) Deep shares his job search experience and how he landed a role at Nvidia.
- (42:02) Deep provides helpful advice to crack the machine learning interview (aka, learning the fundamentals + networking).
- (45:14) Deep talks about the autonomous vehicle system he’s working on at Nvidia.
- (47:20) Deep quickly goes over Nvidia’s company culture.
- (49:32) Deep digs deep into computer vision problems.
- (51:34) Deep predicts the future applications of deep learning.
- (53:40) Closing segments.
His contact info:
- GitHub
His recommended resources:
- Google Brain
- Facebook AI Research
- OpenAI
- Salesforce Research
- Andrej Karpathy's Blog
- Off the Convex Path
- Nvidia AI Research Blog
- Google AI Blog
- Facebook Research Blog
- Arxiv Sanity Presenter
This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit datacast.substack.com/subscribe
View all episodes
By James LeShow Notes:
- (1:58) Deep gives a brief background of his career.
- (3:42) Deep explains the discipline of civil engineering.
- (4:24) Deep talks about his first job out of college as a programmer analyst at Cognizant in Pune, India.
- (5:26) Deep then became a senior software engineer at the investment bank Citigroup in Mumbai, India.
- (7:30) Deep discusses his next role as a senior associate for the e-commerce company Sapient in Bangalore, India.
- (8:57) Deep then went to Barclays Investment Bank back in Pune and worked as an analyst, in which he learned a lot about big data technologies.
- (10:07) Deep talks about the diverse Indian cultures as he had experience working in many different regions in India.
- (12:22) Deep recounts the story that propelled him to explore study opportunities in the United States.
- (16:10) Deep gives practical advice to international students who want to pursue a graduate degree in the US.
- (17:53) Deep talks about the Machine Learning and Deep Learning classes he took while at Galvanize.
- (19:27) As a graduate student researcher, Deep built an image captioning system for a startup named FoxType, which helps non-native English speaker write better emails. He goes over the challenges in building this system.
- (24:55) As a deep learning research intern for Netomi.com, Deep built a system with memory using neural network to solve the shortcoming of understanding long-term conversation and context for conversational agent. He discusses the process he went through.
- (27:59) Deep talks about his machine learning project in which he built a sentiment analysis engine using the 300k reviews from Trip Advisor.
- (31:33) Deep talks about another data engineering project in which he built an end-to-end real-time mapping of geo-locations in San Francisco for Uber Price Surge.
- (37:32) Deep recommend the big data tools and technologies that all data scientists should know about.
- (39:27) Deep shares his job search experience and how he landed a role at Nvidia.
- (42:02) Deep provides helpful advice to crack the machine learning interview (aka, learning the fundamentals + networking).
- (45:14) Deep talks about the autonomous vehicle system he’s working on at Nvidia.
- (47:20) Deep quickly goes over Nvidia’s company culture.
- (49:32) Deep digs deep into computer vision problems.
- (51:34) Deep predicts the future applications of deep learning.
- (53:40) Closing segments.
His contact info:
- GitHub
His recommended resources:
- Google Brain
- Facebook AI Research
- OpenAI
- Salesforce Research
- Andrej Karpathy's Blog
- Off the Convex Path
- Nvidia AI Research Blog
- Google AI Blog
- Facebook Research Blog
- Arxiv Sanity Presenter
This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit datacast.substack.com/subscribe