Episode 009 - Lesson 4 - Part 2 (Practical Deep Learning for Coders)

Startup Data Science

#Deep Learning #Startup Data Science #Alex Au #Apurva Naik #Edderic Ugaddan #Podcasting Education

1:14:43

Artificial intelligence is evolving at an unprecedented pace—what does that mean for the future of technology, venture capital, business, and even our understanding of ourselves? Award-winning journalist and writer Anil Ananthaswamy joins us for our latest episode to discuss his latest book Why Machines Learn: The Elegant Math Behind Modern AI . Anil helps us explore the journey and many breakthroughs that have propelled machine learning from simple perceptrons to the sophisticated algorithms shaping today’s AI revolution, powering GPT and other models. The discussion aims to demystify some of the underlying mathematical concepts that power modern machine learning, to help everyone grasp this technology impacting our lives–even if your last math class was in high school. Anil walks us through the power of scaling laws, the shift from training to inference optimization, and the debate among AI’s pioneers about the road to AGI—should we be concerned, or are we still missing key pieces of the puzzle? The conversation also delves into AI’s philosophical implications—could understanding how machines learn help us better understand ourselves? And what challenges remain before AI systems can truly operate with agency? If you enjoy this episode, please subscribe and leave us a review on your favorite podcast platform. Sign up for our newsletter at techsurgepodcast.com for exclusive insights and updates on upcoming TechSurge Live Summits. Links: Read Why Machines Learn, Anil’s latest book on the math behind AI https://www.amazon.com/Why-Machines-Learn-Elegant-Behind/dp/0593185749 Learn more about Anil Ananthaswamy’s work and writing https://anilananthaswamy.com/ Watch Anil Ananthaswamy’s TED Talk on AI and intelligence https://www.ted.com/speakers/anil_ananthaswamy Discover the MIT Knight Science Journalism Fellowship that shaped Anil’s AI research https://ksj.mit.edu/ Understand the Perceptron, the foundation of neural networks https://en.wikipedia.org/wiki/Perceptron Read about the Perceptron Convergence Theorem and its significance https://www.nature.com/articles/323533a0…

8年之前 25:50

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Alex is excited about collaborative filtering and he could see using it in his startup to help people unlearn toxic behaviors and beliefs in a productive way. Apurva started working remotely; she found it hard to stay motivated to study. She has issues with collaborative filtering in Netflix; she feels like Netflix's recommendation algorithm is not good for discovering new things because she thinks the recommendations tend to be similar to the past. Edderic's been busy with work at Lingo Live. Edderic enjoys the part of the video lesson where Jeremy destroys the movie data set recommender benchmark seamlessly with a Neural Network.

9集单集

Episode 009 - Lesson 4 - Part 2 (Practical Deep Learning for Coders)

Startup Data Science

82 subscribers

published 8年之前

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9集单集

#Deep Learning #Startup Data Science #Alex Au #Apurva Naik #Edderic Ugaddan #Podcasting Education

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Episode 010 - Lesson 5 - Part 1 (Practical Deep Learning for Coders) 31:35

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Alex gives a quick recap of Lesson 5, using embeddings with imdb review data to categorize movies into clusters using Natural Language Processing (NLP). Edderic, Apurva, and Alex discuss what they're excited about with using NLP and also speak to their motivation as they continue to learn deep learning.…

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Episode 009 - Lesson 4 - Part 2 (Practical Deep Learning for Coders) 25:50

8 years之前25:50

25:50

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Episode 008 - Lesson 4 - Part 1 (Practical Deep Learning for Coders) 31:26

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31:26

Apurva loved Jeremy's presentation using Excel to show how calculations are being made; it was a great confidence-building exercise for her to replicate it in Excel. Edderic's excited about Jeremy's claim that Convolutional Neural Networks are doing well in Speech Recognition. There are tons of machine learning algorithms out there; he thinks it would be nice to have just one super algorithm/architecture to rule them all. Alex explains his idea of convolution through an analogy.…

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Episode 007 - Lesson 3 - Part 2 (Practical Deep Learning for Coders) 42:40

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Alex thinks dropout is cool. He's still not quite sure what batch normalization is. Regarding ImageNet competition, Apurva, along with offering tips to staying motivated to learning says that instead of creating "new" models, people are only doing ensembling now to get a marginal edge over everyone else. Edderic announces revamping his PC workstation for deep learning (bye-bye Amazon!)…

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Episode 006 - Lesson 3 - Part 1 (Practical Deep Learning for Coders) 32:36

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