1 Bridge over Troubled Water - Simon & Garfunkel
2 (They Long to Be) Close to You - The Carpenters
3 American Woman - The Guess Who
4 Raindrops Keep Fallin' on My Head - B.J. Thomas
5 War - Edwin Starr
6 Ain't No Mountain High Enough - Diana Ross
7 I'll Be There - The Jackson 5
8 Get Ready - Rare Earth
9 Let It Be - The Beatles
10 Band of Gold - Freda Payne
11 Mama Told Me (Not to Come) - Three Dog Night
12 Everything Is Beautiful - Ray Stevens
13 Make It with You - Bread
14 Hitchin' a Ride - Vanity Fare
15 ABC - The Jackson 5
16 The Love You Save - The Jackson 5
17 Cracklin' Rosie - Neil Diamond
18 Candida - Dawn
19 Thank You (Falettinme Be Mice Elf Agin) - Sly & the Family Stone
20 Spill the Wine - Eric Burdon & War
21 O-o-h Child - Five Stairsteps
22 Spirit in the Sky - Norman Greenbaum
23 Lay Down (Candles in the Rain) - Melanie
24 Ball of Confusion (That's What the World Is Today) - The Temptations
25 Love on a Two-Way Street - The Moments
26 Which Way You Goin' Billy? - The Poppy Family
27 All Right Now - Free
28 I Want You Back - The Jackson 5
29 Julie (Do Ya Love Me) - Bobby Sherman
30 Green-Eyed Lady - Sugarloaf

Direct Preference Optimization (DPO) is a method used for training Large Language Models (LLMs). DPO is a direct way to train the LLM without the need for reinforcement learning, which makes it more effective and more efficient.
Learn about it in this simple video!

This is the third one in a series of 4 videos dedicated to the reinforcement learning methods used for training LLMs.

Full Playlist: https://www.youtube.com/playli....st?list=PLs8w1Cdi-zv

Video 0 (Optional): Introduction to deep reinforcement learning https://www.youtube.com/watch?v=SgC6AZss478
Video 1: Proximal Policy Optimization https://www.youtube.com/watch?v=TjHH_--7l8g
Video 2: Reinforcement Learning with Human Feedback https://www.youtube.com/watch?v=Z_JUqJBpVOk
Video 3 (This one!): Deterministic Policy Optimization

00:00 Introduction
01:08 RLHF vs DPO
07:19 The Bradley-Terry Model
11:25 KL Divergence
16:32 The Loss Function
14:36 Conclusion

Get the Grokking Machine Learning book!
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Build your first AI agent without coding, check out Lindy: https://go.lindy.ai/ishan

In the past few months, the internet has been flooded with articles and videos about AI agents, highlighting how they simplify people's lives, maximize productivity, and even generate income.

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CHAPTERS:-
00:00 - Introduction
01:34 - Lindy interface
02:06 - Flow editor
09:51 - AI agent
11:38 - Examples of AI agents
15:49 - Conclusion

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Boris Cherny is the creator and head of Claude Code at Anthropic. What began as a simple terminal-based prototype just a year ago has transformed the role of software engineering and is increasingly transforming all professional work.

*We discuss:*
1. How Claude Code grew from a quick hack to 4% of public GitHub commits, with daily active users doubling last month
2. The counterintuitive product principles that drove Claude Code’s success
3. Why Boris believes coding is “solved”
4. The latent demand that shaped Claude Code and Cowork
5. Practical tips for getting the most out of Claude Code and Cowork
6. How underfunding teams and giving them unlimited tokens leads to better AI products
7. Why Boris briefly left Anthropic for Cursor, then returned after just two weeks
8. Three principles Boris shares with every new team member

*Brought to you by:*
DX—The developer intelligence platform designed by leading researchers: https://getdx.com/lenny
Sentry—Code breaks, fix it faster: https://sentry.io/lenny
Metaview—The AI platform for recruiting: https://metaview.ai/lenny

*Episode transcript:* https://www.lennysnewsletter.c....om/p/head-of-claude-

*Archive of all Lenny's Podcast transcripts:*
https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0

*Where to find Boris Cherny:*
• X: https://x.com/bcherny
• LinkedIn: https://www.linkedin.com/in/bcherny
• Website: https://borischerny.com

*Where to find Lenny:*
• Newsletter: https://www.lennysnewsletter.com
• X: https://twitter.com/lennysan
• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/

*In this episode, we cover:*
(00:00) Introduction to Boris and Claude Code
(03:45) Why Boris briefly left Anthropic for Cursor (and what brought him back)
(05:35) One year of Claude Code
(08:41) The origin story of Claude Code
(13:29) How fast AI is transforming software development
(15:01) The importance of experimentation in AI innovation
(16:17) Boris’s current coding workflow (100% AI-written)
(17:32) The next frontier
(22:24) The downside of rapid innovation
(24:02) Principles for the Claude Code team
(26:48) Why you should give engineers unlimited tokens
(27:55) Will coding skills still matter in the future?
(32:15) The printing press analogy for AI’s impact
(36:01) Which roles will AI transform next?
(40:41) Tips for succeeding in the AI era
(44:37) Poll: Which roles are enjoying their jobs more with AI
(46:32) The principle of latent demand in product development
(51:53) How Cowork was built in just 10 days
(54:04) The three layers of AI safety at Anthropic
(59:35) Anxiety when AI agents aren’t working
(01:02:25) Boris’s Ukrainian roots
(01:03:21) Advice for building AI products
(01:08:38) Pro tips for using Claude Code effectively
(01:11:16) Thoughts on Codex
(01:12:13) Boris’s post-AGI plans
(01:14:02) Lightning round and final thoughts

*Referenced:*
• Cursor: https://cursor.com
• The rise of Cursor: The $300M ARR AI tool that engineers can’t stop using | Michael Truell (co-founder and CEO): https://www.lennysnewsletter.com/p/the-rise-of-cursor-michael-truell
• Anthropic: https://www.anthropic.com
• Anthropic’s CPO on what comes next | Mike Krieger (co-founder of Instagram): https://www.lennysnewsletter.com/p/anthropics-cpo-heres-what-comes-next
• Claude Code Is the Inflection Point: https://newsletter.semianalysi....s.com/p/claude-code-
• Spotify says its best developers haven’t written a line of code since December, thanks to AI: https://techcrunch.com/2026/02..../12/spotify-says-its
• Anthropic co-founder on quitting OpenAI, AGI predictions, $100M talent wars, 20% unemployment, and the nightmare scenarios keeping him up at night | Ben Mann: https://www.lennysnewsletter.com/p/anthropic-co-founder-benjamin-mann
• Haiku: https://www.anthropic.com/claude/haiku
• Sonnet: https://www.anthropic.com/claude/sonnet
• Opus: https://www.anthropic.com/claude/opus
• Jenny Wen on X: https://x.com/jenny_wen
• Johannes Gutenberg: https://en.wikipedia.org/wiki/Johannes_Gutenberg
• Anthropic jobs: https://www.anthropic.com/careers/jobs
• Lenny’s AI poll post on X: https://x.com/lennysan/status/2020266745722991051
• Fiona Fung on LinkedIn: https://www.linkedin.com/in/fionafung
• Brandon Kurkela on LinkedIn: https://www.linkedin.com/in/bkurkela
• Cowork: https://www.anthropic.com/webinars/future-of-ai-at-work-introducing-cowork
• Chris Olah on X: https://x.com/ch402
• The Bitter Lesson: http://www.incompleteideas.net..../IncIdeas/BitterLess
...References continued at: https://www.lennysnewsletter.c....om/p/head-of-claude-

_Production and marketing by https://penname.co/._
_For inquiries about sponsoring the podcast, email [email protected]._

Lenny may be an investor in the companies discussed.

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Our previous discussions of deep net applications were limited to static patterns, but how can a net decipher and label patterns that change with time? For example, could a net be used to scan traffic footage and immediately flag a collision? Through the use of a recurrent net, these real-time interactions are now possible.

Deep Learning TV on
Facebook: https://www.facebook.com/DeepLearningTV/
Twitter: https://twitter.com/deeplearningtv

The Recurrent Neural Net (RNN) is the brainchild of Juergen Schmidhuber and Sepp Hochreiter. The three deep nets we’ve seen thus far – MLP, DBN, and CNN – are known as feedforward networks since a signal moves in only one direction across the layers. In contrast, RNNs have a feedback loop where the net’s output is fed back into the net along with the next input. Since RNNs have just one layer of neurons, they are structurally one of the simplest types of nets.

Like other nets, RNNs receive an input and produce an output. Unlike other nets, the inputs and outputs can come in a sequence. Here are some sample applications for different input-output scenarios:
- Single input, sequence of outputs: image captioning
- Sequence of inputs, single output: document classification
- Sequence of inputs, sequence of outputs: video processing by frame, statistical forecasting of demand in Supply Chain Planning

Have you ever used an RNN in one of your projects before? If so, please comment and tell us about your experience.

RNNs are trained using backpropagation through time, which reintroduces the vanishing gradient problem. In fact, the problem is worse with an RNN because each time step is the equivalent of a layer in a feedforward net. Thus if the net is trained for 1000 time steps, the gradient will vanish exponentially as it would in a 1000-layer MLP.

There are different approaches to address this problem, the most popular of which is gating. Gating takes the output of any time step and the next input, and performs a transformation before feeding the result back into the RNN. There are several types of gates, the LSTM being the most popular. Other approaches to address this problem include gradient clipping, steeper gates, and better optimizers.

GPUs are an essential tool for training an RNN. A team at Indico compared the speed boost from using a GPU over a CPU, and found a 250-fold increase. That’s the difference between 1 day and over 8 months!

A recurrent net has one additional capability – it can predict the next item in a sequence, essentially acting as a forecasting engine.

Credits
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelan....cers/~0147b8991909b2
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Marek Scibior (Prezi creator, Illustrator) -
http://brawuroweprezentacje.pl/
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

Dawn Song is well-known for her research on the intersection of deep learning and security. Aside from her research, Song is also a Professor in the Department of Electrical Engineering and Computer Science at UC Berkeley and CEO of Oasis Labs, a blockchain startup that is creating a privacy-first cloud computing platform no blockchain. She has received several awards for her work including the MacArthur Fellowship, the Guggenheim Fellowship, and Best Paper awards from top conferences.

Andrew sits down with Song to chat about her unconventional career path and her current research projects.

Here’s what you’ll learn in the interview:

00:34: How Song first got started in deep learning and security
4:00: How Song self-designed a reading program structured around representational learning
13:22: How computer security can help deep learning
17:03: Song’s research on how to build resilient machine learning systems
21:55 How a “consistency check” approach can defend against attacks
25:31: Song’s work in AI and data privacy
27:49: How deep learning can help computer security
30:16: How Song’s startup, Oasis Labs, is creating privacy-preserving smart contracts
34:42: Song’s advice for learners breaking into a new field


Want to build your own career in deep learning? Get started by taking the Deep Learning Specialization.

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In this Ethical Hacking Full Course, will help you learn all about ethical hacking concepts. This ethical hacker tutorial will acquaint you with the importance of ethical hacking, what is ethical hacking, and types of cyberattacks with a hands-on demo for each; you will also learn how to become an ethical hacker. We cover the different types of hackers like white, black and grey hat hackers, and their roles in cybersecurity. We also have a look at an introduction to cryptography along with some important encryption algorithms in the field of network security.

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