Maxime Labonne: LLM Scientist Roadmap, AI Scientist, LLM Course & Open Source - AI Portfolio Podcast

AI Portfolio Podcast

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הוסף לפני thirty-eight שבועות

תוכן מסופק על ידי Mark Moyou, PhD and Mark Moyou. כל תוכן הפודקאסטים כולל פרקים, גרפיקה ותיאורי פודקאסטים מועלים ומסופקים ישירות על ידי Mark Moyou, PhD and Mark Moyou או שותף פלטפורמת הפודקאסט שלהם. אם אתה מאמין שמישהו משתמש ביצירה שלך המוגנת בזכויות יוצרים ללא רשותך, אתה יכול לעקוב אחר התהליך המתואר כאן https://he.player.fm/legal.

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לפני שנה 1:27:24

MP3•בית הפרקים

📌 00:00 – Intro
📚 01:51 – Maxime: Books & Courses
🤖 07:30 – AI Scientist vs. AI Engineer
🚀 09:05 – Path to Becoming an AI Expert
🎓 11:13 – Do You Need a Degree?
⏳ 13:01 – How Long Does It Take to Become an AI Scientist?
👨‍🔬 15:58 – Individual Contributor Role as an LLM Scientist
🧠 26:04 – Understanding LLM Personality
🎯 30:07 – Objective Functions in LLMs
🆕 34:08 – Emerging AI Models
📄 40:51 – How to Read Research Papers Effectively
🗺 45:26 – Roadmap to Becoming an LLM Scientist (Key Section ⭐)
🔎 54:15 – Deep Dive: Understanding the Attention Mechanism
🖼 01:15:33 – Roadmap for a Multi-Modal AI Scientist
📊 01:17:12 – Optimizing Your AI Career Path
💼 01:19:38 – AI Job Market Insights
📖 01:21:50 – Must-Read Books for AI Enthusiasts
💡 01:22:41 – Expert Career Advice
⚡ 01:24:07 – Rapid-Fire Round

22 פרקים

#Business #Mark Moyou, PhD #Mark Moyou

Maxime Labonne: LLM Scientist Roadmap, AI Scientist, LLM Course & Open Source - AI Portfolio Podcast

AI Portfolio Podcast

published לפני שנה

שתפו

MP3•בית הפרקים

22 פרקים

#Business #Mark Moyou, PhD #Mark Moyou

כל הפרקים

1
Umar Jamil: AI Research and Models, Learning How to Learn & Technical Content - AI Portfolio Podcast 1:30:39

לפני 8 weeks1:30:39

1:30:39

Machine Learning Youtuber Umar Jamil, His channel has some of the top technical explanations on technical topics related to Gen AI with over 50k followers. Follow/Connect: Umar LinkedIn: https://www.linkedin.com/in/ujamil/ Mark Linkedin: https://www.linkedin.com/in/markmoyou/ Chapters: 00:00 Intro 01:54 Umar's YouTube Channel 06:10 Umar's Data Science Journey 11:30 Show Your Work 16:35 Pre-Training 20:19 Research Without a PhD 22:22 Lessons Learned as a Research Scientist 23:41 Advice for Technical Creators 25:30 Building 30:17 Hardest Challenges 43:58 Research Papers 49:41 How Language Models Learn 50:48 Coding IDEs 53:19 Choosing a Model 54:50 AI Coding Editors 55:55 Career Optimization Function 57:09 Changing Jobs and Approaching the Job Market 01:00:30 Book Recommendations 01:02:37 Why We Struggle to Finish Things 01:05:15 Career Advice 01:07:26 Coding the Transformer 01:15:27 CLIP Models 01:17:06 Diffusion Models 01:18:22 Autoencoder 01:20:23 Segment Anything Model 01:21:30 Direct Preference Optimization 01:23:30 Multimodal Models 01:26:09 Test-Time Compute 01:27:05 Rapid Round…

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Maxime Labonne: LLM Scientist Roadmap, AI Scientist, LLM Course & Open Source - AI Portfolio Podcast 1:27:24

לפני 10 weeks1:27:24

1:27:24

Maxime Labonne, Co-author of the LLM Engineers Handbook, creator of the LLM course on github with over 40k stars, and author of Hands on Graph Neural Networks. Follow/Connect: Maxime LinkedIn:https://www.linkedin.com/in/maxime-labonne/ Mark Linkedin: https://www.linkedin.com/in/markmoyou/ Chapters: 📌 00:00 – Intro 📚 01:51 – Maxime: Books & Courses 🤖 07:30 – AI Scientist vs. AI Engineer 🚀 09:05 – Path to Becoming an AI Expert 🎓 11:13 – Do You Need a Degree? ⏳ 13:01 – How Long Does It Take to Become an AI Scientist? 👨‍🔬 15:58 – Individual Contributor Role as an LLM Scientist 🧠 26:04 – Understanding LLM Personality 🎯 30:07 – Objective Functions in LLMs 🆕 34:08 – Emerging AI Models 📄 40:51 – How to Read Research Papers Effectively 🗺 45:26 – Roadmap to Becoming an LLM Scientist (Key Section ⭐) 🔎 54:15 – Deep Dive: Understanding the Attention Mechanism 🖼 01:15:33 – Roadmap for a Multi-Modal AI Scientist 📊 01:17:12 – Optimizing Your AI Career Path 💼 01:19:38 – AI Job Market Insights 📖 01:21:50 – Must-Read Books for AI Enthusiasts 💡 01:22:41 – Expert Career Advice ⚡ 01:24:07 – Rapid-Fire Round…

1
Tomás Hernando Kofman: Not Diamond, Gen AI Startups, AI systems and Routing - AI Portfolio Podcast 1:33:33

לפני 15 weeks1:33:33

1:33:33

Tomás Hernando Kofman of Not Diamond, discussing about gen ai model routing. Follow/Connect: Tomas LinkedIn:https://www.linkedin.com/in/tomashk/ Mark Linkedin: https://www.linkedin.com/in/markmoyou/ Chapters: 00:00 Intro 01:56 Not Diamond 08:36 Router Problem 13:33 Time to Build v1 15:49 Human Categories 19:01 System Prompt 27:33 Prompt Drift 30:14 Guardrails 35:48 Model Spectrum 42:23 Caching 45:50 Training Router 54:44 Cost and Latency 01:01:22 Routing Across Agents 01:03:38 Prompt Pasting 01:09:17 Routing for Multimodal Models 01:12:08 Startup 01:21:20 Building Companies in the Gen AI Era 01:27:00 Career Optimization Function 01:29:33 Career Advice 01:32:00 Rapid Round…

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Raza Habib: Humanloop, LLM and Prompting, Gen AI Performace - AI Portfolio Podcast 1:31:00

לפני 19 weeks1:31:00

1:31:00

Dr. Raza Habib, CEO of HumanLoop, discussing the complexities of large language model (LLM) deployment, monitoring, and evaluation. Follow/Connect: Raza Linkedin: https://www.linkedin.com/in/humanloop-raza/ Mark LinkedIn: https://www.linkedin.com/in/markmoyou/ Chapters: 00:00 Intro 02:19 Humanloop 06:08 Prompt Management 13:26 LLM System 17:57 AI Guardrails 22:32 Workflow 27:14 User Generated Hyperparameters 31:30 LLM Routing 34:20 Prompt Engineering 38:22 Data Poisoning 45:17 Latency in LLMs 50:25 Monitoring LLMs 57:57 LLM Eval Benchmarks 01:03:27 Startup 01:17:51 Career Optimization Function 01:19:44 Books 01:24:27 Career Advice 01:27:51 Rapid Round…

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Ravit Jain: Startup Marketing, Ravit Show, Content Creation and Role of Influencers - AI Portfolio 1:36:14

לפני 21 weeks1:36:14

1:36:14

Ravit Jain of the Ravit Show, a top media outlet covering the latest trends in Data and AI Follow/Connect: Mark LinkedIn: https://www.linkedin.com/in/markmoyou/ Ravit Linkedin: https://www.linkedin.com/in/ravitjain/ Chapters: 00:00 Intro 02:26 Ravit Show 06:38 Data Orchestration 11:48 Gen AI Strategy 19:05 Skepticism 24:29 Prompt Engineering 29:53 Top 3 Problems to Invest In 36:44 Open Source Vs Enterprise 39:40 Developers as CEOs 43:07 Challenges in Value Offering 51:26 Targeting Actual Users 01:00:41 Reinforcement through Content 01:03:47 GenAI in Influencer Marketing 01:06:27 Value of Long Form Content 01:09:37 Building Personal Brands 01:19:47 Career Optimization Function 01:25:03 AI Job Market 01:30:55 Book Recommendations 01:32:29 Career Advice 01:32:49 Rapid Round…

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William A. Gaviria Rojas: CoactiveAI, Future of Creator Economy and AI Agents - AI Portfolio Podcast 1:15:33

לפני 22 weeks1:15:33

1:15:33

Welcome to AI Porfolio Podcast Episode 17, Dr. William Gaviria Rojas, Cofounder of Coactive AI a data analytics platform for unstructured visual data at the largest scale. Follow/Connect: Mark LinkedIn: https://www.linkedin.com/in/markmoyou/ William Linkedin: https://www.linkedin.com/in/williamgaviria/ Chapters: 00:00 Intro 01:58 Billion Dollar Startup 02:57 Coactive AI 05:29 Market Size of MultiModal AI 07:43 Large Data Problem 14:56 Ideal Market 20:22 Taxonomy Refinement 24:42 Insight Economy (Derivative Data) 28:15 Computer Vision (High Fidelity Data Analysis) 35:13 Personalized Video Media 38:22 Coactive Software Deployment 42:22 Agents Integration 44:14 Coactive GPUs 45:27 Embedding Models 48:25 Largest Video Dataset 49:29 Unfit Customers 53:04 Starting Coactive 01:02:48 Venture Funding 01:05:50 Unfair Advantage 01:07:52 PhD 01:11:30 Career Advice…

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Philip Rathle: GraphRAG, Neo4J CTO, Graphs and Vectors and Mission - AI Portfolio Podcast 1:42:31

לפני 28 weeks1:42:31

1:42:31

Philip Rathle, the Chief Technical Officer of Neo4j, the popular graph database company which has now taken off by storm because of GraphRag, a new approach for making LLM Retrieval Augmented Generation applications more accurate by leveraging graphs, so you know today will be all about GraphRag and its impact on the market. Chapters: 00:00 Intro 02:09 Is AI Resurgence of Graph tech? 03:46 GraphRAG popularity 05:39 Top Use Cases in GenAI 11:08 Gen AI in supply chain 16:46 Graph and its types in enterprise 24:03 GraphRag 25:25 GNNs in GraphRAG 29:30 Graphs are eating the world 35:16 Knowledge Graph 36:06 Drawbacks of vector based rag 37:43 Neo4j vector database 41:27 Filtering with Knowledge Graph 45:02 Execution Time of LLMs 49:03 Does longer prompts mean longer graph query? 54:26 Scale of Graph 57:05 Marriage of Graphs and Vectors 59:46 Fine Tuning with Graphs 01:00:46 Graphs Use less tokens 01:02:46 Multiple vs One GraphRAG 01:05:38 Updating Knowledge in Graph 01:10:50 large Vs small models 01:13:09 MultiModal GraphRAG 01:15:36 Graphs in Robotics 01:17:11 Neo4j journey 01:20:03 Phillip Linkedin Post 01:21:56 What's different with AI 01:23:31 Advice for Gen AI startups 01:26:00 CTO advice 01:29:36 Chemical Engineering 01:32:00 Career optimization function 01:35:00 Book Recommendations 01:37:06 Rapid Round…

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Kyle Kranen: End Points, Optimizing LLMs, GNNs, Foundation Models - AI Portfolio Podcast #011 1:30:01

לפני 31 weeks1:30:01

1:30:01

Get 1000 free inference requests for LLMs on build.nvidia.com Kyle Kranen, an engineering leader at NVIDIA, who is at the forefront of deep learning, real-world applications, and production. Kyle shares his expertise on optimizing large language models (LLMs) for deployment, exploring the complexities of scaling and parallelism. 📲 Kyle Kranen Socials: LinkedIn: https://www.linkedin.com/in/kyle-kranen/ Twitter: https://x.com/kranenkyle 📲 Mark Moyou, PhD Socials: LinkedIn: https://www.linkedin.com/in/markmoyou/ Twitter: https://twitter.com/MarkMoyou 📗 Chapters [00:00] Intro [01:26] Optimizing LLMs for deployment [10:23] Economy of Scale (Batch Size) [13:18] Data Parallelism [14:30] Kernels on GPUs [18:48] Hardest part of optimizing [22:26] Choosing hardware for LLM [31:33] Storage and Networking - Analyzing Performance [32:33] Minimum size of model where tensor parallel gives you advantage [35:20] Director Level folks thinking about deploying LLM [37:29] Kyle is working on AI foundation models [40:38] Deploying Models with endpoints [42:43] Fine Tuning, Deploying Loras [45:02] SteerLM [48:09] KV Cache [51:43] Advice for people for deploying reasonable and large scale LLMs [58:08] Graph Neural Networks [01:00:04] GNNs [01:04:22] Using GPUs to do GNNs [01:08:25] Starting your GNN journey [01:12:51] Career Optimization Function [01:14:46] Solving Hard Problems [01:16:20] Maintaining Technical Skills [01:20:53] Deep learning expert [01:26:00] Rapid Round…

1
Chris Deotte: Kaggle Competitions, LLM models and techniques, PhD and Technical Career 2:01:09

לפני 31 weeks2:01:09

2:01:09

Kaggle Grandmaster Chris Deotte, he is currently ranked 1 on notebooks and discussions on Kaggle and is part of the KGMON team, Kaggle Grandmasters of NVIDIA. We’ll be discussing GEN AI and personalization, optimizing your kaggle game and other strategies to make progress in your career. Solution: https://arxiv.org/pdf/2408.04658 Mark Moyou, PhD Socials: LinkedIn: https://www.linkedin.com/in/markmoyou/ Twitter: https://twitter.com/MarkMoyou Chapters: 00:00 Intro 01:51 Current Gen AI 04:40 Evolution of Conceptualization in ML Models 06:59 Measuring Tonality in Data Sets 08:51 Multi-Modal Data Sets in Text Based Models 11:56 Large Vs Small Language Models 13:46 KDD 2024 Competition 23:28 Prompt Formatting and Bribing the Model 28:08 Qwen2 Vs LLama 30:39 WiSE - FT 33:53 LoRA on all the layers 35:43 Logit Preprocessor 42:05 Personality of Small Vs Large Model 44:02 Models Understanding Shopping Concepts for E-Commerce 47:26 Offline Purchase Data in E-Commerce Personalization 55:56 Navigating the Problem with Required Data 58:33 Constraining LLM Output 01:00:45 LLMs in Search and Personalization 01:02:03 Kaggle Grandmaster 01:09:45 Gen AI in Kaggle Competition 01:13:07 Learning ML in Non-Traditional Way 01:16:15 Thoughts on doing PhD 01:17:58 Mathematics 01:22:22 Advice for PhD students 01:24:32 Hardest Kaggle Competition 01:27:32 Level of Grit in Competitions 01:32:59 Career Optimization Function 01:35:00 Management vs Technical IC Roles 01:37:27 Making Progress 01:39:48 Book Recommendations 01:44:43 Thoughts on Writing Book 01:46:20 Advice for High Schooler, College Students and Professionals 01:52:20 Rapid Round…

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Chris Walton: The Art of Merchant, E-commerce, Gen AI in Retail Market - AI Portfolio Podcast #014 1:24:33

לפני 38 weeks1:24:33

1:24:33

Chris Walton, a top expert in omnichannel retailing, has nearly 20 years of experience in retail and retail technology. As Co-CEO and Founder of Omni Talk, one of the fastest-growing retail blogs, he's a leading voice in the Retail Technology industry. Chris brings deep insights with a background at Target as VP of Store of the Future and Merchandising for Home Furnishings. He holds an MBA from Harvard and a BA from Stanford. Chris Walton Socials: LinkedIn: https://www.linkedin.com/in/chriswaltonretail/ OmniTalk Retail:https://www.linkedin.com/company/omnitalk/posts/?feedView=all Twitter: https://mobile.x.com/OmniTalk Mark Moyou, PhD Socials: LinkedIn: https://www.linkedin.com/in/markmoyou/ Twitter: https://twitter.com/MarkMoyou Chapters 00:00 Intro 01:58 Gen AI in Retail 04:53 E-Commerce 06:10 When E-Commerce boomed 09:15 Advancement of Retail with accelerated compute 11:03 Fast Commerce is eating business of large ecommerce players 12:43 Labour Dynamics in Retail (Indian vs US market) 14:40 Real Business Value of Gen AI 17:03 Electronic Dynamic Tags 19:35 Computer Vision and Personalization 22:10 The art of merchandising 24:02 Importance of Merchant 29:30 MerchantGPT 35:07 Customer loyalty 37:39 Segmentation of Retail market 39:45 Impact of Gen AI in Retail Market 42:00 Shift in Retail Market 48:28 Marketing with Gen AI 52:07 Why AI can't replicate sale associates and customer service 55:44 Challenges of data in Retail Market 59:54 Doing my own way mindset in retail 01:01:07 Executive Big Bets 01:03:13 Search in Product Discovery 01:06:13 Retail Media Networks 01:08:20 Retail Ads, YouTube and Brand Recognition 01:11:47 AI Generated Content 01:14:40 Are Malls dead? 01:15:20 AI Agents in Retail 01:16:40 Career Optimization Function 01:19:23 Three Book Recommendations 01:20:04 Career Advice 01:22:15 Rapid Round…

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Ramsri Goutham Golla: Micro Saas, Philosophy, Product Mindset - AI Portfolio Podcast 2:33:02

לפני 45 weeks2:33:02

2:33:02

Ramsri, an AI expert with 12+ years of experience in AI and product development, specializing in computer vision and NLP. Ramsri is the founder of Questgen.ai and Supermeme.ai, innovative NLP SaaS apps. He also teaches NLP and AI SaaS courses at LearnNLP Academy and shares insights on Medium, Twitter, and YouTube. 📲 Ramsri Goutham Golla Socials: LinkedIn: https://www.linkedin.com/in/ramsrig/ Twitter: https://twitter.com/ramsri_goutham 📲 Mark Moyou, PhD Socials: LinkedIn: https://www.linkedin.com/in/markmoyou/ Twitter: https://twitter.com/MarkMoyou 📗 Chapters 00:00 Intro 02:43 Bootstrapping Two Companies 05:09 PhD and Entrepreneurship 08:11 Overview of Companies 09:53 Memes 12:23 Bootstrapping Startups 22:27 Minimalistic Philosophy for Startups 25:47 Building Distribution through Personal Brand 34:39 Saas vs Micro Saas 40:15 Happy Place 47:51 Major Components of AI saas apps 54:04 Open Source Models vs API 58:26 Getting Featured in Google 01:08:17 Coming back to India 01:17:36 Takeaways from Silicon Valley 01:32:48 Company of one 01:35:39 Number One on Product Hunt 01:45:25 Generalize High Cognition Barrier 01:53:15 Mistakes in building Saas 01:59:17 Unfair Advantage 02:01:53 Advice for launching micro saas apps 02:05:09 If you have to start over 02:08:36 Advice for high schooler, college student and professionals 02:22:17 Rapid Round…

1
Serg Masis: AGI, Interpretable Machine Learning, Upcoming Book - AI Portfolio Podcast 1:54:54

לפני 45 weeks1:54:54

1:54:54

Serg Masís, a renowned Data Scientist in the agriculture sector, with a multifaceted background in entrepreneurship and web/app development. Serg is also the author of the acclaimed book "Interpretable Machine Learning with Python." 📲 Serg Masís Socials: LinkedIn: https://www.linkedin.com/in/smasis/ 📲 Mark Moyou, PhD Socials: LinkedIn: https://www.linkedin.com/in/markmoyou/ Twitter: https://twitter.com/MarkMoyou 📗 Chapters 00:00 Intro 03:02 Interpretable Machine Learning System 11:06 post-hoc interpretability 12:57 Quality of Interpretability bounded by the breadth of inputs 18:52 EDA is more than just exploring the data 21:12 Interpretable Models 27:19 Upper Limit 28:59 Causal reasoning 35:02 Domain Knowledge in Causality 37:42 Causality on existing and generative data 40:42 AGI and Simulation 47:33 LLMs to Interpret Data 52:25 Interaction with Machine Learning Systems 54:53 Modeling in EDA 57:34 Training Data Mixtures 01:02:26 Impact of Generative AI 01:06:05 Mixture of Experts 01:12:46 Serg's upcoming book 01:18:54 Building Product Mindset 01:23:59 Barrier of Entry for Data Scientists 01:28:01 Advice for Book Writers 01:33:43 Career Optimization Function 01:37:45 Web Development Background 01:42:24 Advice for High Schooler, College Student and Professionals 01:47:57 Book Recommendations 01:49:26 Rapid Round…

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Sanyam Bhutani: LLM Experimentation, Podcasting Insights, and AI Innovations - AI Portfolio Podcast 1:21:46

לפני 50 weeks1:21:46

1:21:46

Sanyam Bhutani, a leading figure in the data science community. Sanyam is a Sr. Data Scientist at H2O.ai, with previous tenures at Weights & Biases and H2O.ai, and an International Fellow at fast.ai . As a Kaggle Grandmaster, his contributions to the field are well-recognized and highly respected. Sanyam delves into the nuances of fine-tuning and optimizing Large Language Models (LLMs). He provides a detailed exploration of the current state and future potential of LLMs, breaking down their architecture and functionality in a way that's accessible to both newcomers and seasoned data scientists. Sanyam discusses the importance of fine-tuning in enhancing the performance and applicability of LLMs, providing practical insights and strategies for effective implementation. 📲 Radek Osmulski Socials: LinkedIn: https://www.linkedin.com/in/sanyambhutani/ Twitter: https://x.com/bhutanisanyam1?lang=en 📲 Mark Moyou, PhD Socials: LinkedIn: https://www.linkedin.com/in/markmoyou/ Twitter: https://twitter.com/MarkMoyou 📗 Chapters 00:00 Intro 02:46 200 days of LLMs 06:16 Venture Capital 08:40 Setting Goals in Public 09:45 Fine tuning Experiment 14:02 Kaggle Grandmasters Team 15:55 Doing Challenges & Reading Research Papers 17:47 Hardest topic to learn in AI 19:05 Are you afraid to ask stupid questions? 20:43 Learning how LLMs work 22:54 Academic vs Product First Mindset 27:51 Training or Inference on LLMs 29:15 Favorite LLM Agent 32:10 How to go about learning LLMs? 36:55 Open Source LLMs on Research Papers 37:41 Capability of Modern GPUs 45:48 Journey to H20.ai 50:07 Why Sanyam stopped podcasting? 56:25 Podcasting Experience 58:39 Top Data Scientists 01:00:19 Advice for New Podcasts 01:03:32 Breaking into Data Science 01:12:23 Career Optimization Function 01:14:02 Making Progress Everyday 01:15:05 Advice for New Professionals 01:17:00 Book Recommendations 01:18:04 Rapid Round…

לפני 1 year1:29:54

1:29:54

In this first episode you will learn why Hamza left Google as a Senior Research Manager to start a search startup given all the progress of large language models (LLM). He covers some core tenants you need to start building LLM-based systems. More importantly, in this fast-moving field, you learn how Hamza navigated his career and what you can do to make progress. Hamza Farooq is the CEO/founder of traversaal.ai. Previously, he was a Sr. Research Science Manager at Google, a lecturer at Stanford, and an adjunct professor at the University of Minnesota. Hamza has worked at top companies such as Walmart Labs and Gallup and teaches a course on Maven called Building LLMs Applications from Scratch into Production. 📲 Hamza Farooq Socials: LinkedIn: / hamzafarooq 📲 Mark Moyou, PhD Socials: LinkedIn: / markmoyou Twitter: / markmoyou Startup (Traversaal.ai) - https://www.traversaal.ai/ 📒 Chapters 00:00:00 Intro 00:03:01 - LLM Summer 00:04:06 - Falcon AI 00:07:27 - Language Modelling 00:08:33 - Distance Vs Metric Space Modeling 00:11:36 - Context Learning vs fine-tuning 00:14:38 - Benefit of larger LLM 00:17:41 - Lexical, Hybrid, or Neural Approach 00:21:41 - Natural preferences in LLM 00:24:52 - LLM in user reviews 00:27:34 - FOMO of AI 00:30:16 - AI Hype or Innovation 00:34:26 - Generative Models 00:35:40 - Open AI vs open-source models 00:38:36 - Open Source Models monetization 00:40:18 - Mistakes in LLM journey 00:41:53 - What is LLM 00:44:01 - LLM optimization 00:46:09 - NLP or LLM 00:47:35 - LLM consumption 00:49:04 - Models Hosting 00:51:17 - Education 00:53:33 - Open Source work 00:55:29 - ML system design 00:58:14 - Career 01:04:15 - Career Optimization function 01:08:24 - Advice for international students 01:11:06 - MBA and PhD 01:15:06 - Imposter Syndrome 01:16:44 - Perspectives for People in their home country 01:20:24 - Unfair Advantage…

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Maxime Labonne: LLM Scientist Roadmap, AI Scientist, LLM Course & Open Source - AI Portfolio Podcast