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Anywhere Club Podcast

Anywhere Club Podcast

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 Anywhere Club Podcasts - is a tech podcast whose goal is to help people learn new technologies, find a job, and join a professional tech and IT community 😊2023 © Anywhere Club 個人的成功 教育 自己啓発
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  • Coding Interview Questions and Answers - Machine Learning / Mock Interview Show #6

    Coding Interview Questions and Answers - Machine Learning / Mock Interview Show #6
    2025/06/11
    In this episode we shift gears into the world of data science and machine learning engineering.Join us as Mykhailo Kuznietsov steps into a real interview setting, answering 30+ questions covering ML theory, data pipelines, prompt engineering, and more.Hosted by Oleksii Malashyna and interviewed by Denys Soloviov, this session is packed with ML best practices and an engaging live coding challenge — including transforming raw user data into a structured format using Python.Whether you're preparing for an ML role or just want to learn how top candidates think on the spot, this episode offers deep insights and honest feedback to help you grow!NAVIGATION0:00 - Intro05:02 - First part. Could you tell more about your experience13:04 - Second part. What sampling methods to get training data do you know?17:23 - What is the main disadvantage of simple random sampling?18:57 - Hand labels and natural labels23:34 - Problem of lacking the labels31:51 - What feature engineering operations do you know?33:19 - Handling missing values. Compare deletion and imputation for it38:07 - What is the bias/variance tradeoff during training?40:40 - What is ensemble learning?43:40 - What is the difference between batch inference and online inference?46:25 - Compare deployments to the cloud and to edge devices. Their pros and cons.50:21 - What is the data distribution shift? What methods exist to detect data distribution shifts?53:45 - How would you standardize the development environment across different workstations?01:00:17 - What prompt engineering techniques do you know to get more qualitative responses from LLMs?01:03:54 - What is RAG, its purpose?01:12:28 - When is it beneficial to fine-tune a language model?01:14:40 - Third part. Practical task01:25:34 - Feedback sessionWHERE TO WATCH US AND LISTEN🔸 YouTube: https://youtu.be/cUQpQiX5jcE🔸 Google Podcasts: https://bit.ly/awclub-en-google🔸 Apple Podcasts: https://bit.ly/awclub-en-apple🔸 Spotify: https://bit.ly/awclub-en-spotify🔸 Download mp3: https://anywhereclub.simplecast.com/episodes/41ADDITIONAL QUESTIONSWhat sampling methods to get training data do you know?What is the main disadvantage of simple random sampling? (Here I can also ask about stratified sampling, its pros and cons)What are the hand labels and natural labels? What are the pros and cons of hand labeling?The label multiplicity problem: how to minimize the disagreement among annotators?What techniques do you know for handling the lack of labels (shortly tell how each technique works)?Explain class imbalance problem and approaches to handle it.What is data augmentation? What problems does it solve?What feature engineering operations do you know?Compare deletion and imputation for handling missing values. What are their advantages and drawbacks?What is data leakage? How to detect it?What things should we consider while selecting an appropriate algorithm for model training? The model will be used by the app serving external clients. (You can mention and compare specific algorithms while answering this question).What is the bias/variance tradeoff?What is cross-validation? Why is it needed?What is model regularization, its goal? What regularization techniques do you know?What is ensemble learning? What algorithms do you know that leverage ensemble learning?What types of neural networks do you know? Map types and suitable tasks for them.What is the difference between the grid search and randomized search hyperparameter tuning techniques?What model evaluation methods do you know to ensure that the model can be deployed to the production environment? (Tell about perturbation tests, slice based evaluation and so on)What is the difference between batch inference and online inference? Tell about use cases.What model compression methods do you know to reduce the size of a model and reduce inference latency?Compare deployments to the cloud and to edge devices. Their pros and cons.What is the data distribution shift? Provide examples. (Here you can also mention that this is one of the reasons why it is important to do model retraining)What methods exist to detect data distribution shifts?What techniques do you know for model testing in production?How would you standardize the development environment across different workstations? What can be done for environment reproducibility and why is it important?What is Docker? How does it help with autoscaling? Why is Docker Compose needed?What is the model store, its purpose?What is the feature store, its purpose?What prompt engineering techniques do you know to get more qualitative responses from LLMs?What is RAG, its purpose?When is it beneficial to fine-tune a language model?What is parameter efficient fine-tuning and what techniques for it do you know?
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    1 時間 35 分
  • Google & Microsoft Just Changed AI Forever / Claude 4 Goes Rogue / The Road to Abundance #4

    Google & Microsoft Just Changed AI Forever / Claude 4 Goes Rogue / The Road to Abundance #4
    2025/06/03
    In this episode of The Road to Abundance, Gordon Mullan and Govind Shukla break down the biggest AI news from Microsoft Build and Google I/O 2025.From Microsoft’s Copilot Chat going open-source and turning VS Code into an AI-native IDE…To Google launching Gemini 2.5 Flash, DeepThink, and GM3N — multimodal models running on just 4GB RAMPlus: Google’s new tools for async coding (Jules), generative design (Stitch), and even the groundbreaking Video 3 — an AI that generates full speech, characters, and cinema-quality scenes.We explore:• Agent infrastructure at OS and browser level• AI-powered video creation that's nearly indistinguishable from reality• The race between Big Tech and open source in democratizing AI• Whether this new wave of models is laying the perceptual groundwork for AGINAVIGATION00:00 - Introduction01:10 – Microsoft Build 202506:09 - Google I/O 202510:40 - Models13:05 - Development16:38 - Media33:39 - COMPUTEX 202538:48 - Anthropic43:57 - OpenAI47:57 - Mistral48:53 - Chinese Scene53:05 - Tools & Updates54:57 - Law & Order56:36 - Science & Tech59:46 - Robotics01:06:45 - Ethics & Candy01:10:12 - FinalWHERE TO WATCH US AND LISTEN🔸 YouTube: https://youtu.be/M3Y-pxN9R0s🔸 Apple Podcasts: https://bit.ly/awclub-en-apple🔸 Spotify: https://bit.ly/awclub-en-spotify🔸 Download mp3: https://anywhereclub.simplecast.com/episodes/40SERVICES AND LINKS FROM THE EPISODE🔹 MCP at the Windows OS level: https://developer.microsoft.com/en-us/windows/agentic🔹 Experimental API in Edge: https://techcrunch.com/2025/05/19/devs-can-now-tap-microsoft-edge-to-power-ai-web-apps🔹 NLWeb: https://github.com/microsoft/NLWeb🔹 Gemini 2.5 Pro Deep Think and 2.5 Flash models: https://blog.google/technology/google-deepmind/google-gemini-updates-io-2025/🔹 Gemma 3n: https://developers.googleblog.com/en/introducing-gemma-3n/🔹 Gemini Diffusion paper: https://goo.gle/44MwCW3🔹 Jules agent: https://blog.google/technology/google-labs/jules/🔹 AlphaEvolve agent: https://deepmind.google/discover/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms🔹 Stitch service: https://stitch.withgoogle.com/create🔹 Example of Veo 3 video: https://vm.tiktok.com/ZNdhurrDe/🔹 Google AI Ultra subscription: https://one.google.com/about/google-ai-plans🔹 Flow editor: https://flow.google🔹 RTX PRO 6000 Blackwell Server Edition: https://www.nvidia.com/en-us/data-center/rtx-pro-6000-blackwell-server-edition/🔹 NVLink Fusion: https://www.tomshardware.com/pc-components/cpus/nvidia-announces-nvlink-fusion-to-allow-custom-cpus-and-ai-accelerators-to-work-with-its-products🔹 Arc Pro “Battlemage” GPUs: https://www.techpowerup.com/336957/intel-announces-arc-pro-b50-and-b60-graphics-cards-for-pro-vis-and-ai-inferencing🔹 Sonnet 4 and Opus 4 models: https://www.anthropic.com/news/claude-4🔹 Claude 4 vulnerability report: https://www-cdn.anthropic.com/6be99a52cb68eb70eb9572b4cafad13df32ed995.pdf🔹 Denis Shiryaev’s post: https://t.me/denissexy/10154🔹 Claude Code SDK: https://docs.anthropic.com/en/docs/claude-code/sdk🔹 Claude Code Action: https://github.com/anthropics/claude-code-action🔹 Claude Code IDE integrations: https://docs.anthropic.com/en/docs/claude-code/ide-integrations🔹 OpenAI Codex: https://openai.com/index/introducing-codex🔹 Video with Jony Ive and Sam Altman: https://www.youtube.com/watch?v=W09bIpc_3ms🔹 Data center in Abu Dhabi: https://www.wsj.com/tech/open-ai-abu-dhabi-data-center-1c3e384d🔹 OpenAI HealthBench: https://openai.com/index/healthbench/?utm_source=alphasignal🔹 Devstral by Mistral: https://huggingface.co/mistralai/Devstral-Small-2505🔹 Seed-Coder-8B: https://github.com/ByteDance-Seed/Seed-Coder/tree/master🔹 BAGEL by ByteDance: https://github.com/bytedance-seed/BAGEL🔹 Wan2.1-VACE by Alibaba: https://github.com/Wan-Video/Wan2.1🔹 HunyuanCustom: https://hunyuancustom.github.io/?utm_source=alphasignal🔹 Hunyuan-TurboS report: https://github.com/Tencent-Hunyuan/Hunyuan-TurboS/blob/main/Hunyuan-Turbos_Report.pdf🔹 CodeBuddy by Tencent: https://copilot.tencent.com/🔹 Cursor 0.50 update: https://www.cursor.com/changelog/0-50🔹 Windsurf Wave 9 update: https://windsurf.com/blog/windsurf-wave-9-swe-1🔹 SWE-rebench by Nebius: https://swe-rebench.com/leaderboard🔹 Agent by flowith: https://flowith.io/blank🔹 Super Agents by SkyWork AI: https://skywork.ai🔹 NotebookLM on iOS: https://apps.apple.com/nl/app/google-notebooklm/id6737527615?l=en-GB🔹 NotebookLM on Android: https://play.google.com/store/apps/details?id=com.google.android.apps.labs.language.tailwind&hl=en_US🔹 CTM by SakanaAI: https://pub.sakana.ai/ctm/🔹 New Pope and AI: https://www.theverge.com/news/664719/pope-leo-xiv-artificial-intelligence-concerns🔹 Empire of AI book: https://bookshop.org/p/books/empire-of-ai-dreams-and-nightmares-in-sam-altman-s-openai-karen-hao/4d0c1753c458e708#engx #googleio2025 #computex2025
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    1 時間 11 分
  • 1M Tokens, Secret AI Socials & Tanks? Welcome to 2025 / The Road To Abundance #3

    1M Tokens, Secret AI Socials & Tanks? Welcome to 2025 / The Road To Abundance #3
    2025/05/19
    NAVIGATION00:00 - Introduction01:06 - Big Fish: Gemini 2.5 Pro (I/O edition)46:37 - Chinese Carp Section: Qwen 355:52 - Other01:01:09 - Stats & Legal News01:04:14 - Law & Order01:13:28 - Robotics01:08:24 - Science & Tech01:21:04 - Military Tech01:22:38 - Food and Agriculture01:24:42 - Ethics & Edge Cases01:29:33 - FinalWHERE TO WATCH US AND LISTEN🔸 YouTube: https://youtu.be/KdCa0pbTpTs🔸 Apple Podcasts: https://bit.ly/awclub-en-apple🔸 Spotify: https://bit.ly/awclub-en-spotify🔸 Download mp3: https://anywhereclub.simplecast.com/episodes/39SERVICES AND LINKS FROM THE EPISODE🔹 OpenAI to acquire Windsurf: https://www.computerworld.com/article/3978426/openai-to-acquire-ai-coding-tool-windsurf-for-3b.html🔹 OpenAI enhances product info in search: https://techcrunch.com/2025/04/28/openai-upgrades-chatgpt-search-with-shopping-features/🔹 Deep Research via GitHub: https://help.openai.com/en/articles/11145903-connecting-github-to-chatgpt-deep-research🔹 Gemini 2.5 Pro update: https://developers.googleblog.com/en/gemini-2-5-pro-io-improved-coding-performance/🔹 Gemini model cards: https://modelcards.withgoogle.com/model-cards🔹 Multistep image editing: https://blog.google/products/gemini/image-editing/?utm_source=alphasignal🔹 DeepMind’s Genie 2 announcement: https://deepmind.google/discover/blog/genie-2-a-large-scale-foundation-world-model/🔹 Phi-4 reasoning by Microsoft: https://aka.ms/phi4-mini-reasoning/hf🔹 Llama API by Meta: https://www.llama.com/products/llama-api/🔹 Meta AI – new app launch: https://about.fb.com/news/2025/04/introducing-meta-ai-app-new-way-access-ai-assistant/🔹 Grok voice assistant: https://x.com/grok/status/1919099647991177712?utm_source=alphasignal🔹 PDF generation in Grok Studio: https://x.com/grok/status/1919795285515211249?utm_source=alphasignal🔹 Perplexity’s WhatsApp number: https://x.com/AravSrinivas/status/1916891961698763205🔹 Mistral Medium 3: https://mistral.ai/news/mistral-medium-3%20🔹 Qwen 3 by Alibaba: https://qwenlm.github.io/blog/qwen3/🔹 Ernie 4.5 Turbo and X1 Turbo by Baidu: https://www.prnewswire.com/news-releases/baidu-launches-ernie-4-5-turbo-ernie-x1-turbo-and-new-suite-of-ai-tools-to-empower-developers-and-supercharge-ai-innovation-302438584.html🔹 Xinxiang by Baidu: https://www.reuters.com/technology/baidu-launches-ai-agent-xinxiang-android-platform-ios-version-under-review-2025-04-23/%20🔹 DeepSeek Prover v2: https://huggingface.co/deepseek-ai/DeepSeek-Prover-V2-671B🔹 Xiaomi MiMo 7B: https://huggingface.co/XiaomiMiMo🔹 GLM-4 32B by Z.ai: https://huggingface.co/collections/THUDM/glm-4-0414-67f3cbcb34dd9d252707cb2e🔹 Z.ai interface: http://chat.z.ai🔹 HunyuanCustom by Tencent: https://hunyuancustom.github.io/🔹 Cursor Pro for students: https://www.cursor.com/students🔹 Windsurf Wave 8: https://windsurf.com/blog/windsurf-wave-8-ux-features-and-plugins🔹 Suno v4.5 update: https://suno.com/blog/introducing-v4-5🔹 Voice Mirroring by HeyGen: https://www.tiktok.com/@benkaluza/video/7501464565083819286🔹 Similarweb GenAI traffic report: https://www.similarweb.com/corp/wp-content/uploads/2025/04/attachment-Global-AI-Tracker-13.pdf🔹 Xi Jinping on AI self-sufficiency: https://www.reuters.com/world/china/chinas-xi-calls-self-sufficiency-ai-development-amid-us-rivalry-2025-04-26/🔹 UAE introduces AI in schools: https://www.bloomberg.com/news/articles/2025-05-04/uae-rolls-out-ai-for-schoolkids-in-new-push-for-sector-forefront🔹 US open letter on AI education: https://csforall.org/unlock8/open-letter🔹 FutureHouse AI agents: https://www.futurehouse.org/research-announcements/launching-futurehouse-platform-ai-agents🔹 AI-powered tank project: https://hightech.plus/2025/04/27/evropa-sobiraetsya-postroit-modulnii-tank-5-go-pokoleniya🔹 SO-101 robotic arm: https://github.com/huggingface/lerobot/blob/main/examples/12_use_so101.md🔹 AI cameras in NYC subway: https://www.theverge.com/news/658524/mta-ai-predictive-crime-new-york-subway-platforms🔹 AI avatar in Arizona courtroom: https://www.nbcnews.com/news/us-news/road-rage-victim-speaks-killers-sentencing-rcna205454
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    1 時間 31 分

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