Machine Learning Guide podcast

Player FM - Internet Radio Done Right

590 subscribers

Artificial Intelligence

הוסף לפני eight שנים

תוכן מסופק על ידי OCDevel. כל תוכן הפודקאסטים כולל פרקים, גרפיקה ותיאורי פודקאסטים מועלים ומסופקים ישירות על ידי OCDevel 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.

TechSurge: Deep Tech VC Podcast

1
Understanding the Elegant Math Behind Modern Machine Learning 1:14:43

לפני 13 ימים1:14:43

הפעל מאוחר יותר

רשימות

לייק

אהבתי

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…

Machine Learning Guide

שתפו

בית הסדרות•Feed

Machine learning audio course, teaching the fundamentals of machine learning and artificial intelligence. It covers intuition, models (shallow and deep), math, languages, frameworks, etc. Where your other ML resources provide the trees, I provide the forest. Consider MLG your syllabus, with highly-curated resources for each episode's details at ocdevel.com. Audio is a great supplement during exercise, commute, chores, etc.

55 פרקים

#Artificial #Intelligence #Introduction #Artificial Intelligence #Tech #Higher Education #Podcasting Education #OCDevel #Learn #Machining

Machine Learning Guide

590 subscribers

updated לפני שנה

שתפו

בית הסדרות•Feed

55 פרקים

#Artificial #Intelligence #Introduction #Artificial Intelligence #Tech #Higher Education #Podcasting Education #OCDevel #Learn #Machining

Wszystkie odcinki

1
MLA 022 Code AI Tools 46:30

לפני 4 weeks46:30

46:30

Try a walking desk while studying ML or working on your projects! https://ocdevel.com/walk Show notes: https://ocdevel.com/mlg/mla-22 Tools discussed: Windsurf: https://codeium.com/windsurf Copilot: https://github.com/features/copilot Cursor: https://www.cursor.com/ Cline: https://github.com/cline/cline Roo Code: https://github.com/RooVetGit/Roo-Code Aider: https://aider.chat/ Other: Leaderboards: https://aider.chat/docs/leaderboards/ Video of speed-demon: https://www.youtube.com/watch?v=QlUt06XLbJE&feature=youtu.be Reddit: https://www.reddit.com/r/chatgptcoding/ Boost programming productivity by acting as a pair programming partner. Groups these tools into three categories: • Hands-Off Tools: These include solutions that work on fixed monthly fees and require minimal user intervention. GitHub Copilot started with simple tab completions and now offers an agent mode similar to Cursor, which stands out for its advanced codebase indexing and intelligent file searching. Windsurf is noted for its simplicity—accepting prompts and performing automated edits—but some users report performance throttling after prolonged use. • Hands-On Tools: Aider is presented as a command-line utility that demands configuration and user involvement. It allows developers to specify files and settings, and it efficiently manages token usage by sending prompts in diff format. Aider also implements an “architect versus edit” approach: a reasoning model (such as DeepSeek R1) first outlines a sequence of changes, then an editor model (like Claude 3.5 Sonnet) produces precise code edits. This dual-model strategy enhances accuracy and reduces token costs, especially for complex tasks. • Intermediate Power Tools: Open-source tools such as Cline and its more advanced fork, RooCode, require users to supply their own API keys and pay per token. These tools offer robust, agentic features, including codebase indexing, file editing, and even browser automation. RooCode stands out with its ability to autonomously expand functionality through integrations (for example, managing cloud resources or querying issue trackers), making it particularly attractive for tinkerers and power users. A decision framework is suggested: for those new to AI coding assistants or with limited budgets, starting with Cursor (or cautiously exploring Copilot’s new features) is recommended. For developers who want to customize their workflow and dive deep into the tooling, RooCode or Cline offer greater control—always paired with Aider for precise and token-efficient code edits. Also reviews model performance using a coding benchmark leaderboard that updates frequently. The current top-performing combination uses DeepSeek R1 as the architect and Claude 3.5 Sonnet as the editor, with alternatives such as OpenAI’s O1 and O3 Mini available. Tools like Open Router are mentioned as a way to consolidate API key management and reduce token costs.…

1
MLG 033 Transformers 42:14

לפני 4 weeks42:14

42:14

Try a walking desk while studying ML or working on your projects! https://ocdevel.com/walk Show notes: https://ocdevel.com/mlg/33 3Blue1Brown videos: https://3blue1brown.com/ Background & Motivation: RNN Limitations: Sequential processing prevents full parallelization—even with attention tweaks—making them inefficient on modern hardware. Breakthrough: “Attention Is All You Need” replaced recurrence with self-attention, unlocking massive parallelism and scalability. Core Architecture: Layer Stack: Consists of alternating self-attention and feed-forward (MLP) layers, each wrapped in residual connections and layer normalization. Positional Encodings: Since self-attention is permutation invariant, add sinusoidal or learned positional embeddings to inject sequence order. Self-Attention Mechanism: Q, K, V Explained: Query (Q): The representation of the token seeking contextual info. Key (K): The representation of tokens being compared against. Value (V): The information to be aggregated based on the attention scores. Multi-Head Attention: Splits Q, K, V into multiple “heads” to capture diverse relationships and nuances across different subspaces. Dot-Product & Scaling: Computes similarity between Q and K (scaled to avoid large gradients), then applies softmax to weigh V accordingly. Masking: Causal Masking: In autoregressive models, prevents a token from “seeing” future tokens, ensuring proper generation. Padding Masks: Ignore padded (non-informative) parts of sequences to maintain meaningful attention distributions. Feed-Forward Networks (MLPs): Transformation & Storage: Post-attention MLPs apply non-linear transformations; many argue they’re where the “facts” or learned knowledge really get stored. Depth & Expressivity: Their layered nature deepens the model’s capacity to represent complex patterns. Residual Connections & Normalization: Residual Links: Crucial for gradient flow in deep architectures, preventing vanishing/exploding gradients. Layer Normalization: Stabilizes training by normalizing across features, enhancing convergence. Scalability & Efficiency Considerations: Parallelization Advantage: Entire architecture is designed to exploit modern parallel hardware, a huge win over RNNs. Complexity Trade-offs: Self-attention’s quadratic complexity with sequence length remains a challenge; spurred innovations like sparse or linearized attention. Training Paradigms & Emergent Properties: Pretraining & Fine-Tuning: Massive self-supervised pretraining on diverse data, followed by task-specific fine-tuning, is the norm. Emergent Behavior: With scale comes abilities like in-context learning and few-shot adaptation, aspects that are still being unpacked. Interpretability & Knowledge Distribution: Distributed Representation: “Facts” aren’t stored in a single layer but are embedded throughout both attention heads and MLP layers. Debate on Attention: While some see attention weights as interpretable, a growing view is that real “knowledge” is diffused across the network’s parameters.…

1
MLA 021 Databricks 26:00

לפני 3 years26:00

26:00

Try a walking desk while studying ML or working on your projects! https://ocdevel.com/walk Discussing Databricks with Ming Chang from Raybeam (part of DEPT® )

1
MLA 020 Kubeflow 1:08:12

לפני 3 years1:08:12

1:08:12

Try a walking desk while studying ML or working on your projects! https://ocdevel.com/walk Conversation with Dirk-Jan Kubeflow (vs cloud native solutions like SageMaker) Dirk-Jan Verdoorn - Data Scientist at Dept Agency Kubeflow . (From the website:) The Machine Learning Toolkit for Kubernetes. The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures. Anywhere you are running Kubernetes, you should be able to run Kubeflow. TensorFlow Extended (TFX) . If using TensorFlow with Kubeflow, combine with TFX for maximum power. (From the website:) TensorFlow Extended (TFX) is an end-to-end platform for deploying production ML pipelines. When you're ready to move your models from research to production, use TFX to create and manage a production pipeline. Alternatives: Airflow MLflow…

1
MLA 019 DevOps 1:14:53

לפני 3 years1:14:53

1:14:53

Try a walking desk while studying ML or working on your projects! https://ocdevel.com/walk Chatting with co-workers about the role of DevOps in a machine learning engineer's life Expert coworkers at Dept Matt Merrill - Principal Software Developer Jirawat Uttayaya - DevOps Lead The Ship It Podcast (where Matt features often) Devops tools Terraform Ansible Pictures (funny and serious) Which AWS container service should I use? A visual guide on troubleshooting Kubernetes deployments Public Cloud Services Comparison Killed by Google aCloudGuru AWS curriculum…

1
MLA 018 Descript 6:22

לפני 3 years6:22

6:22

Try a walking desk while studying ML or working on your projects! https://ocdevel.com/walk (Optional episode) just showcasing a cool application using machine learning Dept uses Descript for some of their podcasting. I'm using it like a maniac, I think they're surprised at how into it I am. Check out the transcript & see how it performed. Descript The Ship It Podcast How to ship software, from the front lines. We talk with software developers about their craft, developer tools, developer productivity and what makes software development awesome. Hosted by your friends at Rocket Insights. AKA shipit.io Brandbeats Podcast by BASIC An agency podcast with views on design, technology, art, and culture. Explore the new microsite at www.brandbeats.basicagency.com…

1
MLA 017 AWS Local Development 1:04:19

לפני 3 years1:04:19

1:04:19

Try a walking desk while studying ML or working on your projects! Show notes: ocdevel.com/mlg/mla-17 Developing on AWS first (SageMaker or other) Consider developing against AWS as your local development environment , rather than only your cloud deployment environment. Solutions: Stick to AWS Cloud IDEs ( Lambda , SageMaker Studio , Cloud9 Connect to deployed infrastructure via Client VPN Terraform example YouTube tutorial Creating the keys LocalStack Infrastructure as Code Terraform CDK Serverless…

1
MLA 016 SageMaker 2 59:57

לפני 3 years59:57

59:57

Try a walking desk while studying ML or working on your projects! Part 2 of deploying your ML models to the cloud with SageMaker (MLOps) MLOps is deploying your ML models to the cloud. See MadeWithML for an overview of tooling (also generally a great ML educational run-down.) SageMaker Jumpstart Deploy Pipelines Monitor Kubernetes Neo…

1
MLA 015 SageMaker 1 47:01

לפני 3 years47:01

47:01

Try a walking desk while studying ML or working on your projects! Show notes Part 1 of deploying your ML models to the cloud with SageMaker (MLOps) MLOps is deploying your ML models to the cloud. See MadeWithML for an overview of tooling (also generally a great ML educational run-down.) SageMaker DataWrangler Feature Store Ground Truth Clarify Studio AutoPilot Debugger Distributed Training And I forgot to mention JumpStart , I'll mention next time.…

1
MLA 014 Machine Learning Server 52:05

לפני 4 years52:05

52:05

Try a walking desk while studying ML or working on your projects! Server-side ML. Training & hosting for inference, with a goal towards serverless. AWS SageMaker, Batch, Lambda, EFS, Cortex.dev

1
MLA 013 Customer Facing Tech Stack 47:08

לפני 4 years47:08

47:08

Try a walking desk while studying ML or working on your projects! Client, server, database, etc.

1
MLA 012 Docker 31:13

לפני 4 years31:13

31:13

Try a walking desk while studying ML or working on your projects! Use Docker for env setup on localhost & cloud deployment, instead of pyenv / Anaconda. I recommend Windows for your desktop.

1
MLG 032 Cartesian Similarity Metrics 41:52

לפני 4 years41:52

41:52

Try a walking desk while studying ML or working on your projects! Show notes at ocdevel.com/mlg/32 . L1/L2 norm, Manhattan, Euclidean, cosine distances, dot product Normed distances link A norm is a function that assigns a strictly positive length to each vector in a vector space. link Minkowski is generalized. p_root(sum(xi-yi)^p). "p" = ? (1, 2, ..) for below. L1: Manhattan/city-block/taxicab. abs(x2-x1)+abs(y2-y1). Grid-like distance (triangle legs). Preferred for high-dim space. L2: Euclidean. sqrt((x2-x1)^2+(y2-y1)^2. sqrt(dot-product). Straight-line distance; min distance (Pythagorean triangle edge) Others: Mahalanobis, Chebyshev (p=inf), etc Dot product A type of inner product. Outer-product: lies outside the involved planes. Inner-product: dot product lies inside the planes/axes involved link . Dot product: inner product on a finite dimensional Euclidean space link Cosine (normalized dot)…

1
MLA 011 Practical Clustering 34:23

לפני 4 years34:23

34:23

Try a walking desk while studying ML or working on your projects! Kmeans (sklearn vs FAISS), finding n_clusters via inertia/silhouette, Agglomorative, DBSCAN/HDBSCAN

1
MLA 010 NLP packages: transformers, spaCy, Gensim, NLTK 25:47

לפני 4 years25:47

25:47

Try a walking desk while studying ML or working on your projects! NLTK: swiss army knife. Gensim: LDA topic modeling, n-grams. spaCy: linguistics. transformers: high-level business NLP tasks.

ברוכים הבאים אל Player FM!

Player FM סורק את האינטרנט עבור פודקאסטים באיכות גבוהה בשבילכם כדי שתהנו מהם כרגע. זה יישום הפודקאסט הטוב ביותר והוא עובד על אנדרואיד, iPhone ואינטרנט. הירשמו לסנכרון מנויים במכשירים שונים.

תקשיבו ל-500+ נושאים

590 subscribers

דומה לMachine Learning Guide

everydrop by Whirlpool Ice and Water Refrigerator Filter 1, EDR1RXD1, Single-Pack , Purple

Apple AirPods Pro 2 Wireless Earbuds, Active Noise Cancellation, Hearing Aid Feature, Bluetooth Headphones, Transparency, Personalized Spatial Audio, High-Fidelity Sound, H2 Chip, USB-C Charging

Bounty Quick Size Paper Towels, White, 8 Family Rolls = 20 Regular Rolls (Packaging May Vary)

פודקאסטים ששווה להאזין

Machine Learning Guide

Machine Learning Guide

פודקאסטים ששווה להאזין

ברוכים הבאים אל Player FM!

The Let Them Theory: A Life-Changing Tool That Millions of People Can't Stop Talking About

The Let Them Theory: A Life-Changing Tool That Millions of People Can't Stop Talking About

MAYHEM

Amazon Basics Multipurpose Copy Printer Paper, 8.5" x 11", 20 lb, 8 Reams, 4000 Sheets, 92 Bright, White

The Let Them Theory: A Life-Changing Tool That Millions of People Can't Stop Talking About

דומה לMachine Learning Guide

מדריך עזר מהיר