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Machine Learning Guide
Machine Learning GuideMLA 027 AI Video End-to-End WorkflowHow to maintain character consistency, style consistency, etc in an AI video. Prosumers can use Google Veo 3’s "High-Quality Chaining" for fast social media content. Indie filmmakers can achieve narrative consistency by combining Midjourney V7 for style, Kling for lip-synced dialogue, and Runway Gen-4 for camera control, while professional studios gain full control with a layered ComfyUI pipeline to output multi-layer EXR files for standard VFX compositing. Links Notes and resources at ocdevel.com/mlg/mla-27 Try a walking desk - stay healthy & sharp while you learn & code Descript - my favorite AI audio/video editor AI Audio Tool...
2025-07-141h 11Machine Learning Guide
Machine Learning GuideMLA 026 AI Video Generation: Veo 3 vs Sora, Kling, Runway, Stable Video Diffusion Google Veo leads the generative video market with superior 4K photorealism and integrated audio, an advantage derived from its YouTube training data. OpenAI Sora is the top tool for narrative storytelling, while Kuaishou Kling excels at animating static images with realistic, high-speed motion. Links Notes and resources at ocdevel.com/mlg/mla-26 Try a walking desk - stay healthy & sharp while you learn & code Build the future of multi-agent software with AGNTCY. S-Tier: Google Veo The market leader due to superior visual quality, physics simulation, 4K resolution, and integrated audio generation, which removes post-production steps. It accurately inte...
2025-07-1240 minMachine Learning Guide
Machine Learning GuideMLA 025 AI Image Generation: Midjourney vs Stable Diffusion, GPT-4o, Imagen & Firefly The AI image market has split: Midjourney creates the highest quality artistic images but fails at text and precision. For business use, OpenAI's GPT-4o offers the best conversational control, while Adobe Firefly provides the strongest commercial safety from its exclusively licensed training data. Links Notes and resources at ocdevel.com/mlg/mla-25 Try a walking desk - stay healthy & sharp while you learn & code Build the future of multi-agent software with AGNTCY. The 2025 generative AI image market is defined by a split between two types of tools. "Artists" like Midjourney excel at creating beautiful, high-quality images but...
2025-07-0958 minMachine Learning Guide
Machine Learning GuideMLG 036 Autoencoders Auto encoders are neural networks that compress data into a smaller "code," enabling dimensionality reduction, data cleaning, and lossy compression by reconstructing original inputs from this code. Advanced auto encoder types, such as denoising, sparse, and variational auto encoders, extend these concepts for applications in generative modeling, interpretability, and synthetic data generation. Links Notes and resources at ocdevel.com/mlg/36 Try a walking desk - stay healthy & sharp while you learn & code Build the future of multi-agent software with AGNTCY. Thanks to T.J. Wilder from intrep.io for recording this episode! Fundamentals of Autoencoders Autoencoders are neural network...
2025-05-301h 05Machine Learning Guide
Machine Learning GuideMLG 035 Large Language Models 2At inference, large language models use in-context learning with zero-, one-, or few-shot examples to perform new tasks without weight updates, and can be grounded with Retrieval Augmented Generation (RAG) by embedding documents into vector databases for real-time factual lookup using cosine similarity. LLM agents autonomously plan, act, and use external tools via orchestrated loops with persistent memory, while recent benchmarks like GPQA (STEM reasoning), SWE Bench (agentic coding), and MMMU (multimodal college-level tasks) test performance alongside prompt engineering techniques such as chain-of-thought reasoning, structured few-shot prompts, positive instruction framing, and iterative self-correction. Links Notes and resources at o...
2025-05-0845 minMachine Learning Guide
Machine Learning GuideMLG 034 Large Language Models 1Explains language models (LLMs) advancements. Scaling laws - the relationships among model size, data size, and compute - and how emergent abilities such as in-context learning, multi-step reasoning, and instruction following arise once certain scaling thresholds are crossed. The evolution of the transformer architecture with Mixture of Experts (MoE), describes the three-phase training process culminating in Reinforcement Learning from Human Feedback (RLHF) for model alignment, and explores advanced reasoning techniques such as chain-of-thought prompting which significantly improve complex task performance. Links Notes and resources at ocdevel.com/mlg/mlg34 Build the future of multi-agent software with AGNTCY Try a...
2025-05-0750 minMachine Learning Guide
Machine Learning GuideMLA 024 Code AI MCP Servers, ML Engineering Tool use in code AI agents allows for both in-editor code completion and agent-driven file and command actions, while the Model Context Protocol (MCP) standardizes how these agents communicate with external and internal tools. MCP integration broadens the automation capabilities for developers and machine learning engineers by enabling access to a wide variety of local and cloud-based tools directly within their coding environments. Links Notes and resources at ocdevel.com/mlg/mla-24 Try a walking desk stay healthy & sharp while you learn & code Tool Use in Code AI Agents Code AI agents offer two primary modes of interaction: i...
2025-04-1343 minMachine Learning Guide
Machine Learning GuideMLA 023 Code AI Models & Modes Gemini 2.5 Pro currently leads in both accuracy and cost-effectiveness among code-focused large language models, with Claude 3.7 and a DeepSeek R1/Claude 3.5 combination also performing well in specific modes. Using local open source models via tools like Ollama offers enhanced privacy but trades off model performance, and advanced workflows like custom modes and fine-tuning can further optimize development processes. Links Notes and resources at ocdevel.com/mlg/mla-23 Try a walking desk stay healthy & sharp while you learn & code Model Current Leaders According to the Aider Leaderboard (as of April 12, 2025), leading models include for vib...
2025-04-1337 minMachine Learning Guide
Machine Learning GuideMLA 022 Code AI: Cursor, Cline, Roo, Aider, Copilot, Windsurf Vibe coding is using large language models within IDEs or plugins to generate, edit, and review code, and has recently become a prominent and evolving technique in software and machine learning engineering. The episode outlines a comparison of current code AI tools - such as Cursor, Copilot, Windsurf, Cline, Roo Code, and Aider - explaining their architectures, capabilities, agentic features, pricing, and practical recommendations for integrating them into development workflows. Links Notes and resources at ocdevel.com/mlg/mla-22 Try a walking desk stay healthy & sharp while you learn & code Definition and Context of Vibe Coding Vibe coding re...
2025-02-0955 minMachine Learning Guide
Machine Learning GuideMLG 033 TransformersLinks: Notes and resources at ocdevel.com/mlg/33 3Blue1Brown videos: https://3blue1brown.com/ Try a walking desk stay healthy & sharp while you learn & code Try Descript audio/video editing with AI power-tools 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...
2025-02-0943 minMachine Learning Guide
Machine Learning GuideMLA 021 Databricks: Cloud Analytics and MLOps Databricks is a cloud-based platform for data analytics and machine learning operations, integrating features such as a hosted Spark cluster, Python notebook execution, Delta Lake for data management, and seamless IDE connectivity. Raybeam utilizes Databricks and other ML Ops tools according to client infrastructure, scaling needs, and project goals, favoring Databricks for its balanced feature set, ease of use, and support for both startups and enterprises. Links Notes and resources at ocdevel.com/mlg/mla-21 Try a walking desk stay healthy & sharp while you learn & code Raybeam and Databricks Raybeam is a data science and analytics company, recently ac...
2022-06-2226 minMachine Learning Guide
Machine Learning GuideMLA 020 Kubeflow and ML Pipeline Orchestration on Kubernetes Machine learning pipeline orchestration tools, such as SageMaker and Kubeflow, streamline the end-to-end process of data ingestion, model training, deployment, and monitoring, with Kubeflow providing an open-source, cross-cloud platform built atop Kubernetes. Organizations typically choose between cloud-native managed services and open-source solutions based on required flexibility, scalability, integration with existing cloud environments, and vendor lock-in considerations. Links Notes and resources at ocdevel.com/mlg/mla-20 Try a walking desk stay healthy & sharp while you learn & code Dirk-Jan Verdoorn - Data Scientist at Dept Agency Managed vs. Open-Source ML Pipeline Orchestration Cloud providers such as AWS, Google Clo...
2022-01-291h 08Machine Learning Guide
Machine Learning GuideMLA 019 Cloud, DevOps & Architecture The deployment of machine learning models for real-world use involves a sequence of cloud services and architectural choices, where machine learning expertise must be complemented by DevOps and architecture skills, often requiring collaboration with professionals. Key concepts discussed include infrastructure as code, cloud container orchestration, and the distinction between DevOps and architecture, as well as practical advice for machine learning engineers wanting to deploy products securely and efficiently. Links Notes and resources at ocdevel.com/mlg/mla-19 Try a walking desk stay healthy & sharp while you learn & code ;## Translating Machine Learning Models to Production After developing an...
2022-01-131h 15Machine Learning Guide
Machine Learning GuideMLA 018 DescriptTry 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 R...
2021-11-0706 minMachine Learning Guide
Machine Learning GuideMLA 017 AWS Local Development Environment AWS development environments for local and cloud deployment can differ significantly, leading to extra complexity and setup during cloud migration. By developing directly within AWS environments, using tools such as Lambda, Cloud9, SageMaker Studio, client VPN connections, or LocalStack, developers can streamline transitions to production and leverage AWS-managed services from the start. This episode outlines three primary strategies for treating AWS as your development environment, details the benefits and tradeoffs of each, and explains the role of infrastructure-as-code tools such as Terraform and CDK in maintaining replicable, trackable cloud infrastructure. Links Notes and resources at ocdevel.com/mlg/m...
2021-11-061h 04Machine Learning Guide
Machine Learning GuideMLA 016 AWS SageMaker MLOps 2 SageMaker streamlines machine learning workflows by enabling integrated model training, tuning, deployment, monitoring, and pipeline automation within the AWS ecosystem, offering scalable compute options and flexible development environments. Cloud-native AWS machine learning services such as Comprehend and Poly provide off-the-shelf solutions for NLP, time series, recommendations, and more, reducing the need for custom model implementation and deployment. Links Notes and resources at ocdevel.com/mlg/mla-16 Try a walking desk stay healthy & sharp while you learn & code Model Training and Tuning with SageMaker SageMaker enables model training within integrated data and ML pipelines, drawing from components such as Da...
2021-11-051h 00Machine Learning Guide
Machine Learning GuideMLA 015 AWS SageMaker MLOps 1 SageMaker is an end-to-end machine learning platform on AWS that covers every stage of the ML lifecycle, including data ingestion, preparation, training, deployment, monitoring, and bias detection. The platform offers integrated tools such as Data Wrangler, Feature Store, Ground Truth, Clarify, Autopilot, and distributed training to enable scalable, automated, and accessible machine learning operations for both tabular and large data sets. Links Notes and resources at ocdevel.com/mlg/mla-15 Try a walking desk stay healthy & sharp while you learn & code Amazon SageMaker: The Machine Learning Operations Platform MLOps is deploying your ML models to the cloud. Se...
2021-11-0447 minMachine Learning Guide
Machine Learning GuideMLA 014 Machine Learning Hosting and Serverless Deployment Machine learning model deployment on the cloud is typically handled with solutions like AWS SageMaker for end-to-end training and inference as a REST endpoint, AWS Batch for cost-effective on-demand batch jobs using Docker containers, and AWS Lambda for low-usage, serverless inference without GPU support. Storage and infrastructure options such as AWS EFS are essential for managing large model artifacts, while new tools like Cortex offer open source alternatives with features like cost savings and scale-to-zero for resource management. Links Notes and resources at ocdevel.com/mlg/mla-14 Try a walking desk stay healthy & sharp while you learn & code Cl...
2021-01-1852 minMachine Learning Guide
Machine Learning GuideMLA 013 Tech Stack for Customer-Facing Machine Learning Products Primary technology recommendations for building a customer-facing machine learning product include React and React Native for the front end, serverless platforms like AWS Amplify or GCP Firebase for authentication and basic server/database needs, and Postgres as the relational database of choice. Serverless approaches are encouraged for scalability and security, with traditional server frameworks and containerization recommended only for advanced custom backend requirements. When serverless options are inadequate, use Node.js with Express or FastAPI in Docker containers, and consider adding Redis for in-memory sessions and RabbitMQ or SQS for job queues, though many of these functions can be...
2021-01-0347 minMachine Learning Guide
Machine Learning GuideMLA 012 Docker for Machine Learning Workflows Docker enables efficient, consistent machine learning environment setup across local development and cloud deployment, avoiding many pitfalls of virtual machines and manual dependency management. It streamlines system reproduction, resource allocation, and GPU access, supporting portability and simplified collaboration for ML projects. Machine learning engineers benefit from using pre-built Docker images tailored for ML, allowing seamless project switching, host OS flexibility, and straightforward deployment to cloud platforms like AWS ECS and Batch, resulting in reproducible and maintainable workflows. Links Notes and resources at ocdevel.com/mlg/mla-12 Try a walking desk stay healthy & sharp while you learn & code Traditional En...
2020-11-0931 minMachine Learning Guide
Machine Learning GuideMLG 032 Cartesian Similarity Metrics Try a walking desk to stay healthy while you study or work! 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 triangl...
2020-11-0841 minMachine Learning Guide
Machine Learning GuideMLA 011 Practical Clustering Tools Primary clustering tools for practical applications include K-means using scikit-learn or Faiss, agglomerative clustering leveraging cosine similarity with scikit-learn, and density-based methods like DBSCAN or HDBSCAN. For determining the optimal number of clusters, silhouette score is generally preferred over inertia-based visual heuristics, and it natively supports pre-computed distance matrices. Links Notes and resources at ocdevel.com/mlg/mla-11 Try a walking desk stay healthy & sharp while you learn & code K-means Clustering K-means is the most widely used clustering algorithm and is typically the first method to try for general clustering tasks. The scikit-learn KMeans implementation is suitable for smal...
2020-11-0834 minMachine Learning Guide
Machine Learning GuideMLA 010 NLP packages: transformers, spaCy, Gensim, NLTK The landscape of Python natural language processing tools has evolved from broad libraries like NLTK toward more specialized packages such as Gensim for topic modeling, SpaCy for linguistic analysis, and Hugging Face Transformers for advanced tasks, with Sentence Transformers extending transformer models to enable efficient semantic search and clustering. Each library occupies a distinct place in the NLP workflow, from fundamental text preprocessing to semantic document comparison and large-scale language understanding. Links Notes and resources at ocdevel.com/mlg/mla-10 Try a walking desk stay healthy & sharp while you learn & code Historical Foundation: NLTK NLTK ("Natural Language Toolkit") was...
2020-10-2826 minMachine Learning Guide
Machine Learning Guide031 The Podcasts ReturnThe podcasts return with new content, especially about NLP: BERT, transformers, spaCy, Gensim, NLTK. Accompanied by a community project - Gnothi, a journal that uses AI to provide insights and resources. Website https://gnothiai.com, project https://github.com/lefnire/gnothi. Share the website on social media and email me a link/screenshot for free access to Machine Learning Applied for 3 months; contribute to the Github repository for free access for life.
2020-10-2807 minMachine Learning Guide
Machine Learning GuideMLA 009 Charting and Visualization Tools for Data Science Python charting libraries - Matplotlib, Seaborn, and Bokeh - explaining, their strengths from quick EDA to interactive, HTML-exported visualizations, and clarifies where D3.js fits as a JavaScript alternative for end-user applications. It also evaluates major software solutions like Tableau, Power BI, QlikView, and Excel, detailing how modern BI tools now integrate drag-and-drop analytics with embedded machine learning, potentially allowing business users to automate entire workflows without coding. Links Notes and resources at ocdevel.com/mlg/mla-9 Try a walking desk stay healthy & sharp while you learn & code Core Phases in Data Science Visualization Exploratory Data Analysis (EDA): ED...
2018-11-0624 minMachine Learning Guide
Machine Learning GuideMLA 008 Exploratory Data Analysis (EDA) Exploratory data analysis (EDA) sits at the critical pre-modeling stage of the data science pipeline, focusing on uncovering missing values, detecting outliers, and understanding feature distributions through both statistical summaries and visualizations, such as Pandas' info(), describe(), histograms, and box plots. Visualization tools like Matplotlib, along with processes including imputation and feature correlation analysis, allow practitioners to decide how best to prepare, clean, or transform data before it enters a machine learning model. Links Notes and resources at ocdevel.com/mlg/mla-8 Try a walking desk stay healthy & sharp while you learn & code EDA in the Data Science Pi...
2018-10-2625 minMachine Learning Guide
Machine Learning GuideMLA 007 Jupyter Notebooks Jupyter Notebooks, originally conceived as IPython Notebooks, enable data scientists to combine code, documentation, and visual outputs in an interactive, browser-based environment supporting multiple languages like Python, Julia, and R. This episode details how Jupyter Notebooks structure workflows into executable cells - mixing markdown explanations and inline charts - which is essential for documenting, demonstrating, and sharing data analysis and machine learning pipelines step by step. Links Notes and resources at ocdevel.com/mlg/mla-7 Try a walking desk stay healthy & sharp while you learn & code Overview of Jupyter Notebooks Historical Context and Scope Jupyter Notebooks be...
2018-10-1616 minMachine Learning Guide
Machine Learning GuideMLA 006 Salaries for Data Science & Machine Learning O'Reilly's 2017 Data Science Salary Survey finds that location is the most significant salary determinant for data professionals, with median salaries ranging from $134,000 in California to under $30,000 in Eastern Europe, and highlights that negotiation skills can lead to salary differences as high as $45,000. Other key factors impacting earnings include company age and size, job title, industry, and education, while popular tools and languages—such as Python, SQL, and Spark—do not strongly influence salary despite widespread use. Links Notes and resources at ocdevel.com/mlg/mla-6 Try a walking desk stay healthy & sharp while you learn & code Global and Region...
2018-07-1919 minMachine Learning Guide
Machine Learning GuideMLA 005 Shapes and Sizes: Tensors and NDArrays Explains the fundamental differences between tensor dimensions, size, and shape, clarifying frequent misconceptions—such as the distinction between the number of features (“columns”) and true data dimensions—while also demystifying reshaping operations like expand_dims, squeeze, and transpose in NumPy. Through practical examples from images and natural language processing, listeners learn how to manipulate tensors to match model requirements, including scenarios like adding dummy dimensions for grayscale images or reordering axes for sequence data. Links Notes and resources at ocdevel.com/mlg/mla-5 Try a walking desk stay healthy & sharp while you learn & code Definitions Tensor: A general te...
2018-06-0927 minMachine Learning Guide
Machine Learning Guide030 New Series: Machine Learning AppliedMLG: I'm rebooting this series to fix mistakes & add more shallows (Bayesian methods, Tree methods, etc). I'm adding Patreon rewards, including access to a new podcast series: Machine Learning Applied, discussing applied/practical 10-20m frequent episodes. ocdevel.com/mlg/30 for notes and resources
2018-05-2500 minMachine Learning Guide
Machine Learning GuideMLA 003 Storage: HDF, Pickle, Postgres Practical workflow of loading, cleaning, and storing large datasets for machine learning, moving from ingesting raw CSVs or JSON files with pandas to saving processed datasets and neural network weights using HDF5 for efficient numerical storage. It clearly distinguishes among storage options—explaining when to use HDF5, pickle files, or SQL databases—while highlighting how libraries like pandas, TensorFlow, and Keras interact with these formats and why these choices matter for production pipelines. Links Notes and resources at ocdevel.com/mlg/mla-3 Try a walking desk stay healthy & sharp while you learn & code Data Ingestion and Preprocessing Data S...
2018-05-2417 minMachine Learning Guide
Machine Learning GuideMLA 002 Numpy & Pandas NumPy enables efficient storage and vectorized computation on large numerical datasets in RAM by leveraging contiguous memory allocation and low-level C/Fortran libraries, drastically reducing memory footprint compared to native Python lists. Pandas, built on top of NumPy, introduces labelled, flexible tabular data manipulation—facilitating intuitive row and column operations, powerful indexing, and seamless handling of missing data through tools like alignment, reindexing, and imputation. Links Notes and resources at ocdevel.com/mlg/mla-2 Try a walking desk stay healthy & sharp while you learn & code NumPy: Efficient Numerical Arrays and Vectorized Computation Purpose and Design NumPy ("Nume...
2018-05-2418 minMachine Learning Guide
Machine Learning GuideMLA 001 Degrees, Certificates, and Machine Learning Careers While industry-respected credentials like Udacity Nanodegrees help build a practical portfolio for machine learning job interviews, they remain insufficient stand-alone qualifications—most roles require a Master’s degree as a near-hard requirement, especially compared to more flexible web development fields. A Master’s, such as Georgia Tech’s OMSCS, not only greatly increases employability but is strongly recommended for those aiming for entry into machine learning careers, while a PhD is more appropriate for advanced, research-focused roles with significant time investment. Links Notes and resources at ocdevel.com/mlg/mla-1 Online Certificates: Usefulness and Limitations Udacity Nanodegree Provi...
2018-05-2411 minMachine Learning Guide
Machine Learning GuideMLG 029 Reinforcement Learning IntroNotes and resources:  ocdevel.com/mlg/29  Try a walking desk to stay healthy while you study or work! Reinforcement Learning (RL) is a fundamental component of artificial intelligence, different from purely being AI itself. It is considered a key aspect of AI due to its ability to learn through interactions with the environment using a system of rewards and punishments. Links: openai/baselines reinforceio/tensorforce NervanaSystems/coach rll/rllab Differential Computers Concepts and Definitions Reinforcement Learning (RL): RL is a framework where an "agent" learns by interacting with its environment and...
2018-02-0543 minMachine Learning Guide
Machine Learning GuideMLG 028 Hyperparameters 2Notes and resources:  ocdevel.com/mlg/28  Try a walking desk to stay healthy while you study or work! More hyperparameters for optimizing neural networks. A focus on regularization, optimizers, feature scaling, and hyperparameter search methods. Hyperparameter Search Techniques Grid Search involves testing all possible permutations of hyperparameters, but is computationally exhaustive and suited for simpler, less time-consuming models. Random Search selects random combinations of hyperparameters, potentially saving time while potentially missing the optimal solution. Bayesian Optimization employs machine learning to continuously update and hone in on efficient hyperparameter combinations, avoiding the exhaustive or ra...
2018-02-0451 minMachine Learning Guide
Machine Learning GuideMLG 027 Hyperparameters 1Full notes and resources at  ocdevel.com/mlg/27  Try a walking desk to stay healthy while you study or work! Hyperparameters are crucial elements in the configuration of machine learning models. Unlike parameters, which are learned by the model during training, hyperparameters are set by humans before the learning process begins. They are the knobs and dials that humans can control to influence the training and performance of machine learning models. Definition and Importance Hyperparameters differ from parameters like theta in linear and logistic regression, which are learned weights. They are ch...
2018-01-2847 minMachine Learning Guide
Machine Learning GuideMLG 026 Project Bitcoin Trader Try a walking desk to stay healthy while you study or work! Ful notes and resources at  ocdevel.com/mlg/26  NOTE. This episode is no longer relevant, and tforce_btc_trader no longer maintained. The current podcast project is Gnothi. Episode Overview TForce BTC Trader Project: Trading Crypto Special: Intuitively highlights decisions: hypers, supervised v reinforcement, LSTM v CNN Crypto (v stock) Bitcoin, Ethereum, Litecoin, Ripple Many benefits (immutable permenant distributed ledger; security; low fees; international; etc) For our purposes: popular, volatile, singular Singular like Forex vs Stock (instruments) Trading ba...
2018-01-2738 minMy JavaScript Story
My JavaScript StoryMJS 039: Tyler RenellePanel:  Charles Max Wood Guest: Tyler Renelle This week on My JavaScript Story, Charles speaks with Tyler Renelle. Tyler is a contractor and developer who has worked in many web technologies like Angular, Rails, React and much more! Tyler is a return guest, previously on Adventure in Angular and JavaScript Jabber talking Ionic and Machine learning. Tyler has recently expanded his work beyond JavaScript and is on the show to talk his interest in AI or Artificial intelligence and Machine Learning. Furthermore, Tyler talks about his early journey as a game developer, web developer, and work with some content management s...
2017-12-1347 minMachine Learning Guide
Machine Learning GuideMLG 025 Convolutional Neural Networks Try a walking desk to stay healthy while you study or work! Notes and resources at  ocdevel.com/mlg/25  Filters and Feature Maps: Filters are small matrices used to detect visual features from an input image by applying them to local pixel patches, creating a 3D output called a feature map. Each filter is tasked with recognizing a specific pattern (e.g., edges, textures) in the input images. Convolutional Layers: The filter is applied across the image to produce an output which is the feature map. A convolutional layer is composed of several feat...
2017-10-3044 minMachine Learning Guide
Machine Learning GuideMLG 024 Tech Stack Try a walking desk to stay healthy while you study or work! Notes and resources at  ocdevel.com/mlg/24  Hardware Desktop if you're stationary, as you'll get the best performance bang-for-buck and improved longevity; laptop if you're mobile. Desktops. Build your own PC, better value than pre-built. See PC Part Picker, make sure to use an Nvidia graphics card. Generally shoot for 2nd-best of CPUs/GPUs. Eg, RTX 4070 currently (2024-01); better value-to-price than 4080+. For laptops, see this post (updated). OS / Software Use Linux (I prefer Ubun...
2017-10-071h 01Machine Learning Guide
Machine Learning GuideMLG 023 Deep NLP 2 Try a walking desk to stay healthy while you study or work! Notes and resources at  ocdevel.com/mlg/23  Neural Network Types in NLP Vanilla Neural Networks (Feedforward Networks): Used for general classification or regression tasks. Examples include predicting housing costs or classifying images as cat, dog, or tree. Convolutional Neural Networks (CNNs): Primarily used for image-related tasks. Recurrent Neural Networks (RNNs): Used for sequence-based tasks such as weather predictions, stock market predictions, and natural language processing. Differ from feedforward networks as they loop back onto previous steps to...
2017-08-2143 minMachine Learning Guide
Machine Learning GuideMLG 022 Deep NLP 1 Try a walking desk to stay healthy while you study or work! Notes and resources at  ocdevel.com/mlg/22  Deep NLP Fundamentals Deep learning has had a profound impact on natural language processing by introducing models like recurrent neural networks (RNNs) that are specifically adept at handling sequential data. Unlike traditional linear models like linear regression, RNNs can address the complexities of language which appear from its inherent non-linearity and hierarchy. These models are able to learn complex features by combining data in multiple layers, which has revolutionized areas like sentiment analysis, machine tr...
2017-07-2949 minMachine Learning Guide
Machine Learning Guide021 New Series: Machine Learning AppliedIntroducing a new podcast series on Patreon: Machine Learning Applied. ocdevel.com/mlg/21 for notes and resources
2017-07-2800 minMachine Learning Guide
Machine Learning GuideMLG 020 Natural Language Processing 3 Try a walking desk to stay healthy while you study or work! Notes and resources at  ocdevel.com/mlg/20  NLP progresses through three main layers: text preprocessing, syntax tools, and high-level goals, each building upon the last to achieve complex linguistic tasks. Text Preprocessing Text preprocessing involves essential steps such as tokenization, stemming, and stop word removal. These foundational tasks clean and prepare text for further analysis, ensuring that subsequent processes can be applied more effectively. Syntax Tools Syntax tools are crucial for understanding grammatical structures within te...
2017-07-2440 minRuby Rogues
Ruby RoguesRR 319 Machine Learning with Tyler RenelleRR 319 Machine Learning with Tyler RenelleThis episode of the Ruby Rogues Panel features panelists Charles Max Wood and Dave Kimura. Tyler Renelle, who stops by to talk about machine learning, joins them as a guest. Tyler is the first guest to talk on Adventures in Angular, JavaScript Jabber, and Ruby Rogues. Tune in to find out more about Tyler and machine learning!What is machine learning?Machine learning is a different concept than programmers are used to.There are three phases in computing technology.First phase – building computers in the first place but it was hard coded onto the physical co...
2017-07-1849 minMachine Learning Guide
Machine Learning GuideMLG 019 Natural Language Processing 2 Try a walking desk to stay healthy while you study or work! Notes and resources at  ocdevel.com/mlg/19  Classical NLP Techniques: Origins and Phases in NLP History: Initially reliant on hardcoded linguistic rules, NLP's evolution significantly pivoted with the introduction of machine learning, particularly shallow learning algorithms, leading eventually to deep learning, which is the current standard. Importance of Classical Methods: Knowing traditional methods is still valuable, providing a historical context and foundation for understanding NLP tasks. Traditional methods can be advantageous with small datasets or limited compute power. ...
2017-07-111h 05Machine Learning Guide
Machine Learning GuideMLG 018 Natural Language Processing 1 Try a walking desk to stay healthy while you study or work! Full notes at  ocdevel.com/mlg/18  Overview: Natural Language Processing (NLP) is a subfield of machine learning that focuses on enabling computers to understand, interpret, and generate human language. It is a complex field that combines linguistics, computer science, and AI to process and analyze large amounts of natural language data. NLP Structure NLP is divided into three main tiers: parts, tasks, and goals. 1. Parts Text Pre-processing: Tokenization: Splitting text into words or tokens. Stop Words Removal: Eliminating com...
2017-06-2658 minMachine Learning Guide
Machine Learning GuideMLG 017 Checkpoint Try a walking desk to stay healthy while you study or work! At this point, browse #importance:essential on ocdevel.com/mlg/resources with the 45m/d ML, 15m/d Math breakdown.
2017-06-0408 minMachine Learning Guide
Machine Learning Guide17. CheckpointCheckpoint - learn the material offline! 45m/d ML - Coursera (https://www.coursera.org/learn/machine-learning) `course:hard` - Python (http://amzn.to/2mVgtJW) `book:medium` - Deep Learning Resources (http://ocdevel.com/podcasts/machine-learning/9) - Go deeper on shallow algos ** Elements of Statistical Learning (http://amzn.to/2tWW8He) `book:hard` ** Pattern Recognition and Machine Learning (http://amzn.to/2sDIIfb) (Free PDF? (https://goo.gl/aX038j)) `book:hard` 15m/d Math - Either LinAlg (https://www.khanacademy.org/math/linear-algebra) `course:medium` OR Fast.ai (http://www.fast.ai/2017/07/17/num-lin-alg/) `course:medium` - Stats (https://www.khanacademy...
2017-06-0406 minMachine Learning Guide
Machine Learning GuideMLG 016 Consciousness Try a walking desk to stay healthy while you study or work! Full notes at  ocdevel.com/mlg/16  Inspiration in AI Development Early inspirations for AI development centered around solving challenging problems, but recent advancements like self-driving cars and automated scientific discoveries attract professionals due to potential economic automation and career opportunities. The Singularity The singularity suggests exponential technological growth leading to a point where AI and robotics automate all technology development, potentially achieving 'seed AI' capable of self-improvement and escaping human intervention. Defining Consciousness Co...
2017-05-211h 14Machine Learning Guide
Machine Learning Guide16. ConsciousnessCan AI be conscious? ## Resources - Philosophy of Mind: Brains, Consciousness, and Thinking Machines (Audible (http://amzn.to/2kQGgk5), TGC (https://goo.gl/fDteyi)) `audio:easy` ## Episode Inspirations for AI - economic automation - singularity - consciousness Definitinitions - cogsci: neuroscience, neuro-x(biology, physiology, computational __, etc), psychology, philosophy, AI ** computational neuroscience => perceptron ** frank rosenblatt, warren McCulloche, walter pitts - all brain guys (neurobiology, neurophysiology, computational neuroscience respectively) - intelligence (computation) vs consciousness (soul); intelligence in scale (animals); brain in scale; consciousness in scale? - perception, self-identity, memory, attention; (self reflection is just a human-special component) - awereness (qualia / sentience / subjective...
2017-05-211h 14Machine Learning Guide
Machine Learning Guide15. PerformancePerformance evaluation & improvement ## Episode Performance evaluation - Performance measures: accuracy, precision, recall, F1/F2 score - Cross validation: split your data into train, validation, test sets - Training set is for training your algorithm - Validation set is to test your algorithm's performance. It can be used to inform changing your model (ie, hyperparameters) - Test set is used for your final score. It can't be used to inform changing your model. Performance improvement - Modify hyperpamaraters - Data: collect more, fill in missing cells, normalize fields - Regularize: reduce overfitting (high variance) and underfitting (high bias)
2017-05-0741 minMachine Learning Guide
Machine Learning GuideMLG 015 Performance Try a walking desk to stay healthy while you study or work! Full notes at  ocdevel.com/mlg/15  Concepts Performance Evaluation Metrics: Tools to assess how well a machine learning model performs tasks like spam classification, housing price prediction, etc. Common metrics include accuracy, precision, recall, F1/F2 scores, and confusion matrices. Accuracy: The simplest measure of performance, indicating how many predictions were correct out of the total. Precision and Recall: Precision: The ratio of true positive predictions to the total positive predictions made by the model (how often your positive predictions were correct). Re...
2017-05-0742 minMachine Learning Guide
Machine Learning Guide14. Shallow Algos 3Speed run of Anomaly Detection, Recommenders(Content Filtering vs Collaborative Filtering), and Markov Chain Monte Carlo (MCMC) ## Resources - Andrew Ng Week 9 (https://www.coursera.org/learn/machine-learning/resources/szFCa) ## Episode - Anomoly Detection algorithm - Recommender Systems (Content Filtering, Collaborative Filtering) - Markov Chains & Monte Carlo
2017-04-2348 minMachine Learning Guide
Machine Learning GuideMLG 014 Shallow Algos 3 Try a walking desk to stay healthy while you study or work! Full notes at ocdevel.com/mlg/14 Anomaly Detection Systems Applications: Credit card fraud detection and server activity monitoring. Concept: Identifying outliers on a bell curve. Statistics: Central role of the Gaussian distribution (normal distribution) in detecting anomalies. Process: Identifying significant deviations from the mean to detect outliers. Recommender Systems Types: Content Filtering: Uses features of items (e.g., Pandora’s Music Genome Project). Collaborative Filtering: Based on user behavior and preferences, like "Users Also Liked" model utilized in platforms like Ne...
2017-04-2348 minMachine Learning Guide
Machine Learning Guide13. Shallow Algos 2Speed run of Support Vector Machines (SVMs) and Naive Bayes Classifier. ## Resources - Andrew Ng Week 7 (https://www.coursera.org/learn/machine-learning/resources/Es9Qo) - Hands-On Machine Learning with Scikit-Learn and TensorFlow (http://amzn.to/2tVdIXN) `book:medium` (replaced R book) - Mathematical Decision Making (https://goo.gl/V75I49) `audio|course:hard` course on "Operations Research", similar to ML - Which algo to use? ** Pros/cons table for algos (https://blog.recast.ai/machine-learning-algorithms/2/) `picture` ** Decision tree of algos (http://scikit-learn.org/stable/tutorial/machine_learning_map/) `picture` ## Episode - Support Vector Machines (SVM) - Naive Bayes Classifier
2017-04-0955 minMachine Learning Guide
Machine Learning GuideMLG 013 Shallow Algos 2 Try a walking desk to stay healthy while you study or work! Full notes at  ocdevel.com/mlg/13  Support Vector Machines (SVM) Purpose: Classification and regression. Mechanism: Establishes decision boundaries with maximum margin. Margin: The thickness of the decision boundary, large margin minimizes overfitting. Support Vectors: Data points that the margin directly affects. Kernel Trick: Projects non-linear data into higher dimensions to find a linear decision boundary. Naive Bayes Classifiers Framework: Based on Bayes' Theorem, applies conditional probability. Naive Assumption: Assumes feature independence to simplify computation. Application: Effective for text classification using a...
2017-04-0955 minMachine Learning Guide
Machine Learning Guide12. Shallow Algos 1Speed-run of some shallow algorithms: K Nearest Neighbors (KNN); K-means; Apriori; PCA; Decision Trees ## Resources - Andrew Ng Week 8 (https://www.coursera.org/learn/machine-learning/resources/kGWsY) - Tour of Machine Learning Algorithms (http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms) `article:easy` - Elements of Statistical Learning (http://amzn.to/2tWW8He) `book:hard` - Pattern Recognition and Machine Learning (http://amzn.to/2sDIIfb) (Free PDF? (https://goo.gl/aX038j)) `book:hard` - Hands-On Machine Learning with Scikit-Learn and TensorFlow (http://amzn.to/2tVdIXN) `book:medium` (replaced R book) - Which algo to use? ** Pros/cons table for algos (https://blog.recast...
2017-03-1953 minMachine Learning Guide
Machine Learning GuideMLG 012 Shallow Algos 1 Try a walking desk to stay healthy while you study or work! Full notes at ocdevel.com/mlg/12  Topics Shallow vs. Deep Learning: Shallow learning can often solve problems more efficiently in time and resources compared to deep learning. Supervised Learning: Key algorithms include linear regression, logistic regression, neural networks, and K Nearest Neighbors (KNN). KNN is unique as it is instance-based and simple, categorizing new data based on proximity to known data points. Unsupervised Learning: Clustering (K Means): Differentiates data points into clusters with no predefined labels, e...
2017-03-1953 minMachine Learning Guide
Machine Learning Guide11. CheckpointCheckpoint - start learning the material offline! 45m/d ML - Coursera (https://www.coursera.org/learn/machine-learning) `course:hard` - Python (http://amzn.to/2mVgtJW) `book:medium` - Deep Learning Resources (http://ocdevel.com/podcasts/machine-learning/9) 15m/d Math (KhanAcademy) - Either LinAlg (https://www.khanacademy.org/math/linear-algebra) `course:medium` OR Fast.ai (http://www.fast.ai/2017/07/17/num-lin-alg/) `course:medium` - Stats (https://www.khanacademy.org/math/statistics-probability) `course:medium` - Calc (https://www.khanacademy.org/math/calculus-home) `course:medium` Audio - The Master Algorithm (http://amzn.to/2kLOQjW) `audio:medium` Semi-technical overview of ML basics & main algorithms ...
2017-03-0807 minMachine Learning Guide
Machine Learning Guide011 CheckpointCheckpoint - start learning the material offline! ocdevel.com/mlg/11 for notes and resources
2017-03-0800 minMachine Learning Guide
Machine Learning Guide10. Languages & FrameworksLanguages & frameworks comparison. Languages: Python, R, MATLAB/Octave, Julia, Java/Scala, C/C++. Frameworks: Hadoop/Spark, Deeplearning4J, Theano, Torch, TensorFlow. ## Resources - Python (http://amzn.to/2mVgtJW) `book:medium` - Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython 2nd Edition (https://amzn.to/2IHFh2m) `book:easy` - TensorFlow Tutorials (https://www.tensorflow.org/get_started/get_started) `tutorial:medium` - Hands-On Machine Learning with Scikit-Learn and TensorFlow (http://amzn.to/2tVdIXN) `book:medium` ## Episode Languages - C/C++ ** Performance ** GPU (CUDA/cuDNN) - Math Langs ** R ** MATLAB / Octave ** Julia - Java / Scala ** Data mining ** Hadoop +...
2017-03-0744 minMachine Learning Guide
Machine Learning GuideMLG 010 Languages & Frameworks Try a walking desk to stay healthy while you study or work! Full notes at  ocdevel.com/mlg/10  Topics: Recommended Languages and Frameworks: Python and TensorFlow are top recommendations for machine learning. Python's versatile libraries (NumPy, Pandas, Scikit-Learn) enable it to cover all areas of data science including data mining, analytics, and machine learning. Language Choices: C/C++: High performance, suitable for GPU optimization but not recommended unless already familiar. Math Languages (R, MATLAB, Octave, Julia): Optimized for mathematical operations, particularly R preferred for data analytics. JVM Languages (Java, Scala): Suite...
2017-03-0744 minMachine Learning Guide
Machine Learning Guide9. Deep LearningDeep learning and neural networks. How to stack our logisitic regression units into a multi-layer perceptron. ## Resources - Overview: ** Deep Learning Simplified (https://www.youtube.com/watch?v=b99UVkWzYTQ) `video:easy` quick series to get a lay-of-the-land. - Quickstart: ** TensorFlow Tutorials (https://www.tensorflow.org/get_started/get_started) `tutorial:medium` - Deep-dive code (pick one): ** Fast.ai (http://course.fast.ai/) `course:medium` practical DL for coders ** Hands-On Machine Learning with Scikit-Learn and TensorFlow (http://amzn.to/2tVdIXN) `book:medium` - Deep-dive theory: ** Deep Learning Book (http://amzn.to/2tXgCiT) (Free HTML version (http://www.deeplearningbook.org/)) `book...
2017-03-0451 minMachine Learning Guide
Machine Learning GuideMLG 009 Deep Learning Try a walking desk to stay healthy while you study or work! Full notes at ocdevel.com/mlg/9  Key Concepts: Deep Learning vs. Shallow Learning: Machine learning is broken down hierarchically into AI, ML, and subfields like supervised/unsupervised learning. Deep learning is a specialized area within supervised learning distinct from shallow learning algorithms like linear regression. Neural Networks: Central to deep learning, artificial neural networks include models like multilayer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs). Neural networks are composed of interconnected units or "neurons," which are mathematical representations ins...
2017-03-0451 minMachine Learning Guide
Machine Learning Guide8. MathIntroduction to the branches of mathematics used in machine learning. Linear algebra, statistics, calculus. ## Resources Come back here after you've finished Ng's course; or learn these resources in tandem with ML (say 1 day a week). Primers (PDFs) - See "Section Notes" of cs229 (http://cs229.stanford.edu/materials.html) `handout:medium` KhanAcademy: - Either LinAlg (https://www.khanacademy.org/math/linear-algebra) `course:medium` OR Fast.ai (http://www.fast.ai/2017/07/17/num-lin-alg/) `course:medium` - Stats (https://www.khanacademy.org/math/statistics-probability) `course:medium` - Calc (https://www.khanacademy.org/math/calculus-home) `course:medium` Books - Introduction to Linear Algebra (https...
2017-02-2327 minMachine Learning Guide
Machine Learning GuideMLG 008 Math for Machine Learning Mathematics essential for machine learning includes linear algebra, statistics, and calculus, each serving distinct purposes: linear algebra handles data representation and computation, statistics underpins the algorithms and evaluation, and calculus enables the optimization process. It is recommended to learn the necessary math alongside or after starting with practical machine learning tasks, using targeted resources as needed. In machine learning, linear algebra enables efficient manipulation of data structures like matrices and tensors, statistics informs model formulation and error evaluation, and calculus is applied in training models through processes such as gradient descent for optimization. Links Notes and resources at o...
2017-02-2328 minMachine Learning Guide
Machine Learning GuideMLG 007 Logistic Regression The logistic regression algorithm is used for classification tasks in supervised machine learning, distinguishing items by class (such as "expensive" or "not expensive") rather than predicting continuous numerical values. Logistic regression applies a sigmoid or logistic function to a linear regression model to generate probabilities, which are then used to assign class labels through a process involving hypothesis prediction, error evaluation with a log likelihood function, and parameter optimization using gradient descent. Links Notes and resources at ocdevel.com/mlg/7 Try a walking desk - stay healthy & sharp while you learn & code Classification versus Regression in Supervised Learning Su...
2017-02-1935 minMachine Learning Guide
Machine Learning Guide7. Logistic RegressionYour first classifier: Logistic Regression. That plus Linear Regression, and you're a 101 supervised learner! ## Resources You've started Ng's Coursera course (https://www.coursera.org/learn/machine-learning), right? Riight? ## Episode See Andrew Ng Week 3 Lecture Notes (https://www.coursera.org/learn/machine-learning/resources/Zi29t)
2017-02-1934 minMachine Learning Guide
Machine Learning Guide6. Certificates & DegreesDiscussion on certificates and degrees from Udacity to a Masters degree. ## Resources - Discussions: 1 (http://canyon289.github.io/DSGuide.html#DSGuide) 2 (https://news.ycombinator.com/item?id=13654127) 3 (http://cole-maclean.github.io/blog/Self%20Taught%20AI/) 4 (https://news.ycombinator.com/item?id=12516441) ## Episode Self-edify - Coursera Specialization - flat $500 - Udacity Nanodegree - $200/m (discount if timely completion) ** Great for self-teaching, not recognized degree ** Machine Learning (https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009) ** Self Driving Car (https://www.udacity.com/drive) ** Artificial Intelligence (https://www.udacity.com/ai) OMSCS (https://www.omscs.gatech.edu/): Great & cheap online masters degree Portfolio: Most important...
2017-02-1715 minMachine Learning Guide
Machine Learning GuideMLG 006 Certificates & Degrees People interested in machine learning can choose between self-guided learning, online certification programs such as MOOCs, accredited university degrees, and doctoral research, with industry acceptance and personal goals influencing which path is most appropriate. Industry employers currently prioritize a strong project portfolio over non-accredited certificates, and while master’s degrees carry more weight for job applications, PhD programs are primarily suited for research interests rather than industry roles. Links Notes and resources at ocdevel.com/mlg/6 Try a walking desk - stay healthy & sharp while you learn & code Learner Types and Self-Guided Education Individuals interested in machine learning may...
2017-02-1716 minMachine Learning Guide
Machine Learning Guide5. Linear RegressionIntroduction to the first machine-learning algorithm, the 'hello world' of supervised learning - Linear Regression ## Resources - Andrew Ng's Machine Learning Coursera course (https://www.coursera.org/learn/machine-learning) `course:hard` No question, the most essential, important, recommended resource in my entire series _period_. Consider it required, not optional. ## Episode See Andrew Ng Week 2 Lecture Notes (https://www.coursera.org/learn/machine-learning/resources/QQx8l)
2017-02-1633 minMachine Learning Guide
Machine Learning GuideMLG 005 Linear Regression Linear regression is introduced as the foundational supervised learning algorithm for predicting continuous numeric values, using cost estimation of Portland houses as an example. The episode explains the three-step process of machine learning - prediction via a hypothesis function, error calculation with a cost function (mean squared error), and parameter optimization through gradient descent - and details both the univariate linear regression model and its extension to multiple features. Links Notes and resources at ocdevel.com/mlg/5 Try a walking desk - stay healthy & sharp while you learn & code Linear Regression Overview of Machine Learning Structure Machine learning i...
2017-02-1634 minMachine Learning Guide
Machine Learning GuideMLG 004 Algorithms - Intuition Machine learning consists of three steps: prediction, error evaluation, and learning, implemented by training algorithms on large datasets to build models that can make decisions or classifications. The primary categories of machine learning algorithms are supervised, unsupervised, and reinforcement learning, each with distinct methodologies for learning from data or experience. Links Notes and resources at ocdevel.com/mlg/4 Try a walking desk stay healthy & sharp while you learn & code The Role of Machine Learning in Artificial Intelligence Artificial intelligence includes subfields such as reasoning, knowledge representation, search, planning, and learning. Learning connects to other AI subfields by enabling sy...
2017-02-1223 minMachine Learning Guide
Machine Learning Guide4. Algorithms - IntuitionOverview of machine learning algorithms. Infer/predict -> error/loss -> train/learn. Supervised, unsupervised, reinforcement learning. ## Resources - Tour of Machine Learning Algorithms (http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms) `article:easy` - The Master Algorithm (http://amzn.to/2kLOQjW) `audio:medium` Semi-technical overview of ML basics & main algorithms ## Episode Learning (ML) - 3-step process ** Infer / Predict ** Error / Loss ** Train / Learn - First as batch from spreadsheet, then "online" going forward ** Pre-train your "model" ** "Examples" ** "Weights" - Housing cost example ** "Features" ** Infer cost based on num_rooms, sq_foot, etc ** Error / Loss function Categories - Supervised learning ** Vision (CNN) ** Speech (RNN) ...
2017-02-1221 minMachine Learning Guide
Machine Learning GuideMLG 003 Inspiration AI is rapidly transforming both creative and knowledge-based professions, prompting debates on economic disruption, the future of work, the singularity, consciousness, and the potential risks associated with powerful autonomous systems. Philosophical discussions now focus on the socioeconomic impact of automation, the possibility of a technological singularity, the nature of machine consciousness, and the ethical considerations surrounding advanced artificial intelligence. Links Notes and resources at ocdevel.com/mlg/3 Try a walking desk stay healthy & sharp while you learn & code Automation of the Economy Artificial intelligence is increasingly capable of simulating intellectual tasks, leading to the replacement of not only re...
2017-02-1019 minMachine Learning Guide
Machine Learning Guide3. InspirationWhy should you care about AI? Inspirational topics about economic revolution, the singularity, consciousness, and fear. ## Resources - The Singularity Is Near (http://amzn.to/2lzCqKk) `audio:easy` - Philosophy of Mind: Brains, Consciousness, and Thinking Machines (Audible (http://amzn.to/2kQGgk5), TGC (https://goo.gl/fDteyi)) `audio:easy` - Superintelligence (http://amzn.to/2lzLcrL) `audio:easy` doom-and-gloom favorite of Musk, Gates, Hawking. ## Episode Economics / Automation - Mental automation (Tax prep; x-rays, surgeons; cars; law; programmers, designers, logos; music, art) - Is your job safe? (http://www.bbc.com/news/technology-34066941) - Universal basic income Singularity (AGI; Singularity; Next stage...
2017-02-1017 minMachine Learning Guide
Machine Learning Guide2. What is AI / MLWhat is artificial intelligence and machine learning? What's the difference? How about compared to statistics and data science? AI history. ## Resources - Wikipedia:AI (https://en.wikipedia.org/wiki/Artificial_intelligence) `article:easy` - The Quest for Artificial Intelligence (http://amzn.to/2kRd4Ie) (Free PDF? (http://ai.stanford.edu/~nilsson/QAI/qai.pdf)) `book:hard` AI history - Machines of Loving Grace (http://amzn.to/2kRcBWq) `audio:easy` AI history ## Episode What is AI? - Simulate any intellectual task - Goals ** Search / planning (eg chess) ** Reasoning / knowledge representation (eg Watson on Jeopardy) ** Perception ** Ability to move and manipulate objects ...
2017-02-0932 minMachine Learning Guide
Machine Learning Guide002 What is AI / MLWhat is artificial intelligence and machine learning? What's the difference? How about compared to statistics and data science? AI history. ocdevel.com/mlg/2 for notes and resources
2017-02-0900 minMachine Learning Guide
Machine Learning GuideMLG 002 Difference Between Artificial Intelligence, Machine Learning, Data Science Artificial intelligence is the automation of tasks that require human intelligence, encompassing fields like natural language processing, perception, planning, and robotics, with machine learning emerging as the primary method to recognize patterns in data and make predictions. Data science serves as the overarching discipline that includes artificial intelligence and machine learning, focusing broadly on extracting knowledge and actionable insights from data using scientific and computational methods. Links Notes and resources at ocdevel.com/mlg/2 Try a walking desk - stay healthy & sharp while you learn & code Track privacy-first web traffic with OCDevel Analytics. Data Science Overview Data science enc...
2017-02-091h 05Machine Learning Guide
Machine Learning GuideMLG 001 IntroductionShow notes: ocdevel.com/mlg/1. MLG teaches the fundamentals of machine learning and artificial intelligence. It covers intuition, models, 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. MLG, Resources Guide Gnothi (podcast project): website, Github What is this podcast? "Middle" level overview (deeper than a bird's eye view of machine learning; higher than math equations) No math/programming experience required Who is it for Any...
2017-02-0108 minMachine Learning Guide
Machine Learning Guide1. IntroductionIntroduction to the Machine Learning Guide Who am I: Tyler Renelle (https://www.linkedin.com/in/lefnire) What is this podcast? - "Middle" level overview (deeper than a bird's eye view of machine learning; higher than math equations) - No math/programming experience required Who is it for - Anyone curious about machine learning fundamentals - Aspiring machine learning developers (maybe transitioning from web/mobile development) Why audio? - Supplementary content for commute/exercise/chores will help solidify your book/course-work What it's not - News and Interviews ** TWiML and AI (https://twimlai.com) ** O'Reilly Data Show (https://www.oreilly...
2017-02-0112 minOCDevel Web Development Podcast
OCDevel Web Development PodcastPodcast 8: LinuxLinux, BSD, Solaris, Windows Server 2008, Server Operating Systems
2008-02-2700 minOCDevel Web Development Podcast
OCDevel Web Development PodcastPodcast 7: Internet MarketingSEO (search engine optimization), Internet Marketing, Monetization, Accessibility.
2008-01-2500 minOCDevel Web Development Podcast
OCDevel Web Development PodcastPodcast 6: CMS & FrameworksCMS (content management systems), Web frameworks, Ruby on Rails, Drupal, etc.
2008-01-2500 minOCDevel Web Development Podcast
OCDevel Web Development PodcastPodcast 5: ToolsWeb design & development tools & IDE's, Eclipse, Firefox plugins, Firebug, etc.
2008-01-2400 minOCDevel Web Development Podcast
OCDevel Web Development PodcastPodcast 4: Your First SiteInstalling your first site on a WAMP server (windows, apache, mysql, php). Making your site publically accessible.
2008-01-2400 minOCDevel Web Development Podcast
OCDevel Web Development PodcastPodcast 3: ClientClient Side Technology includes - DHTML - Flash, Silverlight, Applets - RIA Flash, Silverlight, Applets Define DHTML Code execution: Browser vs. Server DHTML - css - javascript and VBScript - embedded vs external - toolkits: Dojo, Moshikit, Scriptaculous, YUI, Prototype, JQuery, GWT... client-side vs server-side - speed: client way faster - Security: can view source (vs server-side scripting), ctrl+u - Compatibility: server-side produces same output no matter what, client-side has compatibility issues RIA (Flex, OpenLaszlo) AJAX!!!
2007-12-2000 minOCDevel Web Development Podcast
OCDevel Web Development PodcastPodcast 2: ServerSystems (sys admin) - Linux, Windows Server 2003, BSD Server Software (services) - web servers - database servers - mail servers - revision - control servers - *ports Web Server Configuration - apache, IIS Database (dba) - MySql, SQL Server, Oracle, Postgres Server-side Scripting - Script runs on web server, generates dynamic web page - (vs client-side, which generates dynamic content in the browser) - CGI Web server extension modules - CGI Languages: *Perl, Python, Ruby - Web server extension modules: *PHP, ASP, ASP.NET - Java servlets
2007-12-0700 minOCDevel Web Development Podcast
OCDevel Web Development PodcastPodcast 1: Introduction- Client: Web design ( HTML, CSS, Images ), Interactivity / Browser Objects ( Flash, Flex, Laszlo, Silverlight, Java Applets / Webstart), Client-side scripting ( JavaScript, AJAX) - Server: Server-side scripting( PHP, ASP, ASP.NET, J2EE, CGI, Python, Perl, Ruby), Database, Systems Administration - Internet marketing: SEO, Accessibility, Marketing, Monitization - Extras: WYSIWYG tools, Web Frameworks ( Django, Rails ), Blogging & CMS ( WordPress, Joomla!, Drupal)
2007-12-0500 min