Best of Machine Learning — April 2024

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   Community Picks·2y

   Web Development Trends For 2024

   The future trends of web development for 2024 include the adoption of AI and ML, the rise of Internet of Behavior (IoB), the mainstream use of Headless CMS, the expansion of micro frontends, the importance of mobile optimization, the widespread adoption of GraphQL, the emergence of Zero Trust Architecture (ZTA), the trend towards serverless architecture, the introduction of edge computing, and the popularity of programming languages and frameworks such as Rust, JavaScript, Python, Django, Node.js, Svelte, and Qwik.

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   Daily Dose of Data Science | Avi Chawla | Substack·2y

   25 Most Important Mathematical Definitions in Data Science

   The importance of mathematical knowledge in data science and machine learning, a list of important mathematical formulations used in data science and statistics, and the use of mean squared error (MSE) in machine learning.

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   Article

   Medium·2y

   5 AI Books for Software Engineers

   This post provides a list of 5 AI books for software engineers with a good balance of theory and practice. It covers topics such as machine learning with PyTorch and Scikit-Learn, deep learning, and understanding deep learning. The post also includes links to additional resources and recommends taking a slow and thorough approach to learning.

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   KDnuggets·2y

   Free Python Resources That Can Help You Become a Pro

   A collection of free Python resources including courses, books, repositories, projects, cheat sheets, and online compilers to help beginners become proficient in Python programming.

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   Article

   Medium·2y

   25+ Data Science Projects to Boost Your Resume

   Mention and work on projects that can show most of your skills in solving a Data Science problem. Always make sure you are working on a project based on a real-time business problem.

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   Article

   freeCodeCamp·2y

   Understand AI and Neural Networks by Manually Adjusting Parameters

   Learn AI and Neural Networks by adjusting parameters manually in a custom playground. Dr. Radu's course covers key topics like Neural Networks' math, hidden layers, and Dijkstra's algorithm. Gain hands-on experience and a solid understanding of AI fundamentals.

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   Article

   Towards AI·2y

   Building your own Object Detector from Scratch with Tensorflow

   This post discusses building an object detector from scratch using TensorFlow. It covers the model architecture, tasks involved in object detection, and the training process. The post also mentions the importance of data augmentation and improving model capacity to achieve better results.

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   Article

   Machine Learning News·2y

   6 Free Artificial Intelligence AI Courses from Google

   Six free AI courses from Google offer a structured pathway for beginners to start their journey into the world of artificial intelligence. The courses cover topics such as generative AI, responsible AI, Transformer models, large language models, Encoder-Decoder architecture, and attention mechanism.

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   Article

   Watercooler·2y

   when machines starts giving motivation....😆

   A post about machines giving motivation and a humorous image.

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    Article

    Watercooler·2y

    LOL

    A post with a humorous title accompanied by an image.

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    Article

    freeCodeCamp·2y

    Learn Statistics for Data Science, Machine Learning, and AI – Full Handbook

    Learn statistics for data science, machine learning, and AI. Understand the importance of statistics in data analysis and how it provides tools and methods for finding structure and deeper insights. This handbook covers key statistical concepts, as well as prerequisites for learning statistics.

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    Article

    Watercooler·2y

    How much Time you guys think will take to build Jarvis Like AI ?

    Learn how much time it takes to build an AI like Jarvis, the steps involved, and the programming languages commonly used.

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    Article

    KDnuggets·2y

    5 Free Stanford University Courses to Learn Data Science

    Learn data science for free with these 5 courses from Stanford University. Covering programming, databases, machine learning, and more.

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    Article

    KDnuggets·2y

    A Beginner’s Guide to the Top 10 Machine Learning Algorithms

    A beginner's guide to the top 10 machine learning algorithms, covering supervised and unsupervised learning, regression, classification, clustering, and dimensionality reduction.

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    Article

    freeCodeCamp·2y

    How to Build a Production-Grade Movie Recommender in Python – A Machine Learning Handbook

    Learn how to build a movie recommendation system in Python using pandas, machine learning algorithms, and CountVectorizer for text pre-processing. The system analyzes movie descriptions, leverages cosine similarity to find similar movies, and provides personalized recommendations based on user preferences.

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    Article

    Community Picks·2y

    TransformersPHP

    TransformersPHP is a toolkit for PHP developers to easily add machine learning capabilities to their projects by using pre-trained models. It utilizes the ONNX Runtime to run these models locally on the server. The project was inspired by the Xenova/transformers project for JavaScript.

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    Article

    Microsoft Azure·2y

    AI study guide: The no-cost tools from Microsoft to jump start your generative AI journey

    Discover the free tools from Microsoft to jump start your generative AI journey. Learn about the Azure AI Fundamentals course and how to build generative AI applications with Azure AI Studio.

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    Article

    Community Picks·2y

    MIT study reveals AI model that can predict future actions of human

    An AI model developed by MIT can predict future actions of humans and other AI models by analyzing their limitations.

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    Article

    Community Picks·2y

    What Skills Should Junior Developers Have in the AI Period?

    Junior developers in the AI period should have a foundational understanding of AI and machine learning basics, proficiency in programming languages like Python, data manipulation and analysis skills using libraries like Pandas and NumPy, knowledge of software engineering best practices, ability to work with cloud platforms like AWS and Google Cloud, awareness of ethical and responsible AI use, problem-solving and critical thinking skills, commitment to continuous learning and adaptability, collaboration and communication skills, and specialization in relevant AI skills.

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    Article

    DEV·2y

    Why enterprise AI projects are moving too slowly

    AI/ML projects face challenges in coordination, collaboration, and deployment due to the different assets involved. Jupyter notebooks are useful for data scientists in experimentation, but not easily extractable for other teams. Model development for production requires additional considerations and operational needs. Integrating data science and operations teams can help bridge the gap and enhance workflow efficiency. AI projects in production require proper packaging and versioning. Jozu's KitOps offers a solution for packaging and versioning AI projects into ModelKits.

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    Article

    Community Picks·2y

    Recommender Systems, Not Just Recommender Models

    This post discusses the challenges of building recommender systems and introduces a design pattern consisting of four stages: Retrieval, Filtering, Scoring, and Ordering. Examples from companies like Meta, Netflix, and Pinterest are provided to illustrate this pattern. The post emphasizes the importance of understanding recommender systems beyond just the models.

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    Article

    Codemotion·2y

    Prompt Engineer: Beyond the Buzzword

    Prompt engineering is a fast-growing field in the tech industry that combines technical expertise with creative thinking. It requires a solid understanding of AI model architectures, linguistic and psychological insight, and problem-solving skills. Prompt engineers use development platforms, analytics software, and version control systems to refine their prompts. The demand for prompt engineers is increasing in major companies for various applications, such as customer service automation, content creation, and educational enhancements. Prompt engineering is seen as a critical tech job that may replace data scientists in the future.

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    Article

    Community Picks·2y

    microsoft/onnxruntime: ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

    ONNX Runtime is a cross-platform, high performance ML inferencing and training accelerator. It supports models from deep learning frameworks like PyTorch and TensorFlow/Keras, as well as classical machine learning libraries. ONNX Runtime inference enables faster customer experiences and lower costs, while ONNX Runtime training can accelerate model training time on multi-node NVIDIA GPUs.

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    Article

    Irrational Exuberance·2y

    My advice for how to use LLMs in your product.

    Advice on using LLMs in products, mental models, revamping workflows, retrieval augmented generation (RAG), rate of innovation, human-in-the-loop (HITL), hallucinations and legal liability, zero to one versus one to N, copyright law, data processing agreements, and provider availability.

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    Medium·2y

    Mastering the Art of Data: Python Code Snippets to Elevate Your Skills

    Master the art of data with Python and enhance your data skills using powerful Python code snippets. From data engineering and data science to data visualization and machine learning, this article covers a wide range of data-related tasks. Explore essential Python code snippets, tips, and tricks to streamline workflows, uncover insights, and deliver high-quality data solutions. Whether you're a beginner or an experienced practitioner, this article serves as a gateway to mastering data with Python.

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