AI in Plain English·2yWhy is GPT-4o Free? OpenAI’s Master Plan
OpenAI has launched GPT-4o for free with the aim of achieving mass adoption, competing with other models, collecting more data for training future models, countering the scorched earth theory, and driving network effects. They may also have even better products coming soon.
freeCodeCamp·2yDeep Learning Course – Math and Applications
Learn the math behind deep learning with a 14-hour course on the freeCodeCamp YouTube channel. Developed by Ayush Singh, the course covers fundamental concepts, deep learning techniques, mathematical insights, and practical applications. Topics include vectors, matrices, linear algebra, calculus, machine learning, and neural networks.
Quastor Daily·2yThe Engineering behind Instagram's Recommendation Algorithm
The post discusses the engineering behind Instagram's recommendation algorithm, including the candidate generation/retrieval phase, the use of Two Tower Neural Network models, and the ranking stages in the recommendation system.
Medium·2yThe Art of the Prompt: A Look at 26 Prompting Principles
This post explores the principles of prompt engineering and how they can be used to improve the quality and accuracy of AI-generated responses. It discusses different approaches to prompt design and provides examples of how to optimize prompts for specific use cases.
DEV·2yStudyPal: Your AI-Powered Personalized Learning Companion
StudyPal is an AI-powered personalized learning companion that offers tailored learning experiences, generates study materials, provides MCQ practice with AI assistance, tracks progress, and supports various use cases in education, professional development, special education, language learning, and exam preparation.
Appwrite·2yIntroducing the Python machine learning runtime
Appwrite introduces the Python ML runtime tailored for machine learning use cases, providing a streamlined experience for building AI-powered applications. It overcomes limitations of the current Python runtime, allowing the installation of machine learning libraries and tools. The Python ML runtime is currently CPU-only during the Beta.
KDnuggets·2yGoogle Have Just Dropped a New Course: AI Essentials
Google has released a new course called AI Essentials, designed to help beginners learn how to implement AI into their workflow. The course takes approximately 9 hours to complete and covers topics such as generative AI tools, prompt engineering, responsible AI usage, and staying up-to-date in the AI landscape. Other recommended courses for implementing AI are also mentioned.
AI in Plain English·2yYour guide to Vector Databases
This post provides a guide to understanding Vector Databases, including their definition, functionality, and applications. It also highlights the features of a good Vector Database and offers best practices for choosing the right one. The post includes a list of over 80 Vector Databases for AI projects.
Hacker News·2ySimplicity is An Advantage but Sadly Complexity Sells Better
Complexity signals effort, mastery, innovation, and more features, but simplicity has advantages in terms of understanding, usability, buildability, scalability, and lower operational costs. Complexity bias leads to undervaluing simple ideas and systems. Overcoming complexity bias can be achieved through Occam's razor and considering the cost of complexity.
freeCodeCamp·2yLinear vs Logistic Regression: How to Choose the Right Regression Model for Your Data
Linear Regression focuses on predicting continuous values, while Logistic Regression is designed specifically for binary classification. Both models assume a linear relationship between the input features and the output. Linear regression predicts continuous output on a straight line graph, while logistic regression predicts probabilities for binary classification on an S-shaped curve.
Engineering Enablement·2yReducing Code Review Time at Google
Google created a code review comment-resolution assistant using machine learning to help developers address review comments more efficiently. The assistant suggests code changes based on comment text, reducing the time spent resolving code review comments.
KDnuggets·2y5 Machine Learning Papers to Read in 2024
Discover five machine learning papers recommended to read in 2024, including HyperFast for instant classification, EasyRL4Rec for user-friendly code library, ZLaP for zero-shot classification, Infini-attention for efficient infinite context transformers, and AutoCodeRover for autonomous program improvement.
Towards Data Science·2yA Complete Guide to BERT with Code
The post provides a complete guide to BERT, including its history, architecture, pre-training objectives, and fine-tuning for sentiment analysis. It discusses the key features of BERT, such as its encoder-only architecture, pre-training approach, model fine-tuning, and use of bidirectional context. The post also covers the tokenization process, creating train and validation data loaders, instantiating a BERT model, and setting up an optimizer, loss function, and scheduler for fine-tuning. The fine-tuning loop is explained, highlighting the steps taken for each epoch and within each batch.
