Welcome to Hyperparameter Optimization for Machine Learning. In this course, you will learn multiple techniques to select the best hyperparameters and improve the performance of your machine learning models. If you are regularly training machine learning models as a hobby or for your organization and want to improve the performance of your models, if you are keen to jump up in the leader board of a data science competition, or you simply want to learn more about how to tune hyperparameters of machine learning models, this course will show you how. We'll take you step-by-step through engaging video tutorials and teach you everything you need to know about hyperparameter tuning. Throughout this comprehensive course, we cover almost every available approach to optimize hyperparameters, discussing their rationale, their advantages and shortcomings, the considerations to have when using the technique and their implementation in Python.

Most notably, text-to-image models (AI art) became extremely popular. Search engines were swapped for sophisticated chatbots such as ChatGPT. With open-source alternatives such as PaLM RLHF on the horizon, AI and machine learning will become more accessible to neophyte developers. However, becoming a true machine learning engineer requires more skill than just scripting or coding. As such, more people are beginning to consider it as a potential career path.

Machine learning engineering is the field of developing, implementing, and maintaining machine learning systems. It involves the application of engineering principles to the design, development, and deployment of machine learning models, algorithms, and applications. The primary focus of ML engineering is to build scalable and efficient machine learning systems that can process large volumes of data and generate accurate predictions. It involves various tasks such as data preparation, model development, model training, model deployment, and model monitoring. ML engineering requires a combination of skills in computer science, mathematics, statistics, and domain-specific knowledge.

I am a postgraduate in Computer Science (Cyber Security) from IIT Bombay and hold a keen interest in Cyber Security, Blockchain, AI & IoT, Electric Vehicle (EV) among other deeptech areas. At the application level, I love SaaS applications. Currently, I am the Founder & CEO of Tosh Innovations Private Limited, a Gurugram based Bootstrapped, Cash positive EdTech Company. Tosh Innovations offers online educational certifications in deeptech areas. Tosh Innovations is a Bootstrapped company of 30 young people who create exceptionally informative content for a global community.

Azure AI #30DaysOfAzureAI is a series of daily posts throughout April. Hear from our experts in the product teams, cloud advocacy, community and follow along at your own pace! Where relevant, the daily posts have accompanying Open Source repositories, code samples, and other resources. Add #30DaysOfAzureAI to your calendar for all of April and follow on Twitter hashtags #AzureAIDevs #30DaysOfAzureAI and be sure to share and join the conversation. We've organized the content into 4 themed weeks.

Using artificial intelligence and archival news articles, a teenager in Northern Virginia created a program to measure media biases – and in researching older news articles, she found that Black homicide victims were less likely to be humanized in news coverage. Emily Ocasio, an 18-year-old from Falls Church, Virginia, created an AI program that analyzed FBI homicide records between 1976 and 1984 and their corresponding coverage published in The Boston Globe to determine whether victims were presented in a humanizing or impersonal way. After analyzing 5,042 entries, the results showed that Black men under the age of 18 were 30% less likely to receive humanizing coverage than their White counterparts, Ocasio told CNN. Black women were 23% less likely to be humanized in news stories, Ocasio added. A news article was considered humanizing when it mentioned additional information about the victim and presented them "as a person, not just a statistic," Ocasio said in her project presentation.

An increasing number of monitoring systems have been developed in smart cities to ensure that the real-time operations of a city satisfy safety and performance requirements. However, many existing city requirements are written in English with missing, inaccurate, or ambiguous information. There is a high demand for assisting city policymakers in converting human-specified requirements to machine-understandable formal specifications for monitoring systems. To tackle this limitation, we build CitySpec, the first intelligent assistant system for requirement specification in smart cities. To create CitySpec, we first collect over 1,500 real-world city requirements across different domains (e.g., transportation and energy) from over 100 cities and extract city-specific knowledge to generate a dataset of city vocabulary with 3,061 words. We also build a translation model and enhance it through requirement synthesis and develop a novel online learning framework with shielded validation. The evaluation results on real-world city requirements show that CitySpec increases the sentence-level accuracy of requirement specification from 59.02% to 86.64%, and has strong adaptability to a new city and a new domain (e.g., the F1 score for requirements in Seattle increases from 77.6% to 93.75% with online learning). After the enhancement from the shield function, CitySpec is now immune to most known textual adversarial inputs (e.g., the attack success rate of DeepWordBug after the shield function is reduced to 0% from 82.73%). We test the CitySpec with 18 participants from different domains. CitySpec shows its strong usability and adaptability to different domains, and also its robustness to malicious inputs.

The advancement of audio-language (AL) multimodal learning tasks has been significant in recent years. However, researchers face challenges due to the costly and time-consuming collection process of existing audio-language datasets, which are limited in size. To address this data scarcity issue, we introduce WavCaps, the first large-scale weakly-labelled audio captioning dataset, comprising approximately 400k audio clips with paired captions. We sourced audio clips and their raw descriptions from web sources and a sound event detection dataset. However, the online-harvested raw descriptions are highly noisy and unsuitable for direct use in tasks such as automated audio captioning. To overcome this issue, we propose a three-stage processing pipeline for filtering noisy data and generating high-quality captions, where ChatGPT, a large language model, is leveraged to filter and transform raw descriptions automatically. We conduct a comprehensive analysis of the characteristics of WavCaps dataset and evaluate it on multiple downstream audio-language multimodal learning tasks. The systems trained on WavCaps outperform previous state-of-the-art (SOTA) models by a significant margin. Our aspiration is for the WavCaps dataset we have proposed to facilitate research in audio-language multimodal learning and demonstrate the potential of utilizing ChatGPT to enhance academic research. Our dataset and codes are available at https://github.com/XinhaoMei/WavCaps.

Nowadays, foundation models become one of fundamental infrastructures in artificial intelligence, paving ways to the general intelligence. However, the reality presents two urgent challenges: existing foundation models are dominated by the English-language community; users are often given limited resources and thus cannot always use foundation models. To support the development of the Chinese-language community, we introduce an open-source project, called Fengshenbang, which leads by the research center for Cognitive Computing and Natural Language (CCNL). Our project has comprehensive capabilities, including large pre-trained models, user-friendly APIs, benchmarks, datasets, and others. We wrap all these in three sub-projects: the Fengshenbang Model, the Fengshen Framework, and the Fengshen Benchmark. An open-source roadmap, Fengshenbang, aims to re-evaluate the open-source community of Chinese pre-trained large-scale models, prompting the development of the entire Chinese large-scale model community. We also want to build a user-centered open-source ecosystem to allow individuals to access the desired models to match their computing resources. Furthermore, we invite companies, colleges, and research institutions to collaborate with us to build the large-scale open-source model-based ecosystem. We hope that this project will be the foundation of Chinese cognitive intelligence.

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multi-center study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and postprocessing (66%). The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.