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Akshit Keoliya

Учасник із 2019

Срібна ліга

Кількість балів: 6365
Значок за Encoder-Decoder Architecture Encoder-Decoder Architecture Earned лист. 19, 2023 EST
Значок за Attention Mechanism Attention Mechanism Earned лист. 14, 2023 EST
Значок за Generative AI Fundamentals - Українська Generative AI Fundamentals - Українська Earned лист. 7, 2023 EST
Значок за Introduction to Responsible AI - Українська Introduction to Responsible AI - Українська Earned лист. 7, 2023 EST
Значок за Introduction to Large Language Models - Українська Introduction to Large Language Models - Українська Earned лист. 3, 2023 EDT
Значок за Introduction to Image Generation Introduction to Image Generation Earned жовт. 12, 2023 EDT
Значок за Introduction to Generative AI - Українська Introduction to Generative AI - Українська Earned вер. 19, 2023 EDT
Значок за Pet Theory Pet Theory Earned лист. 20, 2019 EST
Значок за Diwali Speedrun Diwali Speedrun Earned жовт. 28, 2019 EDT
Значок за [DEPRECATED] OK Google: Build Interactive Apps with Google Assistant [DEPRECATED] OK Google: Build Interactive Apps with Google Assistant Earned жовт. 21, 2019 EDT
Значок за Google Developer Essentials Google Developer Essentials Earned жовт. 21, 2019 EDT
Значок за BigQuery for Machine Learning BigQuery for Machine Learning Earned жовт. 20, 2019 EDT
Значок за Intermediate ML: TensorFlow on Google Cloud Intermediate ML: TensorFlow on Google Cloud Earned жовт. 19, 2019 EDT
Значок за Baseline: Deploy & Develop Baseline: Deploy & Develop Earned жовт. 13, 2019 EDT
Значок за Create and Manage Cloud Resources Create and Manage Cloud Resources Earned жовт. 12, 2019 EDT
Значок за Machine Learning APIs Machine Learning APIs Earned жовт. 12, 2019 EDT
Значок за Intro to ML: Image Processing Intro to ML: Image Processing Earned жовт. 12, 2019 EDT
Значок за Intro to ML: Language Processing Intro to ML: Language Processing Earned жовт. 7, 2019 EDT
Значок за Baseline: Data, ML, AI Baseline: Data, ML, AI Earned жовт. 7, 2019 EDT

This course gives you a synopsis of the encoder-decoder architecture, which is a powerful and prevalent machine learning architecture for sequence-to-sequence tasks such as machine translation, text summarization, and question answering. You learn about the main components of the encoder-decoder architecture and how to train and serve these models. In the corresponding lab walkthrough, you’ll code in TensorFlow a simple implementation of the encoder-decoder architecture for poetry generation from the beginning.

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This course will introduce you to the attention mechanism, a powerful technique that allows neural networks to focus on specific parts of an input sequence. You will learn how attention works, and how it can be used to improve the performance of a variety of machine learning tasks, including machine translation, text summarization, and question answering. This course is estimated to take approximately 45 minutes to complete.

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Щоб отримати кваліфікаційний значок, пройдіть курси "Introduction to Generative AI", "Introduction to Large Language Models" й "Introduction to Responsible AI". Пройшовши завершальний тест, ви підтвердите, що засвоїли основні поняття, які стосуються генеративного штучного інтелекту. Кваліфікаційний значок – це цифровий значок від платформи Google Cloud, який свідчить, що ви знаєтеся на продуктах і сервісах Google Cloud. Щоб опублікувати кваліфікаційний значок, зробіть свій профіль загальнодоступним, а також додайте значок у профіль у соціальних мережах.

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Це ознайомлювальний курс мікронавчання, який має пояснити, що таке відповідальне використання штучного інтелекту, чому воно важливе і як компанія Google реалізує його у своїх продуктах. Крім того, у цьому курсі викладено 7 принципів Google щодо штучного інтелекту.

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У цьому ознайомлювальному курсі мікронавчання ви дізнаєтеся, що таке великі мовні моделі, де вони використовуються і як підвищити їх ефективність коригуванням запитів. Він також охоплює інструменти Google, які допоможуть вам створювати власні додатки на основі генеративного штучного інтелекту.

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This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Diffusion models draw inspiration from physics, specifically thermodynamics. Within the last few years, diffusion models became popular in both research and industry. Diffusion models underpin many state-of-the-art image generation models and tools on Google Cloud. This course introduces you to the theory behind diffusion models and how to train and deploy them on Vertex AI.

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Це ознайомлювальний курс мікронавчання, який має пояснити, що таке генеративний штучний інтелект, як він використовується й чим відрізняється від традиційних методів машинного навчання. Він також охоплює інструменти Google, які допоможуть вам створювати власні додатки на основі генеративного штучногоінтелекту.

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Welcome to the serverless Pet Theory game! Click "Join this Game". To modify your player name or avatar, go to your My Account page at https://google.qwiklabs.com. Points are earned by completing the steps in the lab.... and bonus points are earned for speed! Be sure to complete each lab by selecting the END option to get the maximum points. Please respect the GCP resource quotas that have been allocated. Otherwise, you'll waste your Game time and gain fewer points.

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Welcome to Diwali Speedrun! Here's some more fun for you this Diwali. Learn the ins and outs of the Google assistant with these interesting game labs. Complete the game labs one by one. The faster you complete the lab objectives, the higher your score. You can take each lab up to 5 times. Good luck!

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With Google Assistant part of over a billion consumer devices, this quest teaches you how to build practical Google Assistant applications integrated with Google Cloud services via APIs. Example apps will use the Dialogflow conversational suite and the Actions and Cloud Functions frameworks. You will build 5 different applications that explore useful and fun tools you can extend on your own. No hardware required! These labs use the cloud-based Google Assistant simulator environment for developing and testing, but if you do have your own device, such as a Google Home or a Google Hub, additional instructions are provided on how to deploy your apps to your own hardware.

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This introductory-level quest shows application developers how the Google Cloud ecosystem could help them build secure, scalable, and intelligent cloud native applications. You learn how to develop and scale applications without setting up infrastructure, run data analytics, gain insights from data, and develop with pre-trained ML APIs to leverage machine learning even if you are not a Machine Learning expert. You will also experience seamless integration between various Google services and APIs to create intelligent apps.

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Want to build ML models in minutes instead of hours using just SQL? BigQuery ML democratizes machine learning by letting data analysts create, train, evaluate, and predict with machine learning models using existing SQL tools and skills. In this series of labs, you will experiment with different model types and learn what makes a good model. Looking for a hands on challenge lab to demonstrate your skills and validate your knowledge? On completing this quest, enroll in and finish the additional challenge lab at the end of this quest to receive an exclusive Google Cloud digital badge.

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TensorFlow is an open source software library for high performance numerical computation that's great for writing models that can train and run on platforms ranging from your laptop to a fleet of servers in the Cloud to an edge device. This quest takes you beyond the basics of using predefined models and teaches you how to build, train and deploy your own on Google Cloud.

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In this introductory-level quest, you will learn the fundamentals of developing and deploying applications on the Google Cloud Platform. You will get hands-on experience with the Google App Engine framework by launching applications written in languages like Python, Ruby, and Java (just to name a few). You will see first-hand how straightforward and powerful GCP application frameworks are, and how easily they integrate with GCP database, data-loss prevention, and security services.

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Пройдіть квест Create and Manage Cloud Resources й отримайте skill badge. Ви навчитеся виконувати наведені нижче дії. Писати команди gcloud і використовувати Cloud Shell, створювати й розгортати віртуальні машини в Compute Engine, запускати контейнерні додатки за допомогою Google Kubernetes Engine, а також налаштовувати розподілювачі навантаження для мережі й HTTP.Skill badge – це ексклюзивна цифрова винагорода, яка підтверджує, що ви вмієте працювати з продуктами й сервісами Google Cloud, а також застосовувати ці знання в інтерактивному практичному середовищі. Щоб отримати skill badge й показати його колегам, пройдіть цей квест і підсумковий тест.

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It's no secret that machine learning is one of the fastest growing fields in tech, and Google Cloud has been instrumental in furthering its development. With a host of APIs, Google Cloud has a tool for just about any machine learning job. In this advanced-level quest, you will get hands-on practice with machine learning APIs by taking labs like Detect Labels, Faces, and Landmarks in Images with the Cloud Vision API. Looking for a hands-on challenge lab to demonstrate your skills and validate your knowledge? On completing this quest, enroll in and finish the additional challenge lab at the end of this quest to receive an exclusive Google Cloud digital badge.

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Using large scale computing power to recognize patterns and "read" images is one of the foundational technologies in AI, from self-driving cars to facial recognition. The Google Cloud Platform provides world class speed and accuracy via systems that can utilized by simply calling APIs. With these and a host of other APIs, GCP has a tool for just about any machine learning job. In this introductory quest, you will get hands-on practice with machine learning as it applies to image processing by taking labs that will enable you to label images, detect faces and landmarks, as well as extract, analyze, and translate text from within images.

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It’s no secret that machine learning is one of the fastest growing fields in tech, and the Google Cloud Platform has been instrumental in furthering its development. With a host of APIs, GCP has a tool for just about any machine learning job. In this introductory quest, you will get hands-on practice with machine learning as it applies to language processing by taking labs that will enable you to extract entities from text, and perform sentiment and syntactic analysis as well as use the Speech to Text API for transcription.

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Big data, machine learning, and artificial intelligence are today’s hot computing topics, but these fields are quite specialized and introductory material is hard to come by. Fortunately, Google Cloud provides user-friendly services in these areas, and with this introductory-level quest, so you can take your first steps with tools like Big Query, Cloud Speech API, and AI Platform. Want extra help? 1-minute videos walk you through key concepts for each lab.

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