#aiml

[kiraleighart]

alert: go to your goddamn Adobe account and turn this trashy AI scraping crap off

[description of screenshot: Adobe Account settings page

"Content analysis: Adobe may analyze your content using techniques such as machine learning (e.g., for pattern recognition) to develop and improve our products and services. If you prefer that Adobe not analyze your files to develop and improve our products and services, you can opt out of content analysis at any time. This setting does not apply in certain limited circumstances." Opt-in button in the "on" state (this is automatic for all accounts, please, please go turn it off): "Allow my content to be analyzed by Adobe for product improvement and development purposes."

[clearqueenllama]

I have my machine learning final tomorrow and I'm officially losing it 😭😭

[aiaccelerationist]

I have opened a Mastadon where I will do more frequent posting. Ping me if you want a follow as I need some cool feeds.

@aiaccelerationist@mastodon.social

[sirtbhopal]

Future-Ready: AIML & CSCY Students of SIRT Bhopal Gain Real-World Insights at State Command Centre

Exploring the future of technology!✨🧑‍💻 Real-world learning! Students of AIML and CSCY Department, SIRT Bhopal visit State Command Centre for hands-on experience🧑‍💻

[shailesh-shetty]

What is data labeling in artificial intelligence?

Data labeling is a fundamental aspect of AI and ML development, enabling machines to understand and interpret complex data. With EnFuse Solutions as a trusted partner, businesses can access top-tier data labeling services that drive success in AI-driven applications. Their commitment to quality, accuracy, and efficiency makes them a preferred choice for companies seeking reliable data labeling solutions in India and beyond. Get in touch with EnFuse Solutions today!

[drishti-nayak]

What Does A Data Annotator Do?

A data annotator is a vital link in the machine-learning pipeline, facilitating the creation of accurate and effective models. EnFuse Solutions, with its expertise and commitment to quality, emerges as a top choice for organizations seeking reliable and professional data annotation services.

[eminence-technology]

Artificial Intelligence For Teachers: Elevating Lesson Planning

Learn how artificial intelligence streamlines lesson planning, enhances personalization, and boosts classroom efficiency, empowering teachers to create more impactful, tailored learning experiences for their students. Do you ever feel overwhelmed by the endless tasks of lesson planning, grading, and tracking student progress? Many teachers struggle to find enough time to focus on what truly…

[tania-arora]

What is data annotation and why is it important?

In the ever-evolving landscape of artificial intelligence, the significance of data annotation cannot be overstated. It serves as the foundation for training robust and accurate machine learning models, driving advancements across various industries. EnFuse Solutions emerges as a trusted partner in this critical domain, offering top-notch annotation services across the globe with a focus on quality, expertise, and scalability.

[govindhtech]

Intel Tiber Developer Cloud, Text- to-Image Stable Diffusion

Check Out GenAI for Text-to-Image with a Stable Diffusion Intel Tiber Developer Cloud Workshop.

What is Intel Tiber Developer Cloud?

With access to state-of-the-art Intel hardware and software solutions, developers, AI/ML researchers, ecosystem partners, AI startups, and enterprise customers can build, test, run, and optimize AI and High-Performance Computing applications at a low cost and overhead thanks to the Intel Tiber Developer Cloud, a cloud-based platform. With access to AI-optimized software like oneAPI, the Intel Tiber Developer Cloud offers developers a simple way to create with small or large workloads on Intel CPUs, GPUs, and the AI PC.

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Developers and enterprise clients have the option to use free shared workspaces and Jupyter notebooks to explore the possibilities of the platform and hardware and discover what Intel can accomplish.

Text-to-Image

This article will guide you through a workshop that uses the Stable Diffusion model practically to produce visuals in response to a written challenge. You will discover how to conduct inference using the Stable Diffusion text-to-image generation model using PyTorch and Intel Gaudi AI Accelerators. Additionally, you will see how the Intel Tiber Developer Cloud can assist you in creating and implementing generative AI workloads.

Text To Image AI Generator

AI Generation and Steady Diffusion

Industry-wide, generative artificial intelligence (GenAI) is quickly taking off, revolutionizing content creation and offering fresh approaches to problem-solving and creative expression. One prominent GenAI application is text-to-image generation, which uses an understanding of the context and meaning of a user-provided description to generate images based on text prompts. To learn correlations between words and visual attributes, the model is trained on massive datasets of photos linked with associated textual descriptions.

A well-liked GenAI deep learning model called Stable Diffusion uses text-to-image synthesis to produce images. Diffusion models work by progressively transforming random noise into a visually significant result. Due to its efficiency, scalability, and open-source nature, stable diffusion is widely used in a variety of creative applications.

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The Stable Diffusion model in this training is run using PyTorch and the Intel Gaudi AI Accelerator. The Intel Extension for PyTorch, which maximizes deep learning training and inference performance on Intel CPUs for a variety of applications, including large language models (LLMs) and Generative AI (GenAI), is another option for GPU support and improved performance.

Stable Diffusion

To access the Training page once on the platform, click the Menu icon in the upper left corner.

The Intel Tiber Developer Cloud‘s Training website features a number of JupyterLab workshops that you may try out, including as those in AI, AI with Intel Gaudi 2 Accelerators, C++ SYCL, Gen AI, and the Rendering Toolkit.

Workshop on Inference Using Stable Diffusion

Thwy will look at the Inference with Stable Diffusion v2.1 workshop and browse to the AI with Intel Gaudi 2 Accelerator course in this tutorial.

Make that Python 3 (ipykernel) is selected in the upper right corner of the Jupyter notebook training window once it launches. To see an example of inference using stable diffusion and creating an image from your prompt, run the cells and adhere to the notebook’s instructions. An expanded description of the procedures listed in the training notebook can be found below.

Note: the Jupyter notebook contains the complete code; the cells shown here are merely for reference and lack important lines that are necessary for proper operation.

Configuring the Environment

Installing all the Python package prerequisites and cloning the Habana Model-References repository branch to this docker will come first. Additionally, They are going to download the Hugging Face model checkpoint.%cd ~/Gaudi-tutorials/PyTorch/Single_card_tutorials !git clone -b 1.15.1 https://github.com/habanaai/Model-References %cd Model-References/PyTorch/generative_models/stable-diffusion-v-2-1 !pip install -q -r requirements.txt !wget https://huggingface.co/stabilityai/stable-diffusion-2-1-base/resolve/main/ v2-1_512-ema-pruned.ckpt

Executing the Inference

prompt = input("Enter a prompt for image generation: ")

The prompt field is created by the aforementioned line of code, from which the model generates the image. To generate an image, you can enter any text; in this tutorial, for instance, they’ll use the prompt “cat wearing a hat.”cmd = f'python3 scripts/txt2img.py --prompt "{prompt}" 1 --ckpt v2-1_512-ema-pruned.ckpt \ --config configs/stable-diffusion/v2-inference.yaml \ --H 512 --W 512 \ --n_samples 1 \ --n_iter 2 --steps 35 \ --k_sampler dpmpp_2m \ --use_hpu_graph'

print(cmd) import os os.system(cmd)

Examining the Outcomes

Stable Diffusion will be used to produce their image, and Intel can verify the outcome. To view the created image, you can either run the cells in the notebook or navigate to the output folder using the File Browser on the left-hand panel:

/Gaudi-tutorials/PyTorch/Single_card_tutorials/Model-References /PyTorch/generative_models/stable-diffusion-v-2-1/outputs/txt2img-samples/Image Credit To Intel

Once you locate the outputs folder and locate your image, grid-0000.png, you may examine the resulting image. This is the image that resulted from the prompt in this tutorial:

You will have effectively been introduced to the capabilities of GenAI and Stable Diffusion on Intel Gaudi AI Accelerators, including PyTorch, model inference, and quick engineering, after completing the tasks in the notebook.

Read more on govindhtech.com

[kiraleighart]

caring about tech's ethics means caring about its outcomes. humans use technology. we are dangerously close to it being the other way around for business reasons. in perpetuity.

this should piss you off.

[dronacharyacollege]

Placement in  Telaverge Communications India Pvt. Ltd. - Harsh Vatsal

We are excited to share that Harsh Vatsal, a dedicated student from the Batch of 2020-2024, CSE (AIML), at Dronacharya College of Engineering, has been selected by Telaverge Communications India Pvt. Ltd.! 🏆✨ Your hard work, dedication, and expertise in AI and ML have led you to this incredible achievement. We're so proud of you and can't wait to see all the amazing things you'll accomplish in your new role.

[sgnog]

𝗙𝗥𝗢𝗠 𝗙𝗔𝗦𝗧 𝗧𝗢 𝗙𝗔𝗦𝗧𝗘𝗥

#SGNOG11 takes you through a journey through 𝗰𝘂𝘁𝘁𝗶𝗻𝗴-𝗲𝗱𝗴𝗲 transceiver technologies, backbone infrastructures and 𝗔𝗜/𝗠𝗟 connectivity. 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗵𝗲𝗿𝗲: https://www.sgnog.net/?page_id=7025

[shailesh-shetty]

What's The Importance Of Annotation And Labeling Services In Data Science?

The importance of data annotation and labeling services in data science cannot be overstated. EnFuse's commitment to delivering accurate, domain-specific, and ethically sourced annotations positions them as a frontrunner in the competitive landscape.

[classroomlearning]

Unlocking the Future: MTech in Artificial Intelligence and Machine Learning at Brainware University

Apply Here -

In an era where technology is advancing at lightning speed, Computer Science and Engineering stands as a beacon of innovation and opportunity. As industries around the globe embrace the transformative power of Artificial Intelligence (AI) and Machine Learning (ML), the demand for skilled professionals in these fields has never been higher. If you're passionate about diving into these cutting-edge technologies and shaping the future, Brainware University's MTech program in AI and ML is your gateway to success.

Programme Overview: A Pathway to Technological Excellence

Brainware University's MTech in Artificial Intelligence and Machine Learning is a two-year postgraduate program designed to immerse students in the world of advanced computing technologies. As one of the leading private universities in West Bengal, Brainware offers a comprehensive curriculum that covers the latest trends and developments in the field. From deep learning and neural networks to natural language processing and computer vision, the program ensures that students gain a thorough understanding of AI and ML technologies.

Syllabus Outline: A Deep Dive into Cutting-Edge Technologies

The curriculum for the MTech in AI and ML at Brainware University is meticulously designed to provide students with both theoretical knowledge and practical experience. Key topics include:

Deep Learning: Explore advanced neural networks and their applications.

Natural Language Processing: Understand how machines process and interpret human language.

Computer Vision: Delve into techniques for enabling machines to interpret visual data.

Data Analytics: Learn how to analyze and interpret complex datasets.

This rich syllabus is complemented by hands-on projects and research opportunities, ensuring that students are well-equipped to tackle real-world challenges.

Fees and Eligibility: Invest in Your Future

Pursuing an MTech in AI and ML at Brainware University is a valuable investment in your future. The program offers a competitive fee structure, designed to provide top-notch education while remaining affordable. Eligibility criteria typically include:

Bachelor's Degree: A relevant undergraduate degree in Computer Science, Engineering, or a related field.

Entrance Examination: Performance in a relevant entrance exam or academic records as specified by the university.

For detailed information on fees and specific eligibility requirements, prospective students are encouraged to visit the university’s official website or contact the admissions office.

Career Options: Paving the Path to Success

Graduates of the MTech in AI and ML program are well-prepared to embark on exciting career paths in various sectors. Potential roles include:

Business Intelligence Developers: Designing and implementing strategies for data-driven decision-making.

Big Data Engineers: Managing and analyzing large datasets to extract valuable insights.

Robotic Scientists: Developing intelligent systems and robots for various applications.

The skills gained through the program open doors to numerous opportunities in tech-driven industries and research institutions.

Salient Features: A Comprehensive Learning Experience

Brainware University’s MTech program stands out due to its commitment to providing a holistic learning experience. Key features include:

State-of-the-Art Infrastructure: Modern facilities equipped with the latest technology.

Global Learning: Exposure to international trends and practices in AI and ML.

Pre-Placement Training: Support in preparing for job interviews and securing employment.

360-Degree Placement Support: Comprehensive assistance in job placement to ensure a smooth transition into the professional world.

Admission Procedure: How to Get Started

The admission process for the MTech in AI and ML involves several steps:

Application: Submit your application form along with the required documents.

Entrance Exam: Clear the entrance exam as per university guidelines.

Interview: Participate in an interview if required.

Selection: Await the admission decision and complete the enrollment process.

For detailed instructions and deadlines, refer to the university’s admission portal.

Departmental Achievements: Excellence in Education

Brainware University’s Department of Computer Science and Engineering is renowned for its contributions to the field of AI and ML. The department boasts numerous research publications, industry collaborations, and successful alumni who have made significant impacts in their respective fields.

Placement Highlights: Success Stories

The MTech in AI and ML program has a strong track record of successful placements. Graduates have secured positions in leading tech companies, research labs, and innovative startups. The university’s robust placement support system plays a crucial role in facilitating these successful outcomes.

Conclusion: Embark on Your Journey of Innovation

If you're ready to explore the fascinating world of Artificial Intelligence and Machine Learning, Brainware University's MTech program offers a gateway to advanced knowledge and career success. With its cutting-edge curriculum, state-of-the-art infrastructure, and dedicated support services, Brainware University stands out as a premier choice for aspiring tech professionals. Embrace the future of technology and position yourself at the forefront of innovation with an MTech in AI and ML from Brainware University.