#fully automatic notebook making machine

[foodmart01]

Notebook Making Machine

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[imawreck]

Demands

Pairing: Bucky Barnes/Winter Soldier x Original Character

Summary: Banner delves deeper into his research about Snow’s genetic alterations, and Bucky gets some time to share a moment with Snow.

Warnings: None, just some little tiny angst if you squint.

Word Count: 3,129

Snow-

Doctor Banner glided a device attached to the arm of a white machine over my bruised and battered abdomen, displaying the image of my healing insides. Each time he scanned over me the image became less muddled and showed a more healed wound.

He spoke with a light flitter of wonder in his voice, "Months of recovery in a matter of hours. It's astonishing."

I pulled myself up as gently as I could, my body ached from the events of the day and my energy was draining quickly from the wound healing on my side. It takes a lot out of me to heal this fast, especially if I'm willing it to stitch together faster. My body is working on overdrive to right the injury, meaning all of my resources are put towards healing while depleting my energy nearly enough to knock me out on the spot. I tugged the gap in my suit back over the area. "It's tiring." I winced as I pushed myself to the edge of the table.

Banner sent me a wary glance, "Are you alright?"

"Yeah, healing like this just takes lots of energy I don't have right now. I need some rest." I gave him a subtle smile. I'm sure I looked awful all covered in blood and sweat. I felt awful, but he gave me a kind smile in return none the less.

Just as I was about to make the trek off the table and up to my rooms, a body came jogging through the door. His blue eyes were searching and worried, and when they landed on me a sigh escaped him. I shot Bucky a wry smile, "I told you I'd be back."

A smile grew on his face too, but quickly dropped a little as he made his way over to the table I sat on. Banner continued to actively ignore us and focus hard on the x-rays in front of him. Bucky's finger swiped delicately over my cheek as his eyes traveled over me. A pinch of pain rose from the contact. "You're bruised... " I could tell he wanted to say more as he swiped his fingers over some dried blood clinging to my arm.

I shook my head, gently patting my healing side, "Don't worry about it, I'm alright. The bruises will leave once this is gone."

He frowned at that, "Steve said it was pretty bad out there. Everyone looked exhausted."

I stared at the floor as memories of the fight flashed behind my eyes. "He's right, it was bad. The soldiers were strong, and the team was struggling against them. Steve was afraid, I could smell his fear in the air."

Both Banner and Bucky looked at me strangely at that. Bucky's frown deepened, "You could smell his fear?"

"Yeah, I can tell when people are afraid. I can smell other things too, but fear is the strongest. Something to do with pheromones or something like that." I looked at Bucky, giving him a lopsided grimace, "I think Steve is afraid of me."

Bucky shook his head almost automatically. "No, Steve isn't afraid of you. I'm sure he's not."

"It's okay, it doesn't bother me if he is. He has every right to be." I shrugged, wincing.

Bucky's hand twitched up at my pain but dropped back after a moment. "You should get some rest. I'll help you back to your room." He slipped his flesh arm under one of my own and half lifted me, half dragged me off of the table and onto my feet. He was patient as we made our way towards the doors.

Banner had wandered over to a notebook in the corner and had begun to write vigorously across its pages. He spoke after us, "Snow, I would like to run some more tests when you are fully recovered. I want to look into some more of your genetic enhancements."

I couldn't help the anxious frown that flashed across my face, too exhausted to control my outward appearances any longer. Bucky seemed to notice my discontent and paused, turning just enough for Banner to see his face. "I think you've tested her enough for now, don't you?"

Doctor Banner stuttered at this, "I-I'm sorry, you're right. Don't worry about it Snow, it can wait." He sent me a nervous smile before Bucky tugged us back into motion.

Even with one arm, Bucky was surprisingly strong. He managed to haul my tired limp form all the way down the hall, through the elevator, and down another hall to make it to my door. By the time we had made it, I was barely warding off sleep. My body was shutting down in efforts to keep functioning along with healing. He pressed the door open with his shoulder, gently pushing me in first and following as I stumbled forward. Bucky's grip was strong but in a gentle manner as he maneuvered me onto the plush mattress and adjusted the covers.

Through the haze of my mind, I managed to get out a couple words of thanks before I drifted off into the darkness tugging at my eyelids.

_____

Bucky-

For someone who could manage to throw a 260-pound man around like a rag doll, Snow sure was light. Even with one arm out of commission I could easily carry her body weight against me all the way to her room. She was barely holding on to her consciousness, obviously drained from whatever was going on with her body. Snow fought for a moment longer and her voice was light and filled with kindness, "Thank you, Winter."

My heart panged at the name, but I didn't have it in me to correct her. She had drifted off right afterwards, her face relaxed and her lashes brushed against her cheeks. I was reminded once more of how beautiful she was. Her hair was getting longer, naturally white and a little curly if you looked at her long enough. Her brows were slim and dark, contrasting her light hair and complexion. She had a little tan despite the lack of sun in the mountains where they’d kept her for so long, and her features were soft, nothing like what I had initially thought when I first saw her in that room. Back then she looked sharp as a knife, her whole body was a weapon. Now, up close, it seems that wasn't the case at all.

I found my hand on her face in the midst of my thoughts, brushing away a stray piece of hair behind her ear. I stepped back from the side of her bed, dragging myself away from her and trying to figure out the hurricane of jumbled emotions I had been juggling for so long. It wasn't any use, but I felt like if I sorted it out then maybe I could...

What am I saying? I'm not worth that. I don't deserve that. She doesn't even see me as anything more than a mission.

I closed my eyes and gripped the door, clicking it closed behind me.

_____

Banner-

I studied the x-rays of Snow's healing wound in utter amazement. Her healing abilities were fascinating and absolutely world altering if applied in the right ways. I had shown them to Tony the day I had gotten the results. He was as equally as amazed as I was.

He had given me the job of studying her physical qualities and her genetic mutations to better understand her abilities for both her and the avengers in case of an emergency. Steve had come in later, after Bucky had taken her back to her rooms, with a handful of what looked like claws. Which turns out they were. He explained to me that she had... grown them, and used them in battle before they had simply fallen off when she had finished fighting. He had tossed them onto the table for me to study before he left, and they were nearly as amazing as her x-rays.

Her blood, I had found, was untraceable due to the mixed work Hydra had done to her, which made it more difficult to break down how she made her body do the things it did. I had to find the baseline, her actual DNA, in the mess of all the other strands jumbled around it.

The nails were still mostly the same genetic makeup as the average human, but they resembled animal claws much like a large cat would have. She had physically altered the way her nails grew and how fast with her regenerative healing, strengthening them far more than the average persons to make them durable for battle. It was incredible.

The next day, Tony ordered Snow to the lab with me for another checkup. She came in with a bowl of cereal in hand, explaining that after major damage is received, she has to make up for the resources used up to heal it and I quickly jotted it down in my notes. Bucky watched from the other side of the glass doors with a wrinkle in his brow as I scanned her once more to make sure she was fully healed. Which she was. Completely, not even a scar left to tell the story. I sent her off after I was finished and watched as they walked into Tony's lab next door.

I turned away, looking back up at the screen in front of me, wondering if I could find and recreate her regenerative quality for... something else. I sat at my desk, staring at my notes, and got to work.

_____

Bucky-

Snow busied herself around Tony's lab collecting several tools in order for her to fix my arm. She ordered me to sit in the singular office chair Tony kept in the room and rolled over a short metal table right next to it presumably for her to work on my arm as we sat. I plopped down in the chair and watched her pick up a couple more items, juggling them in her arms before making her way over and dropping them on the table beside me.

She heaved her shoulders in a huff, "Alright, if you'll set it on the table, I'll see what I can do." She smiled warmly and my stomach flipped.

I nodded, using my good arm to lift the useless chunk of metal onto the table for her to examine. She wasted no time taking note of the multiple bent plates and a few internal joints that shouldn't have been sticking out in between the gaps. I watched as she took a screwdriver and popped off the plates to take a look at the inside, setting the sections off to the side of the table and labeling them accordingly. She seemed to move in a way that told me she had done this more than once before. Her body was fluid in her movements and her hand scrawled out her labels without her batting an eye in their direction.

I swallowed a lump in my throat I hadn't realized was there. "You used to do this a lot, didn't you?"

She looked up from the pair of wires she held between her fingers, blinking a few times. "I told you I used to work on your arm, didn't I? I used to fix you up all the time after you came back from missions. You were always getting this thing banged up."

Of course, I thought, mentally cursing myself. I went quiet for a while, letting her work in silence.

Her brows furrowed after a while, and she paused with one of the plates held in her hand. "Do you... Do you ever remember anything? You don't have to tell me if you're not comfortable, I know how precious memories are." The distant look that passed over her face was hard to miss.

I thought hard on whether I should tell her about the memories I've had with her in them. The brief flash of sheets and pages flickered in my mind before heat crawled up my neck.

All the fragments I have had with her involved felt so intimate. I wasn't sure how well I could discern them from dreams. "I have broken memories, it's just pieces of a picture I can't put together."

Snow had hung onto every word I said, her whole body leaning forward at my confession. She was quiet for a moment, studying the piece of metal in her hand before getting up and walking over to some sort of scanner Tony had. She busied herself scanning the metal plate of my arm and a machine beside her whirred to life, spitting out hot metal and molding it into thin layers. She spoke as the machine worked, turning her head to look at me over her shoulder. "Did you remember something when we took the trip to the mall? The bookstore, I mean."

Another flicker of her hand in my metal one, gently folding and unfolding my fingers over hers. I nodded, unable to find my voice.

She nodded along with me, turning back to the machine as it completed an identical copy of the plate she had scanned, free of any damage. She let it cool before picking it up and setting it on the table with the others. Her fingers wrapped around the next plate, and she repeated the same action with it as she did the other, making brand new plates to replace the old ones.

While the machine got to work, she returned to her seat next to me and continued to fiddle with the internal parts of my arm. "That was the second to last time I got to see you before they put me in Cryo."

She hadn't looked up when she spoke, instead keeping her hands busy putting the plates back into their order. Her nimble fingers worked at something in my forearm as I stared at her face, waiting for her to continue.

"You had come to my quarters right after a mission in the middle of the night. Nearly scared the hell out of me when I woke up to the door creaking open." She smiled lightly, "You always wore this expression on your face, all tough business, but then you would look at me and smile." Her hair fell in front of her face, and she ran her hand through it only for the strands to fall again.

"You had made it a habit of showing up at my personal quarters instead of waiting for Commander to call me in the following day to check on you. I always knew there was a risk that we would get caught, but you and I both didn't seem all that bothered by it at the time." Her smile dropped as she clicked a plate into place, neatly folding a pair of wires behind it. "I had been reading that same book you gifted to me. It was something left behind from my father as a parting gift, and you had asked me to read it to you." She seemed to think upon the memory with great fondness. Her shoulders relaxed when she spoke of it and her eyes seemed alive at the thought.

I furrowed my brows as I remembered it too, her voice reading words from old pages. I could almost feel the pressure of her fingers against my palm. "I remember that. I remember you reading."

Her smile grew just a little and her eyes met mine just for a moment before she gave her attention back to my arm. "I had to make you leave later, you had fallen asleep on my bed. I didn't want you to get in trouble, so it wasn't too long. The sun hadn't even risen yet, but you were expected back."

I remembered her hand on my face, the warmth of it against my skin. "I told you I didn't want to leave."

Her eyes snapped up at that, wide and a little wary. "You did." I felt the last plate snap into place and my arm whirred back to life under her fingers. She smiled triumphantly, "All fixed up. Can you move everything okay?"

I clenched and unclenched my fist, bending my arm at the elbow and rolling my shoulder. Everything worked perfectly. “Wow, I'm impressed."

She gave me a sly smile, resting her hands against her hips in a defiant way, "I told you I could, I rival the Tony Stark. He said so himself."

Her grin grew at her statement, and I had to laugh at her ridiculously proud posture. I sat up in the chair, smiling at her. I was going to ask further about the memory, but Tony came around the corner wearing a business suit and a serious expression. I frowned, standing up next to Snow as she turned to see him.

"Hey, Tony," a flash of worry crossed Snow’s features, "What's wrong?"

He sighed, "The feds and I have been trying to interrogate Strucker all night, but he won't give in. He refuses to give us any information unless he can speak to you."

I stilled at this and watched as the information visibly affected her, stiffening her posture and wiping her face of all the happiness she had shown previously. Her gaze steeled and her fists clenched, "I thought he was going to be in a cell by now."

Tony raised his hand to swipe it down his face, "I know, so did I, but we need any information he has about the other Hydra pockets. We have to get rid of Hydra and we can't do that without leads."

Snow cursed under her breath, "What do you want me to do?"

"All you have to do is go in there and talk to him, cater to him just enough to get anything you can out of him and that's it. We can ship him off and that will be the last time you will ever have to see him."

I frowned at the fact he was asking this of her and didn't seem to understand the amount of stress he had just placed on her shoulders. "Tony-"

"I'll do it, but if he tries anything, I can't promise he will live past today. Do you understand?" Her voice was icy, and I was almost certain that even if Tony didn't agree with her statement, he knew he didn't have much room to argue.

He bobbed his head, "Crystal. Please come with me."

She turned to me, apology written across her face, "I'll see you later, Bucky."

I could only watch as she walked with Tony out of the room throwing one last wavering smile over her shoulder.

Tags<3

@blackbirdwitch22 / @cjand10 / @imdoingathingmom

[howzitsa]

Port Connect 65W Notebooks Adapter Lenovo The Port Connect Universal Notebook Adapter can power and charge most brands of laptops on the market. This model is designed specifically for Lenovo laptops up to 15.6″ in size. The Port Connect notebook adapter can keep your devices fully charged with ease when away from home or at the office. It comes in an ABS and PCBA designed casing that keeps the internals protected during usage. It outputs 65W of power making it ideal for laptops up to 15.6″. Port Connect universal power supplies automatically adapt to the power of your laptop to power and charge it. They also incorporate protection against overvoltage for increased safety. Check the power required by your machine by consulting its technical specifications: The 65W notebook adapter will automatically adapt to lower power laptops. FEATURES: For 65W notebooks and lower power Compatible with most Notebooks up to 15.6″ ABS and PCBA construction Adaptable output power & over-voltage protection Compatible with Lenovo Laptops SPECIFICATIONS: Input Voltage: 100V-240V AC, 50/60Hz Output Voltage: 19V DC Output Current: 3.41A Output Power: 65W max Materials: ABS + PCBA Dimensions: 5 x 11.3 x 3 cm Weight: 340 g Compatibility: Up to 15.6″ Lenovo Laptops WHAT’S IN THE BOX: Port Connect 65W Lenovo Notebook Adapter x1 1.8m DC Cable x1 1.2m 220AC Cable x1 User Manual x1

[govindhtech]

SandboxAQ News: Improve Drug Discovery With Cloud-Scale AI

SandboxAQ News

Conventional drug discovery involves huge costs, long timelines, and a shocking failure rate. A novel medication may take decades to develop from earliest research to regulatory clearance. Many intriguing pharmaceutical candidates fail due to safety or efficacy difficulties. Few applications pass regulatory and clinical testing.

SandboxAQ helps scientists forecast biological occurrences, explore broad chemical areas, and understand molecular relationships. It reduces drug discovery and development time by employing cutting-edge computational methods including active learning, absolute free energy perturbation solution (AQFEP), generative AI, structural analysis, and predictive data analytics. All of this is cloud-native.

The Design-Make-Test cycle creates, synthesises, and tests molecules for drug design. Many clients use SandboxAQ during design when their computational methods fail. SandboxAQ speeds up this cycle to assist pharmaceutical scientists introduce strong novel compounds. SandboxAQ increased chemical space from 250,000 to 5.6 million molecules in a neurodegenerative illness project, increasing hit rate and candidate drug identification 30-fold.

Scientific understanding through cloud-native development

SandboxAQ uses Google Cloud resources and infrastructure to improve flexibility and scale for large-scale computing.

Large-scale virtual screening initiatives demand cost-effective and flexible solutions. In particular, SandboxAQ developers must quickly duplicate scientific code, execute it at scale economically, and store and organise all created data.

SandboxAQ improved scalability and efficiency with Google Cloud infrastructure. They increased computational throughput by 100X and employed tens of thousands of VMs in parallel. By 90% reducing idle time, they boosted utilisation. SandboxAQ combined development and deployment on Google Cloud to expedite large-scale batch processing, machine-learning model training, code development, and testing.

What's SandboxAQ?

Cloud-based SandboxAQ is built and implemented. A cloud-based platform enables scientists and engineers self-service virtual machines (VMs) with standardised and centrally controlled environments and tools for development, while code and data live in cloud services. This matters because scientific programming requires powerful computers. Researchers can utilise GPUs or 96-core CPUs. They may also construct new computers with multiple CPU types or configurations for low-friction testing and development across heterogeneous resources, as seen below.

The bench client lets SanboxAQ scientists and developers operate and access their Bench equipment. They can use JupyterLab for a familiar notebook development flow, a browser-based VNC service for rapid remote desktops, or SSH to access to PCs.

Once the code is ready for larger scale, researchers may utilise an internal tool powered by Batch, a fully managed service to plan, queue, and execute batch tasks on Google infrastructure, to send SandboxAQ parameterised calculations as jobs. With development and batch execution environments synchronised, scaled changes may be made quickly. After posting to GitHub, bench machine code can be batch executed immediately.

SandboxAQ scientists' bench computers automatically receive new tools as they are vetted and merged into the monorepo. These scientists may run parallel processes processing millions of molecules on any Google Cloud virtual machine resource in any geographic zone using on-demand or Spot VMs.

Globally resolved transitive dependency trees in SandboxAQ facilitate package and dependency management. Google Batch can simply integrate with engineer-created tools to train multiple model instances using this method.

Due to its heavy use of machine learning, SandoxAQ needs easy data access. SandboxAQ's Drug Discovery team provides services to clients with sensitive data. Bench and batch workloads read and write data from a single IAM-controlled interface, providing the firm granular control over data sources to secure client data.

Tools to monitor these workloads, surface logs to SandboxAQ scientists, and sift huge output data with Google Cloud services are trivial to construct. When new features are tried or faults are found, updates are done instantaneously without infrastructure work by the scientific team. The code may be centralised and uniformly integrated into production apps on Google Cloud after stabilising.

SandboxAQ has less trouble building and scaling new workloads thanks to Google Cloud's unified development, batch processing, and production environment. SandboxAQ's shared environments for scientific workload research and engineering enable customers to swiftly go from experimental to production, achieving desired outcomes.

Real-world SandboxAQ solution

SandboxAQ is already affecting drug development for difficult-to-treat diseases. There are innovative relationships with Riboscience, Sanofi, the Michael J. Fox Foundation, and UCSF Professor Stanley Pruisner's group. This method, based on Google Cloud SandboxAQ, outperforms high-throughput screening, demonstrating SandboxAQ's revolutionary potential in drug research and patient care.

[huansheng33]

Paper Cardboard Cover File Folder

The backbone of our Paper Cardboard Cover File Folder is its sturdy cardboard cover material. Designed to withstand daily wear and tear, this material provides a good balance between flexibility and resilience. It offers ample protection to your documents while maintaining a professional appearance, making it ideal for both internal use and client presentations.

Jinhua Huangsheng Stationery Co., Ltd. has 6 large-scale imported production equipment plate-making machines, 1 6-color printing machine (Japan), 6 film blowing machines, and bag-cutting machines. 10 sets, 1 overlay machine, 5 beer machines, L folder fully automatic production line and more than 300 sets of a series of office stationery and household products production equipment, mainly producing and selling office stationery (file folders, file boxes, organ bags, information Albums, business card albums, photo albums, notebooks and various packaging bags, etc.) The products are exported to Europe, the United States, South Korea, Japan, and other places.

[learning-code-ficusoft]

How to Use Amazon Sage Maker for Machine Learning Projects

How to Use Amazon SageMaker for Machine Learning Projects

Amazon SageMaker is a fully managed service that enables developers and data scientists to build, train, and deploy machine learning (ML) models at scale. It simplifies the ML workflow by providing infrastructure, automation, and built-in tools.

Step 1: Setting Up Amazon SageMaker

Log in to AWS Console: Navigate to Amazon SageMaker in the AWS Management Console.

Create a SageMaker Notebook Instance:

Go to Notebook Instances → Create Notebook Instance.

Select an instance type (e.g., ml.t2.medium for small workloads).

Attach an IAM Role with permissions to access S3, CloudWatch, and SageMaker.

Wait for the instance to be in the “InService” state.

Open Jupyter Notebook: Once the instance is ready, open Jupyter and start coding.

Step 2: Data Preparation

Load Data from Amazon S3

python

import boto3 import pandas as pd s3_bucket = "your-bucket-name" file_key = "data/train.csv" s3 = boto3.client("s3") obj = s3.get_object(Bucket=s3_bucket, Key=file_key) df = pd.read_csv(obj["Body"])

Preprocess the Data

Handle missing values.

Normalize numerical features.

Encode categorical variables.

python

df.fillna(0, inplace=True) # Replace missing values with zero df = pd.get_dummies(df, columns=["category_column"]) # One-hot encoding

Step 3: Training a Machine Learning Model

Select a Built-in Algorithm

SageMaker offers built-in algorithms like XGBoost, Linear Learner, and DeepAR.

Example: Using Linear Learner for classification.

Upload Data to S3

python

from sagemaker import Session session = Session() s3_train_path = session.upload_data("train.csv", bucket=s3_bucket, key_prefix="data")

Define an Estimator and Train the Model

python

import sagemaker from sagemaker.amazon.linear_learner 

import LinearLearner role = sagemaker.get_execution_role() linear_learner = LinearLearner(role=role, instance_count=1, instance_type="ml.m4.xlarge")

 linear_learner.fit({"train": s3_train_path})

Step 4: Model Deployment

Deploy as a Real-time Endpoint

python

predictor = linear_learner.deploy(initial_instance_count=1, instance_type="ml.m4.xlarge")

Make Predictions

python

import numpy as np test_data = np.array([[5.1, 3.5, 1.4, 0.2]]) # Example input result = predictor.predict(test_data) print(result)

Step 5: Model Monitoring and Optimization

Use Amazon CloudWatch to track metrics such as inference latency and CPU usage.

Enable Model Drift Detection using SageMaker Model Monitor.

Retrain Model Automatically using SageMaker Pipelines.

Conclusion

Amazon SageMaker simplifies the ML workflow by automating data preprocessing, training, deployment, and monitoring. It is ideal for businesses looking to scale ML applications efficiently.

WEBSITE: https://www.ficusoft.in/aws-training-in-chennai/

[angel-jasmine1993]

A Deep Dive into AWS SageMaker: Your Complete Resource

In today's data-driven world, machine learning (ML) has become a cornerstone for innovation and efficiency across industries. Amazon Web Services (AWS) SageMaker stands out as a powerful platform designed to simplify the complexities of machine learning. In this blog, we will explore AWS SageMaker in detail, covering its features, benefits, and how it can transform your approach to ML.

If you want to advance your career at the AWS Course in Pune, you need to take a systematic approach and join up for a course that best suits your interests and will greatly expand your learning path.

What is AWS SageMaker?

AWS SageMaker is a fully-managed service that enables developers and data scientists to quickly build, train, and deploy machine learning models. Launched in 2017, SageMaker integrates various tools and services, which streamlines the machine learning workflow. This allows users to focus on developing high-quality models without worrying about the underlying infrastructure.

Key Features of AWS SageMaker

1. Integrated Jupyter Notebooks

SageMaker provides built-in Jupyter notebooks for data exploration and visualization. This feature allows users to prototype and test different algorithms in a user-friendly environment, eliminating the need for external setups.

2. Diverse Algorithms and Frameworks

SageMaker offers a variety of built-in algorithms optimized for speed and efficiency. It also supports popular frameworks, such as TensorFlow, PyTorch, and MXNet, giving users the flexibility to work with their preferred tools.

3. Automated Hyperparameter Tuning

One of SageMaker’s standout features is its automatic hyperparameter tuning, which optimizes model parameters to enhance performance. This automation saves time and improves model accuracy without requiring extensive manual effort.

4. Scalable Training and Inference

With SageMaker, users can train models on large datasets quickly using distributed training. Once a model is trained, deploying it for inference is straightforward, allowing for real-time predictions with ease.

5. Model Monitoring and Management

SageMaker includes robust tools for monitoring the performance of models in production. This feature ensures that models remain effective over time, with capabilities for automatic retraining when necessary.

6. SageMaker Studio

SageMaker Studio is an integrated development environment (IDE) that consolidates all aspects of the machine learning workflow. It allows users to build, train, and deploy models while facilitating collaboration among team members.

To master the intricacies of AWS and unlock its full potential, individuals can benefit from enrolling in the AWS Online Training.

Benefits of AWS SageMaker

Cost-Effective Solutions

SageMaker operates on a pay-as-you-go pricing model, allowing businesses to scale their machine learning operations without significant upfront investment. You only pay for the resources you consume.

Accelerated Time to Market

By streamlining the ML lifecycle, SageMaker helps teams transition from experimentation to production faster. This acceleration is crucial for organizations looking to capitalize on AI-driven applications quickly.

Increased Accessibility

The user-friendly interface and comprehensive documentation make SageMaker accessible to users at all skill levels, from novices to experienced data scientists.

Robust Security and Compliance

Being part of the AWS ecosystem, SageMaker benefits from advanced security features and compliance certifications, making it suitable for handling sensitive and regulated data.

Conclusion

AWS SageMaker is a game-changer for organizations aiming to harness the power of machine learning. By simplifying the model-building process and providing a robust set of tools, SageMaker enables businesses to unlock insights from their data efficiently.

Whether you are just starting your machine learning journey or looking to enhance your existing capabilities, AWS SageMaker offers the resources and flexibility you need to succeed. Embracing this powerful platform can lead to innovative solutions, driving growth and competitive advantage in today’s marketplace.

[aryaindustry]

Notebook Making Machine Price in Patna: Arya Industries

In the bustling industrial landscape of Patna, Arya Industries stands out as a leader in manufacturing and supplying cutting-edge notebook-making machines. As education continues to be a cornerstone of growth in Bihar, the demand for quality notebooks has surged, paving the way for lucrative business opportunities. Arya Industries caters to this demand with its high-performance machines that are both efficient and affordable.

Affordable Notebook-Making Machines Price for Every Business

Arya Industries takes pride in offering a range of notebook-making machines tailored to suit different scales of operation, from small startups to large-scale manufacturing units. The price range of these machines in Patna depends on various factors such as machine capacity, features, and automation level.

Basic Manual Notebook-Making Machines These machines are perfect for small businesses or individuals starting their ventures. Priced between ₹1,50,000 to ₹3,00,000, these models are cost-effective and easy to operate, requiring minimal maintenance.

Semi-Automatic Notebook-Making Machines Designed for mid-sized enterprises, semi-automatic machines strike a balance between affordability and efficiency. The price for these models typically ranges from ₹4,00,000 to ₹8,00,000, depending on the production capacity and features.

Fully Automatic Notebook-Making Machines For large-scale manufacturers, fully automatic machines are the ideal choice. These advanced machines, priced between ₹10,00,000 to ₹20,00,000, offer high-speed production and superior precision, significantly reducing manual intervention.

Factors Influencing Notebook-Making Machine Prices in Patna

Production Capacity: Machines with higher production capacity are priced higher due to their enhanced efficiency and ability to produce more notebooks in less time.

Technology and Features: Advanced features such as automatic paper cutting, ruling, and binding increase the cost of the machine.

Material Quality: Machines built with durable and high-quality materials are priced slightly higher but ensure longevity and reliability.

Brand Reputation: Arya Industries, being a trusted brand in Patna, offers machines that combine quality with affordability, ensuring a competitive edge.

Why Choose Arya Industries for Notebook-Making Machines?

High-Quality Machines: Arya Industries uses state-of-the-art technology to manufacture machines that deliver consistent performance and superior output.

Affordable Pricing: The company ensures competitive pricing to support businesses of all sizes.

Customization Options: Arya Industries provides machines tailored to specific business needs, whether it’s for small-scale production or high-volume manufacturing.

After-Sales Support: With excellent customer service, the company ensures seamless installation, training, and maintenance for all its machines.

Empowering Businesses in Patna

Arya Industries is committed to empowering entrepreneurs in Patna by providing them with the tools they need to thrive in the competitive notebook manufacturing industry. With flexible pricing options and robust machines, the company ensures that every business has access to quality machinery without straining their budget.

Conclusion

If you’re planning to venture into notebook production, Arya Industries is your trusted partner in Patna. Offering top-notch machines at affordable prices, the company guarantees reliability and efficiency. Contact Arya Industries today to explore the best notebook-making machines and take the first step toward building a successful business.

[armindindustries]

The fully automatic notebook-making machine streamlines the entire production process by automating key tasks such as cutting, ruling, stitching, and binding. Designed for high efficiency and precision, it significantly reduces manual labour and increases production speed, making it ideal for large-scale notebook manufacturers. Capable of handling various notebook sizes and formats, it ensures consistent quality with every batch. Its user-friendly interface allows for easy operation and minimal maintenance, while its robust design ensures long-term durability. Perfect for businesses aiming to improve productivity, reduce costs, and deliver high-quality notebooks in a competitive market.

[colgos]

HANGZHOU CAIBA TECHNOLOGY CO. LTD - Book Casing in Machine

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[devstreeau]

Best Machine Learning Tools for Success

Introduction:

In the dynamic field of software development, the infusion of machine learning tools has become a pivotal force, reshaping conventional methodologies and expediting innovation. This article delves into the significance of six advanced machine learning tools, spotlighting their applications, advantages, and real-world success stories.

Microsoft Azure Machine Learning

Azure Machine Learning, a cloud platform, empowers developers to construct, train, and deploy AI models. Microsoft consistently enhances its machine learning tools, recently retiring the Azure Machine Learning Workbench in favor of more streamlined options.

IBM Watson

Watson Machine Learning, an IBM cloud service, leverages data to operationalize machine learning and deep learning models. Suited for building applications through API connections, IBM Watson facilitates essential machine learning operations like training and scoring.

Google TensorFlow

TensorFlow, an open-source library developed by Google, is a dataflow programming framework for research and production. Its rapid evolution and user-friendly neural network visualization make it attractive to developers.

Amazon Machine Learning

Amazon offers a robust suite of machine learning tools, including Amazon Machine Learning, a managed service for model building and predictions. Simplifying processes further, it incorporates an automatic data transformation tool. Amazon SageMaker, another offering, provides a fully managed platform for developers and data scientists.

Apache Mahout

An open-source machine-learning library, Apache Mahout, delivers scalable and efficient implementations of various algorithms. Key features include scalability, flexibility supporting multiple programming languages, and extensibility for building custom algorithms.

Benefits of Integrating Machine Learning Tools in Software Development:

The amalgamation of these machine learning tools yields numerous benefits for software development teams, enhancing efficiency, expediting development cycles, and future-proofing projects with cutting-edge technology. This article underscores how embracing these tools empowers developers to lead in technological advancements, ensuring project success in a competitive landscape.

Challenges and Considerations:

While the advantages are substantial, integrating machine learning tools presents challenges. This section addresses potential obstacles and provides best practices for overcoming them, emphasizing security and ethical considerations for a responsible and sustainable development environment.

Conclusion:

Devstree Australia underscores the transformative impact of integrating machine learning tools into software development. Encouraging developers to explore tools like TensorFlow, Scikit-Learn, PyTorch, Jupyter Notebooks, Apache Spark MLlib, and Keras, the article highlights the potential for unlocking new possibilities, enhancing efficiency, and fostering innovation. This journey towards revolutionizing software development allows developers to navigate the evolving technological landscape confidently, ensuring unprecedented project success. Devstree Australia stands as a guiding force, empowering developers to harness the full potential of cutting-edge machine learning tools for a future-ready and competitive software development environment.

[howzitsa]

Port Connect 65W Notebook Adapter Dell The Port Connect Universal Notebook Adapter can power and charge most brands of laptops on the market. This model is designed specifically for Dell laptops up to 15.6″ in size. The Port Connect notebook adapter can keep your devices fully charged with ease when away from home or at the office. It comes in an ABS and PCBA designed casing that keeps the internals protected during usage. It outputs 65W of power making it ideal for laptops up to 15.6″. Port Connect universal power supplies automatically adapt to the power of your laptop to power and charge it. They also incorporate protection against overvoltage for increased safety. Check the power required by your machine by consulting its technical specifications: The 65W notebook adapter will automatically adapt to lower power laptops. FEATURES: For 65W notebooks and lower power Compatible with most Notebooks up to 15.6″ ABS and PCBA construction Adaptable output power & over-voltage protection Compatible with Dell Laptops SPECIFICATIONS: Input Voltage: 100V-240V AC, 50/60Hz Output Voltage: 19V DC Output Current: 3.41A Output Power: 65W max Materials: ABS + PCBA Dimensions: 5 x 11.3 x 3 cm Weight: 340 g Compatibility: Up to 15.6″ Dell Laptops WHAT’S IN THE BOX: Port Connect 65W Dell Notebook Adapter x1 1.8m DC Cable x1 1.2m 220AC Cable x1 User Manual x1

[techthrivesolutionspvtlimited]

Top AI Tools for UI and UX Designers

Artificial intelligence (AI) in design allows designers to automate their workflow fully. Tools with artificial intelligence enhancements for user interface and user experience design make every aspect of the design process much faster and easier. Let’s look at the top artificial intelligence (AI) design tools on the market and discuss how UI/UX designers can use them.

Uizard

Uizard is a widely used system that automates the learning of human-like graphical user interface comprehension. To create a native mobile app, all a designer needs is a drawing and some AI help. Not only does it produce code from the sketch, but it also automates the design process. Uizard is an excellent tool for user-testing your design and the flow.

Chat GPT

The design process can benefit greatly from using Chat GPT. It can help with everything from design inspirations and ideas for user research to accessibility solutions, thanks to its ability to generate content-based text prompts. UX designers can get help from ChatGPT in making their digital goods more user-friendly and interesting. Designers may enhance the user experience by providing better content, and the tool can assist them.

Khroma

Khroma is a state-of-the-art color tool developed for designers who want to reduce the time spent choosing colors. You may create an infinite number of color schemes with Khroma by selecting 50 of your favorite colors and then training the AI algorithm to recognize countless more similar hues. Text, Poster, Gradient, and Picture are just a few of the categories into which these color schemes fall, allowing them to be easily accessed by the user.

Let’s Enhance

Let’s Enhance is a robust artificial intelligence tool that helps designers up-res images without losing quality. Images can be magnified up to 16 times without any discernible quality loss. High resolutions with no human intervention are possible with this tool for designers.

Balsamiq

When envisioning the final form of their creation, most designers place a premium on keeping things as straightforward as possible. They can get some help with this process by using Balsamiq. It aspires to provide a digital equivalent to doodling on a notebook or whiteboard. The greatest benefit of this instrument is that it frees the designer to concentrate on the substance of the work rather than the presentational aspects, such as color, font, and layout, which may be adjusted later.

Mockplus

Every designer in the age of AI-driven design requires Mockplus. This tool allows designers to automate their workflow by exporting their work directly from programs like Sketch, Photoshop, and Adobe XD. It’s also helpful because designers may use it to see premade specs and create interactive prototypes.

Beautiful.ai

The presentation software Beautiful.ai is changing the way people create stunning visual papers. Anyone can create stunning presentations in minutes with their Design AI. It’s simple to get started with over 70 professionally designed slide templates. Your slide will instantly change to accommodate any new text you add.

Flair AI

Flair is an AI-powered design tool that helps customers easily, rapidly, and economically creates high-quality marketing assets for their brands. With Flair, users can set up elaborate photoshoots in seconds, take pictures of their products anywhere, and stay true to their brand’s aesthetic.

Galileo AI

From a plain text specification, Galileo AI generates Figma-editable UI designs. You should be able to design more quickly than ever with the help of the initial automated design. The developers state that hundreds of successful designs were used to teach the algorithm.

Adobe Sensei

Adobe Sensei is the artificial intelligence (AI) and machine learning platform that supports a wide range of Adobe’s creative software. Content-aware fill, face-aware liquify, automatic colorization, and animation are some of the time-saving features made possible by Adobe Sensei. It also can create realistic audio and visual content from scratch or depending on user input.

Figma

Figma is a web-based UI/UX design tool that enables real-time design creation, collaboration, and prototyping. Figma can generate icons, logos, graphics, color palettes, typefaces, layouts, and more with the help of various artificial intelligence algorithms. Iconscout, Logo Lab, Unsplash, Font Pairing AI, Auto Layout, and many others fall under this category.

Components AI

You can make your design from the start or use premade, created designs with Components AI. Additionally, it is simple to create designs for various screen resolutions, share those ideas with coworkers simply, and work together on multiple projects. Users may also export their designs into code forms like React, JS, JSON, JSX, SVG, PNG, HTML, CSS, CSS custom properties, and Sass and Components AI is compatible with the complete Google Font Library.

Marvel

When making UI/UX designs for online and mobile apps, Marvel is another prototype and collaboration tool that might help. The different artificial intelligence technologies that Marvel is compatible with can help you with various design-related activities, including creating icons, logos, graphics, color palettes, typefaces, layouts, and more.

Landbot

Create conversational UI and UX for your websites and apps with the help of Landbot, a chatbot builder. There are several ways in which Landbot’s AI may assist you. These include the generation of interesting conversations, the identification of user intent, the customization of responses, and more.

Wix

Wix is a website builder that lets you make professional-looking sites without knowing how to code. Wix also employs AI to aid in selecting a template, modifying the design, enhancing search engine optimization, and other similar activities. Wix ADI (Artificial Design Intelligence) is Wix’s AI tool, and it uses your responses to a few questions to design a unique website for you.

Canva

Canva is a web-based application for making eye-catching graphics for online and offline marketing materials, including social media posts, presentations, posters, and flyers. Canva also uses AI to assist you in locating the most appropriate visuals, typefaces, hues, and structures for your design.

InVision

To make your UI/UX designs come to life, use InVision, a prototype and collaboration tool. You may use AI in InVision to assist with animation, transition, gesture, voice interaction, and more. Freehand, an AI tool in InVision, simplifies the development of interactive prototypes.

Techthrive Solutions is based out of Bangalore, India. We are a cross-disciplinary design team that loves to create great experiences and make meaningful connections for businesses and their users through UI & UX. Techthrive Solutions is the best UI&UX design company in Bangalore company that encompasses services from creating your Brand identity through digitally defining your ideas and focusing on getting your products & services to the ever growing digital market thereby increasing your brand’s awareness, sales and desire. We are passionate to craft your brand’s digital journey.

[akhil-1]

Data Engineering Course in Ameerpet | AWS Data Engineering Training

AWS Encoding categorical values

AWS Data Engineering involves designing, implementing, and managing data architecture and infrastructure on the Amazon Web Services (AWS) cloud platform. It encompasses a range of tasks, including data extraction, transformation, and loading (ETL), data integration, and the creation of scalable and efficient data pipelines. When working with categorical values in the context of AWS (Amazon Web Services), one common task is encoding these categorical values into a format that machine learning models can understand. This is often referred to as feature encoding or one-hot encoding. AWS provides several services that can be used for this purpose, including AWS Glue, SageMaker, and others.

Here's a general guide on how you might perform encoding of categorical values using AWS services

AWS Data Engineering Online Training

AWS Glue:

AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy to prepare and load data for analysis. You can use Glue for encoding categorical values in your dataset.

Define a Glue job: Create a Glue ETL job in the AWS Glue console.

Specify the source and target: Define your source data (e.g., in Amazon S3) and the target location for the transformed data.

Transform the data: Use the Glue job script to perform one-hot encoding or other encoding methods on the categorical columns.

Save the transformed data: Store the transformed data in a new location, such as another Amazon S3 bucket.                     - AWS Data Engineering Training

Amazon SageMaker:

Amazon SageMaker is a fully managed machine learning service that you can use to build, train, and deploy machine learning models.

Notebook Instances: You can use a SageMaker notebook instance to write Python code for data pre-processing. Libraries like sickie-learn or pandas can be used for one-hot encoding.

SageMaker Processing Jobs: Use SageMaker Processing Jobs to run your pre-processing script at scale, handling large datasets.

SageMaker Autopilot: SageMaker Autopilot is a service that automatically builds, trains, and tunes machine learning models. It can handle categorical data during the feature engineering process.

                                                         - Data Engineering Training in Hyderabad

AWS Data Pipeline:

 AWS Data Pipeline is a web service for orchestrating and automating the movement and transformation of data between different AWS services.

Define a pipeline: Set up a data pipeline to move data from source to destination.                                 

Use AWS Data Pipeline activities: Configure activities in the pipeline to perform data transformations, including encoding of categorical values.

AWS Lambda with Step Functions:

You can create an AWS Lambda function to handle the encoding logic.

Use AWS Step Functions to orchestrate the Lambda function and other processing steps.              - AWS Data Engineering Training in Hyderabad

Remember, the specific approach depends on your use case, the size of your dataset, and the tools you are comfortable using. Always consider the requirements of your machine learning model and the characteristics of your data when choosing an encoding strategy.

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[armindindustriesbihar]

Armind Industries is the manufacturer and supplier of best quality of different kinds of notebook making machine such as manual, automatic, and semi-automatic notebook machine helps in the production of notebooks.

[singularityhacker]

7 Power Laws of the Technological Singularity

When people talk about the technological singularity they usually do so exclusively in the context of Moore’s Law. But there are several Moore’s Law-like laws at work in the world and each of them is equally baffling. I’m referring to this list of trends as “power laws” because of the nature of their incredible rate of growth and because they independently work as pistons driving the engine of the singularity. A few things to note about these power laws. Firstly they are just observations. There are no, known, deeper physical principles in the universe that would lead us to believe that they must hold true. Secondly, we’ve observed these trends long enough to warren their recognition as power laws and there is no evidence or signs of their stagnating. We’ll start with the most famous and well-known power law and work our way through the others.

1. Moore’s Law

Moore’s Law states that transistors on a chip double about every two years and that the cost of that doubling halves. This is a double-edged sword. It means that the next model computer will be way faster than the previous but it also means that the value of your existing computer is dropping rapidly. The end of Moore’s Law has been proclaimed for a long time but there seems to be no end to its progression.

As we reach the physical limits of transistor sizes, entirely new hardware architectures are developed that sustain the progression. Things like 3D chips, specialized chips, and non-silicon based chips like photonics, spintronics, and neuromorphic chips are being developed and will ensure that this law continues.

“Regular boosts to computing performance that used to come from Moore’s Law will continue, and will instead stem from changes to how chips are designed.” — Mike Muller, CTO at ARM

What this does not mean is that a user’s experience of computer speed will increase. We tend to be more sloppy with application development when it’s cheap to make up for it with hardware horsepower. There is a standing joke that the same amount of computing resources that were used to send astronauts to the moon in the ’60s is now accidentally used by a sluggish browser tab.

2. Kryder’s Law

The second law driving our propulsion into the technological singularity is Kryder’s Law. It states, loosely, that digital storage doubles every year. It specifically has to do with magnetic storage but the principle is applicable to all digital storage as you will see. While you may not see this law exactly played out in the price of external drives in your local Best Buy, you can see it if you consider the price of cloud storage services.

Let’s look at the current top cloud storage providers. Apple offers two terabytes of cloud storage for about $10 a month. Google offers the same space for the same price as well as 10 terabytes for about $100 a month. After that, users can get 20 terabytes for $200 a month, 30 terabytes for $300 a month, and so on. Dropbox offers yet a similar package but with extras like full-text search for $20 a month. Lastly and most competitive is Amazon offering an incredible $0.004 per gigabyte per month through its Glacier storage service. When you take these cloud providers into account and consider that they will only grow via economies of scale, you see that Kryder’s law is in full effect.

Note that this also doesn’t even take into consideration innovations like Filecoin that actually distribute Kryder’s Law by allowing anyone with storage capacity to rent that space out. You could look at it like Uber or Airbnb for digital storage. This highlights the idea that this digital power law, like the others, should not be strictly tied to a hardware implementation. Similar to how Moore’s Law continues but not strictly through cramming more transistors on a chip but through new engineering architectures. The same principle applies.

3. Nielsen’s Law

Thirdly, we have Nielsen’s Law. If the previous laws could be summarized as computation and storage, this one is summarized as throughput. It states that bandwidth grows by 50% a year. More precisely, it states that the bandwidth of high-end users grows by 50% a year. That’s just 10% less annual growth than Moore’s Law.

In practice, we don’t see this linear growth and there are three reasons for it. One, Telecom companies are conservative. It cost billions of dollars to update their sprawling hardware. Two. The immediate impact of the end-user is not a guaranteed faster experience if they do upgrade their infrastructure. You can have the fastest hardware in the world on your street but that doesn’t automatically make the rest of the countries hardware faster. That slow loading web page may only be imperceptible faster after your area’s hardware is upgraded. Lastly, as new people get online, it’s more likely they are using older slower devices so the average expected speed is kept pressed down by these newcomers.

Since 1G was introduced in the 1980s, new wireless technology has been released every ten years. The advent of 1G introduced mobile telephony. Than 2G in the ’90s brought about global roaming and SMS. The 200’s saw 3G and smartphones with data. 2010 introduced 4G and mobile broadband. The year 2020 will be the year of 5G and the realization of the fully ubiquitous cloud. To put this in perspective, let’s say you wanted to download the newest episode of your favorite television show. At 800MB it would have taken 8 hours hrs to download in 1998, 5 hrs in 2001, 45 minutes in 2009, and 1 second with the new wireless protocol.

The impact and roll out of 5G will be enormous. With current networks, it takes about 100 milliseconds for information to travel across a network. With 5G, that latency will be reduced to 1 millisecond. We are talking about downloading full-length 5k movies in less than a second, surgeons controlling surgical robots in real-time from across the country, smart cities, and smart car-to-car communications.

4. Koomey’s Law

Koomey’s law has to do with the efficient use of energy and states that the number of computations per joule of energy dissipated has been doubling approximately every 1.57 years. This trend has been stable since the 1950s and has been faster than Moore’s law. Jonathan Koomey reframed the trend as follows: “at a fixed computing load, the amount of battery you need will fall by a factor of two every year and a half”. You can see the effect of this law in today’s newest generation CPU’s (Apple’s M1 chip) that are pumping out incredible amounts of processing power at significantly reduced levels of energy consumption.

5. Metcalfe’s Law

This power law with its closely associated cousin, the network effect, asserts that the value of a network is proportional to how many users are a part of it and that the addition of a new member adds value to all the existing members. A good example of this power law at work are social media sites like Facebook and YouTube. These sites had no revenue model in the beginning and were very expensive to run but grew to have so many users that the value grew directly from the value of the size of the network itself. Not too many years ago, software products had to packaged on physical media and shipped through the mail to users. Now, the same products can be built and deployed to one of any number of app stores and have a global audience with little to no overhead.

6. Hendy’s Law

Next, consider Hendy’s Law. Hendy’s Law states that the number of pixels per dollar in a digital camera doubles every two years. We can generalize this trend to encompass the idea that our ability to capture images and video of the world is exponentially improving year over year. This improvement opens the door to such high-fidelity VR and lifelogging that our human senses begin to find synthetic media and real-life indistinguishable. This already exists in the form of gigapixel photography where images are used instead of real specimens in biological study where we can’t tell the difference even under a microscope. Imagine being able to photograph a group of people and then zoom in so close later that you can identify properties of their cellular biology.

7. Bell’s Law

Last on our list is Bell’s Law. It says a new class of smaller, cheaper computers comes along about every decade. With each new class, the volume shrinks by two orders of magnitude, and the number of systems per person increases. The law has held from 1960s’ mainframes through the ‘80s’ personal computers, the ‘90s’ notebooks, and the new millennium’s smart phones. This is likely manifesting in the realm of wearable right now with the wild success of smart watched and wireless intelligent earbuds.

To wrap this up and summarize, while there may be temporary or geographically isolated stalls in the progression of these laws, they are still holding steady. You might compare them to walking up a set of stairs. At various points in your travel up the stairs, you rise up very high and then drop low. You do not move at a constant linearly increasing height. You go up and down but the trend is a clear move upwards. Through that up and down, you are converging on a net increase. The same is true of these digital laws. The overarching result is that software is eating the world and eating itself, recursively accelerating the process even further. One doesn’t need to theorize about potential advances in machine intelligence to see that we are accelerating into an unimaginable future. A clear technological singularity.

If none of the above convinces I will leave you with this chart illustrating the grown of the global economy. Assuming the continuation of these power laws, where are we 50–100 years from now?