Strong beginnings learning center: Strong Beginnings Learning Center

Опубликовано: August 17, 2023 в 4:52 pm

Автор: alexxlab

Категории: Miscellaneous

Strong Beginnings | Early Learning Center

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Strong Beginnings Early Learning Center is a faith-enriched, caring & developmentally appropriate educational child care center for children for ages 6 weeks – 4 years. Strong Beginnings Preschool supports full & part-time developmental, social, pre-academic skills, and Christian education for children ages 4 to 6 years. Strong Beginnings is a Non-Profit 501C3.

“My child has attended Strong Beginnings Early Learning Center since she was 6 weeks old and is now almost 3. The teachers that help provide care today are some of the same teachers that held, rocked, and nurtured my baby when I had to return to work after maternity leave. Being a working mother, it is difficult to be without your baby while at work and I have always had confidence and trust in every teacher providing care for my daughter. She is now in the Preschool 1 classroom which provides an excellent developmental curriculum for her. I am proud to say my daughter attends Strong Beginnings Early Learning Center and so grateful for all they do for our family.”

“Faith-based education is integral to the care my child has received at Strong Beginnings.”

“Strong Beginnings has a dedicated staff and board. They have worked tirelessly to keep the center open, a challenge in such a small town. We know many families that would not be able to work were it not for Strong Beginnings. And we know what a financial struggle it can be to operate the center. Strong Beginnings relies on donations and fundraisers throughout the year to maintain their high level of care.”

“I know that my children are learning what they need for their age and to help prepare them for school. If anyone asked about childcare in Neoga, I would highly recommend Strong Beginnings.”

“The staff care very much for these children and all of the families that have Strong Beginnings for their childcare are blessed to have this facility. ”

“The staff was very supportive of our family growing even when my husband and I were a little nervous about having 2 little ones so close in age. My son started going to Strong Beginnings at 12 weeks. The staff have cared for him in a way that I will always be thankful for. It is hard when you are a parent who works outside the home, but it is easier to return to work when you know that your children will be so well cared for.”

“Our children are gifts from God and knowing that they are receiving the best care each and every day is the best feeling.”

“As a former faith-based educator, I know what it takes to create a positive school culture. Strong Beginnings Early Learning Center continues to do that and more. As a working mother I know the value of affordable, high-quality child care. Strong Beginnings is like a part of our family.”

“The staff is well educated on normal childhood development and social and emotional development.”

“I have been taking my children to this center since it opened. I love it. I am a single mom of 2 they have helped me out more times than I can say. I work some crazy hours and this center has saved me so I can be at work. My children have a safe and good place to go and I know that I can trust the people there. When I need something, they are here to help me whenever I need it. My children have learned a lot from them.”

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Help Us Grow Stronger!

At Strong Beginnings our passion is children. Their safety, their education and their well-being are of utmost importance and we strive everyday to provide an environment for them to grow and learn. Strong Beginnings ELC is a non-profit and provides quality care with an amazing group of teachers. Our curriculum is faith-enriched and children ages 6 weeks to 6 years benefit. We employ 12 staff members and are often in need of financial support for scholarships, operational costs, educational materials and equipment. We need your support. Any amount is appreciated to invest in our future by investing in children! (Strong Beginnings is a 501c3 and all donations are tax deductible. )

Thank You,

Forms & Rates

Forms:

Strong Beginnings Enrollment Application

Please return this completed application along with the non-refundable $25 Registration Fee. Checks should be made out to Strong Beginnings. You will be contacted by the director to confirm enrollment. Download file to learn more.

Download File

Rates:

Registration Fee
$25.00 deposit reserves your spot.

Infant
Full Week: $140

Toddler
Full Week: $135

Preschool 1
Full Week: $130

Preschool 2
Full Week: $130

Child Care Assistance Program (CCAP)

DHS’ Child Care Assistance Program provides low-income, working families with access to quality, affordable child care that allows them to continue working and contributes to the healthy, emotional and social development of the child. Families are required to cost-share on a sliding scale based on family size, income and number of children in care.

About Us

Infants
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Toddlers
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Preschool 1
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Preschool 2
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Infant Room

Our infant room holds a maximum of 8 children and we begin taking infants at 6 weeks of age. We provide a nurturing environment that helps our infants grow and develop. The infants are constantly learning, changing, and growing so quickly! The caregivers in the infant room are always responsive to the infants ever-changing needs, including feeding, changing, and comforting the children as needed!

Toddler Room

Toddlers are so fun!! There are many small and large motor activities that are encouraged and supported though out the day to help develop overall motor skills. Emerging language skills are reinforced and supported. Learning through play, art and music are the tasks of children in this room. Songs and books are also used daily to help promote literacy skills. Beginning self-care and independence are encouraged and supported as well. Bible stories and songs are presented and experienced.

Preschool 1

Children at this age will grow in language as well as fine and gross motor skills, through daily experiences in the classroom. Art and sensory activities are added at this age. Weekly themes include Bible stories and songs, with projects, play and music, which all help our little ones develop through hands on experience and language development. The Preschool 1 classroom is all about growing independence! Our staff works diligently with parents to assist with toilet training. Also, we begin letters and numbers with early reading and math skills. Through the use of monthly and weekly themes, simple science and social studies concepts are introduced. Colors, shapes, language and social skills, as well as fine and gross motor skills are emphasized.

Preschool 2

By meeting the needs of individual children and setting age appropriate goals, children learn at their own pace and are motivated and challenged to learn. Children will practice social skills: cooperation, listening, sharing, taking turns, and following directions. They will be introduced to fine and large motor skills and creative expression through dramatic play, music and art, colors, shapes, body parts and more. The letters will be taught through a letter of the week curriculum that includes letters, sounds, rhyming and blending. At Strong Beginnings we follow God’s Little Explorer Curriculum and help this age group prepare for kindergarten. Name recognition, writing, and small and large group activities, as well as experiences in a wide variety of social studies and science activities through thematic units will keep children excited about school. They will learn about God’s love, and about Jesus, their Savior, who loves them. These classes challenge children to learn school skills: enthusiasm for learning, persistence, motivation, as well as creative and critical thinking.

%23forms | Strong Beginnings | Early Learning Center

“Faith-based education is integral to the care my child has received at Strong Beginnings.”

“Our children are gifts from God and knowing that they are receiving the best care each and every day is the best feeling.”

“The staff is well educated on normal childhood development and social and emotional development.”

“The staff care very much for these children and all of the families that have Strong Beginnings for their childcare are blessed to have this facility.”

“I know that my children are learning what they need for their age and to help prepare them for school. If anyone asked about childcare in Neoga, I would highly recommend Strong Beginnings.”

Donate

Thank You,

Forms & Rates

Forms:

Download File

Rates:

About Us

Infants
learn more
Toddlers
learn more
Preschool 1
learn more
Preschool 2
learn more

Infant Room

Toddler Room

Preschool 1

Preschool 2

What is boosting? – Boosting in Machine Learning Explained – AWS

What is Boosting in Machine Learning?

Boosting is a technique used in machine learning to reduce errors in predictive data analysis. Data scientists train machine learning software called machine learning models on labeled data to make assumptions about unlabeled data. One machine learning model can make prediction errors depending on the accuracy of the training dataset. For example, if a cat identification model was trained only on images of white cats, in some cases it may incorrectly identify a black cat. Boosting attempts to overcome this problem by training multiple models sequentially to improve the accuracy of the entire system.

Why is boosting important?

Boosting improves the prediction accuracy and performance of models by transforming weak classifiers into a single strong learning model. Machine learning models can be divided into weak or strong:

Weak models

Weak models have low predictive accuracy, which can be compared to random guessing. Such models are prone to overfitting: they cannot classify data that is very different from the original set. For example, if you teach a model to identify cats as animals with pointed ears, it will not be able to recognize a cat with folded ears.

Strong models

Strong models have higher prediction accuracy. Boosting transforms a system of weak models into a single strong learning algorithm. For example, to recognize an image of a cat, it combines a weak model classifying pointy ears and another model classifying cat eyes. After analyzing the image of the animal for the presence of pointed ears, the system analyzes it again to recognize the cat’s eyes. This approach improves the overall accuracy of the system.

How does boosting work?

To understand how the boosting algorithm works, you need to understand how machine learning models make decisions. Despite the variety of implementations, data scientists often use boosting with decision trees:

Decision trees

Decision trees are machine learning data structures that divide a data set into smaller subsets based on their characteristics. The idea is that decision trees split the data repeatedly until only one class remains. For example, a tree might ask a series of yes or no questions and categorize the data at each step.

Boosting – ensemble method

Boosting builds ensembles of models by successively combining several weak decision trees. The output of individual trees is assigned weights. The misclassifications from the first decision tree are then given more weight, after which the data is passed to the next tree. After numerous cycles, boosting combines weak classifiers into one powerful prediction algorithm.

Difference between boosting and bagging

Boosting and bagging are two common ensemble methods that improve prediction accuracy. The main difference between them is the teaching method. In the case of bagging, data scientists improve the accuracy of weak models by training some of them in parallel on different datasets. Boosting trains weak models sequentially.

The learning method depends on the type of boosting called algorithm. In this case, the algorithm performs the following general steps for training the boosting model:

Step 1

The boosting algorithm assigns equal weight to each data sample. It passes data to the first model, called the underlying algorithm. For each data sample, the underlying algorithm makes predictions.

Step 2

The boosting algorithm evaluates model predictions and increases the weight of samples with larger errors. Also the weight is assigned based on the performance of the model. The model with the best predictions will have a big impact on the final decision.

Step 3

The algorithm passes the weighted data to the next decision tree.

Step 4

The algorithm repeats steps 2 and 3 until the training errors fall below a certain threshold.

What types of boosting are there?

Three main types of boosting are listed below:

Adaptive boosting

Adaptive boosting (AdaBoost) is one of the earliest boosting models. It adapts and independently corrects the classifiers in each boosting iteration.

AdaBoost initially assigns the same weight to each data set. It then automatically adjusts the sample point weights after each step in the decision tree. Items that were classified incorrectly gain more weight in the next iteration. The process is repeated until the residual error or the difference between the actual and predicted values falls below an acceptable level.

AdaBoost can be used with many predictors. Also, it is less sensitive than other boosting algorithms. AdaBoost is not as efficient when it comes to correlation between features or using high dimensional data. In general, AdaBoost copes with qualification tasks.

Gradient boosting

Gradient boosting (GB) is similar to AdaBoost: it is also a progressive learning method. The difference between AdaBoost and GB is that GB does not assign more weight to misclassified elements. Instead, the GB software optimizes the loss function by generating base models sequentially, so that the current base model is always better than the previous one. Unlike AdaBoost, the GB method tries to generate accurate results right away, rather than correct errors. For this reason, the GB method gives more accurate results. Gradient boosting is suitable for both classification and regression problems.

Extreme Gradient Boost

Extreme Gradient Boost (XGBoost) improves gradient boosting in various ways, focusing on computational speed and model scale. XGBoost is designed for efficient multi-core parallel processing right during training. This boosting algorithm is an effective tool for working with big data. The key features of XGBoost are parallelization, distributed computing, cache optimization, and external computing.

What are the main benefits of boosting?

The main advantages of boosting are:

Ease of implementation

Boosting has algorithms that are easy to understand and interpret and can learn from their mistakes. These algorithms do not require pre-processing of data, and also have built-in procedures for handling missing values. In addition, most languages have built-in libraries for implementing boosting algorithms with many parameters that allow you to fine-tune performance.

Bias reduction

Bias is the presence of uncertainty or inaccuracy in machine learning results. Boosting algorithms combine several weak models into a sequential method that iteratively improves observations. This approach helps reduce the high bias that is common in machine learning models.

Algorithm performance

Boosting algorithms focus on elements that improve prediction accuracy during training. They are able to reduce the number of data attributes and handle large sets efficiently.

What are the disadvantages of boosting?

The following are the main disadvantages of boosting:

Vulnerability to outliers in data

Boosting models are vulnerable to outliers or data values that differ from other datasets. Outliers can significantly skew the results as each model attempts to correct the errors of the previous one.

Real-time implementation

Because this algorithm is more complex than other processes, it can be difficult to implement real-time boosting. Boosting is highly adaptive, so you can use a variety of model parameters that directly affect its performance.

How can AWS be used for boosting?

AWS Network Services is designed to provide businesses with the following tools:

Amazon SageMaker

Amazon SageMaker brings together a rich set of machine learning capabilities. This solution can be used to quickly build, train, and deploy high-quality machine learning models.

Amazon SageMaker Autopilot

Amazon SageMaker Autopilot takes the pains out of creating machine learning models by letting you automatically build and train models based on the data you use. SageMaker Autopilot simply provides a dataset in tabular form and selects a target column for prediction, which can contain a numeric value or a category designation. SageMaker Autopilot automatically learns the available solutions and selects the best model from them. From the same interface, you can deploy the resulting model to production with just one click, or explore different solutions sequentially in Amazon SageMaker Studio to further improve the quality of the model.

Amazon SageMaker Debugger

The Amazon SageMaker Debugger makes it easy to optimize machine learning models by collecting real-time training metrics and sending alerts when errors are found. This approach allows you to instantly correct inaccurate model predictions (for example, incorrect image identification).

Amazon SageMaker provides fast and easy methods for training large models and deep learning datasets. SageMaker’s distributed training libraries train large datasets faster.

Create an AWS account and get started with Amazon SageMaker today.

A story about learning burnout / Sudo Null IT News

I decided to write this article as an attempt to streamline the movement of thoughts in my head. As an opportunity to share with those who want to become a member that thousands of words about the need to actively study and endure hardships and everything will definitely work out do not always reflect reality.

Now I am in an active stage of burnout from the path I have traveled, and this has turned me into a wheel of self-flagellation and hatred. I’m trying to get out.

Sunny 2020

My colleague quits his job as a laboratory assistant and goes to work as a java programmer. This was the first, but not yet defining bell. Even then, after 7 years as a marine engineer and being highly qualified, I understood that I was reaching the ceiling. I did not want to do business, but the ability to learn was in abundance. Behind him was 6 years of university and 4 years of graduate school. The first impulse to learn the language ended quickly, the sisharp was given crumpled. Without seeing the general concept of the language, and also without a clue why it was needed at all, educational queries quickly disappeared from the search history.

Already less rosy 2021

Promises and assurances about the need for IT sector masters are attacked everywhere, both from friends who almost shook with questions about familiar programmers, and notorious programming schools. At that time, the desire for adventure again zagozilo and began to warm up. A former colleague also added fuel to this fire, which could both work filigree and spend time with a child. Not wanting to vegetate on the territory of the enterprise, without the opportunity to see even future, but already beloved children, as well as the desire to grow in skills and salary, I decided to rethink the beginning of my career in the IT field.

Taking a step back, I looked at the areas that divided the information technology sector. Exploring every area, I searched for the answer within myself. A recollection or an echo of annoyance for something that he himself used and that he himself would like to do better. In the process, I learned that my strongest memory was the sadness of statically loading pages in my 2003. When a fifth grader opens Internet Explorer and sees images, texts loading in fits and starts and no magic. The kind of magic he’d seen in sci-fi movies where, even though they were ads, the windows were animated and personalized. Where the transition from page to page was like a transition from one world to another. It was by comparing these two emotions that I decided to get deeper into web development.

The beginning was neat and inexpensive. Kirup Chinnathambi’s uncomplicated book “JavaScript From Scratch” showed me what the site consists of and did not sprinkle me with particularly complex terms. Having dealt with the book and written several pages, from which euphoria seemed to have no limit, I consolidated my desire to stay in this area. Unfortunately, there was no one familiar with knowledge in this area, and indeed no one who wanted to talk about the wrong side of the world of studying technology. I made this decision for myself: “I’ll take courses at some site and use them as a skeleton for my own teaching of what I want.” Great plan Walter.jpg. Taking the courses in one hand and the textbooks of Ilya Kantor in the other, I began to climb to my peak.

So, for half a year I methodically devoured block after block and chapter after chapter. A still formless and flimsy understanding was already beginning to emerge that the path would not be one thousand steps, or even two. With each term and abstraction I passed, my horizon of self-requirements grew like a shark’s smile. Here, on top of the regular knowledge of the holy trinity (JS, CSS, HTML), you understand that you need the concepts of algorithms, UI / UX design habits, and marketing skills in order to understand: “What exactly needs to be written and done on the page in order to realize that boy’s dream from 2003? Somehow trying to fit this knowledge and pathetic attempts to turn it into a qualification, I suddenly find out about some frameworks there. At this point, resentment was already rolling up. Like a tidal wave, she tickled the nerves, saying, “All the knowledge learned so far has been useless. You couldn’t raise them, but it turns out you didn’t have to deal with them when there was this amazing, like a child’s, fuse. Pulling towards Frontend development, the courses became more and more a burden. Or the material was already irrelevant for me personally. For example, algorithms that I studied a little earlier, albeit not at the level of the great and terrible. Or homework forced you to remember the syntax of the language that you don’t even plan to memorize. With all my gratitude to Java, I didn’t want to study it, but all the courses were held strictly on it. I understand why this language was the main one, I understand that it could be better this way. But going to the courses, I nevertheless reported on my interests in website building. Now everyone is shaved with the same brush for 10 months, leaving only a couple of weeks for specialization.

Beginning of 2022

So everything would have continued, and would have resulted in no one understands what, if not for a fatal accident. In early March, I suffered a serious leg injury (ACL rupture). This required an expensive operation and a lengthy rehabilitation process from me and my loved ones. In order not to go crazy at home, I decided to take the bull by the horns and sit down firmly at the framework with all the elements of containerization, styling, methods of working with databases and methods of organizing SPA that accompany it. So I eagerly absorbed lesson after lesson, my working day was 8 hours of continuous study, practice and skill polishing. In two weeks, I completely swallowed the ReactJS YouRa Allakhverdov course.

This was followed by an English-language course on patterns in working with the framework. At the same time, taking layouts from freelance sites without accepting the order itself, I began to typeset, so that the portfolio would be filled and my hand would be full. Feeling the power, I gave myself up to creativity in my personal business card website. Everything seemed clear and understandable. Working with states, functional and class components. It seemed that any interview was mine, and I would be able to embark on a new dizzying path without any difficulties. I matured so much that I went to the stock exchange and sorted out questions from interviews. It seemed that I was close to my goal.

After surgery

Already in April I spent 3 weeks preparing and post-op recovery. For a number of reasons, it turned out to be more difficult. I won’t dive deep into this step. I can only say that the key complication was the “Post-Puncture Syndrome”. Believe me, the head at this time does not belong to you at all.

So I was able to sit and work with my head again. “Well, now how will I continue my ascent, and in general I will become the best,” I thought naively, sitting down to my favorite thing. I forgot to say, I streamed my processes on Twitch. So there was no socialization, and they suggested what was best done where. And so I sat down to make a layout of an eco-products store, cheerfully throwing ideas on, I spent more than one hour with pleasure. The next day, inspired by the previous day, I started streaming again and found that I couldn’t even raise my fortune. For me, resentment and disappointment rushed, knowledge dissolved in just a couple of weeks without practice. Reading and viewing tutorials was useless. I, having apologized to the audience, ended the stream and realized that everything had to be started from the beginning.

Continuing what seemed to me useless training courses and reviewing what I had done, I gradually sank into burnout syndrome. Starting over, I found TypeScript to be a more strict but successful language. Which, of course, again hit me with frustration. “The previously studied again turned out to be only a step (if not a waste of time) to the desired heights and did not become that plateau of knowledge stability, improving which you can enter the labor market.” So I concluded, sitting down for courses from Ulbi TV on React and Typescript from Anton Larichev during a break reading a new work for me by Kirup Chinnathambi “Learning React”.

With each new chapter of information, layout, I realized that I was learning out of habit, but headache, apathy and indifference haunt me. At the same time, the quality of education was actively declining. Apparently desperate, I described my problem to ChatGPT and received the answer that was already in my head: “Burnout”. Having clarified with the program the difference between laziness and burnout, I decided to describe my experiences in this form of retelling.