✅✅ E̲x̲p̲e̲r̲i̲e̲n̲c̲e̲ ̲N̲o̲.̲ 1: -🚀 🚀
Topics in which you are good, make yourself best in those.
Topics in which you're average, make yourself good in those
Topics in which you're weak, make those average.
The important point is- you cannot ignore any topic mentioned in the syllabus.
✅✅ E̲x̲p̲e̲r̲i̲e̲n��c̲e̲ ̲N̲o̲.̲ 2 :– 🚀🚀
You'll have to practice practice practice questions/examples till the correct result isn't found. Don't ignore practice by thinking that ok these easy questions I can do. Believe me..No matter you're from which degree or Insitute, you'll get disaster in the exam hall.
✅✅ E̲x̲p̲e̲r̲i̲e̲n̲c̲e̲ ̲N̲o̲.̲ 3 : - 🚀🚀
Finish your Optional before the preliminary exam. Not only finished but also practiced well. Between pre and mains your 50–60% of time in the day will go for Mathematics Provided that you have finished Optional before the preliminary exam.
✅✅ E̲x̲p̲e̲r̲i̲e̲n̲c̲e̲ ̲N̲o̲.̲ 4 : - 🚀🚀
Align your GS preparation in a manner that you don't need much time to prepare this after the preliminary result.
✅✅ E̲x̲p̲e̲r̲i̲e̲n̲c̲e̲ ̲N̲o̲.̲ 5 : - 🚀🚀
Join the test series before preliminary and examine your preparation of Optional regularly. After prelim, take at least 4 full-length tests, to simulate yourself for mains.
✅✅ E̲x̲p̲e̲r̲i̲e̲n̲c̲e̲ ̲N̲o̲.̲ 6:- 🚀🚀
Don't worry much about interview that they'll ask very special questions from Mathematics. 99 percent possibility is that in your interview board, No Mathematics expert will be there. So focus on getting more n more marks in Mathematics Optional.
Feeling that Victoria Secret vibe while wearing Honey Birdette. 😝 Good morning! Another one of my favorite pictures from last weekend‘s photo shoot with @lifestyle_boudoir 🥰 #lingerie #lace #ErikaJordan #thursdaymorning #momboss #mompreneur #followme https://www.instagram.com/p/ClEXjgbvG1U/?igshid=NGJjMDIxMWI=
Presently, it is Kalyug Unrighteousness has increased in it. In Kalyug, the faith of human beings towards bhakti reduces. Either they do not do bhakti, or if they do, then abandoning the injunctions of scriptures do arbitrary bhakti, which is forbidden in Adhyay 16 Shlok 23-24.
As a result of which the benefit, which is desired from God, is not obtained. Therefore maximum people become atheist.
For More Information Visit @spiritualleadersaintrampaljim Maharaj
Happy Thursday beloved souls.Make love your daily bread.
Know it takes SelfControl to not allow the intricacies of life to push you off your life path and into wrong conduct.Embodying love helps you transcend the material plain to reclaim the warrior spirit of love that you are💞
Feeding your soul is about taking steps to feel at peace with yourself, wherever you are in your life at this moment in time, whether things are going well or you're in the midst of challenges. To align to your soul is easy, just Follow your bliss.
Know that Happiness is something you create by learning how to feed your soul. Step away from the stress and scarcity driven ego mindset and embrace Self-love as it facilitates self-knowledge which is a catalyst for Potent conscious growth.
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Here are eight common writing mistakes that you should avoid, or correct if you want your writing to stand out for the right reasons. Misspelled words, inconsistent spelling, and punctuation, wrong word usage, confusing contractions with possessive pronouns, weak qualifiers, sentences that go on and on, walls of text, and lack of focus. But with that said, remember to "Enjoy the Write!" KSCarson
Powerful AI tools, every beginner should know about
Certainly! There are several powerful AI tools and frameworks that beginners can explore to get started with artificial intelligence. Here are some widely used ones:
TensorFlow:
Developed by Google, TensorFlow is an open-source machine learning library. It provides a comprehensive ecosystem of tools, community resources, and a flexible platform for building and deploying ML models.
PyTorch:
An open-source deep learning library developed by Facebook, PyTorch is known for its dynamic computation graph. It's popular among researchers for its flexibility and ease of use.
Scikit-learn:
Scikit-learn is a simple and efficient tool for data analysis and machine learning. It provides simple and efficient tools for predictive data analysis and is built on NumPy, SciPy, and Matplotlib.
Keras:
Keras is an open-source neural network library written in Python. It is capable of running on top of TensorFlow, Theano, or Microsoft Cognitive Toolkit (CNTK). Keras is known for its user-friendly API.
Jupyter Notebooks:
Jupyter is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and narrative text. It's widely used for data exploration, visualization, and collaborative work.
OpenCV:
OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning software library. It's designed to help developers work with images and videos and includes many image processing functions.
Pandas:
Pandas is a fast, powerful, and easy-to-use open-source data manipulation and analysis library built on top of Python. It provides data structures for efficiently storing and manipulating large datasets.
NLTK (Natural Language Toolkit):
NLTK is a library for the Python programming language that provides tools for working with human language data. It is used for tasks such as classification, tokenization, stemming, tagging, parsing, and more.
Microsoft Azure ML Studio:
Azure Machine Learning Studio is a cloud-based integrated development environment for building, training, and deploying machine learning models. It offers drag-and-drop features for model development.
IBM Watson Studio:
Watson Studio, by IBM, is a comprehensive platform for AI and machine learning. It allows data scientists and developers to collaborate on data analysis and model development in a cloud-based environment.
Remember that as a beginner, it's essential to understand the fundamentals of machine learning and AI concepts before diving into specific tools. Additionally, exploring coding languages such as Python and understanding basic statistics and linear algebra can be beneficial for a solid foundation in AI.