90.3K
FUWhat is DuckDB? And why does it have such a wierd name?
#coding #developer #techtok #programming #petergriffin #brainrot #bigdata
@fullstackpeter
#Bigdata Engineer
Regardez 5.6K vidéos Reels sur Bigdata Engineer de personnes du monde entier.
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PREngineering Fields with Good Scope, Opportunities & high Salaries💰
1) AI/ML Engineer: 10L to 50L+
Skills Required - Python, TensorFlow, Data Science
AI-driven automation is at the highest demand in India
2) DATA SCIENTIST: 8L+ to 55L+
Skills Required - Python, Data Science
Deals with big data on daily basis for companies of every sector
3) Software Developer, Full Stack & DEVOPS: 8L to 55L+
Skills Required - JavaScript, Python, React, AWS
India is going Digital and at this new age, such skills will remain at the top of the charts
4) CyberSecurity Engineer - IT PROFESSIONAL: 9L to 35L+
Skills Required - Ethical Hacking, Python, Security Analysis, Penetration Testing
Every company ever created will always need a tech-security expert and knowing AI is just cherry on cake.
5) Blockchain Developer: 8L to 30L+
Skills Required - Solidity, Ethereum, Hyperledger, Cryptography
Web3 and smart contracts are rising trends.
6) Aerospace Engineer: 10L to 40L+
Skills Required - Aerodynamics, Rocket Propulsion, CFD, CAD
With privatization, India’s space industry is set for massive expansion, Agnikul & Skyroot is booming
7) Game Developer: 7L to 30L+
Skills Required - Unity, Unreal Engine, C++, 3D Modeling
AR/VR gaming is the new addiction
8) Quantum Computing: 5L to 60L+
Skills Required - ML, Programming, Quantum Error Correction, Quantum Mechanics
It uses quantum mechanics to perform calculations that are faster than classical computers
9) BioMedical Engineering: 6L to 20L+
Skills Required - Biotechnology, AI in Healthcare, Biomedical Device Design
Growing demand for bioengineering in India’s healthcare sector.
10) Energy Engineering: 8L to 18L+
Skills Required - Data Analysis, Maths, Project Management
It involves the study of energy resourcesand technologies to improve energy efficiency and sustainability
#engineering #engineeringlife #engineer #engineeringstudent #engineers #artificialintelligence #machinelearning #datascience #aerospaceengineering #fullstackdeveloper #devops
@programmatixinstitute
438.3K
COLogic building in python...
Useful for all profiles 👍
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#dataengineer
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@code_with_sohail
1.7M
DODo People Really Understand Big Data?
@agt @standuplivehunt @standuplive @zaniesnashville #standupcomedy #nerd #engineer
@donmcmillancomedy
35.0K
DALooking to level up your data engineering salary? Here’s 3 ways to do it….
#dataengineering #data #dataanalytics #dataanalysis #bigtech
@data_engineer_academy
937.1K
NAIf you need to learn DSA, try these
🏷️ data structures, algorithms, dsa, data science, big data, data engineering, ai, coding, ai for beginners, fyp, ai girl
@nataindata
1.9M
SEFind out how normal you are through facial ai
This website not only guesses how normal you are but it also teaches you how facial recognition is used throughout the world.
What scores did you get?
Enjoy!
@setupspawn
299.4K
_SMeet Coverage Responsibility, a new stat from @nextgenstats 🏈
With tracking chips in every player’s shoulder pads, the NFL can now finally quantify one of the toughest positions to measure, defensive backs.
And it all started as an idea in the NFL Big Data Bowl, the league’s annual analytics competition.
@amazonwebservices
@_sportsball
389.8K
AIIn 3 dimensions, linear regression can be represented using planes. Extending to even higher dimensions, linear regression would fit a n-dimensional hyperplane to our data.
To train our model or to fit the plane to our high dimensional data, we require calculus and linear algebra. We also need a metric to determine how good our plane is. This metric is called the loss function, and is typically the mean-squared error (MSE) or equivalents.
In the training process, we feed input data to the model, producing an output, then measuring the difference between the predicted and real outputs. We take this difference (loss) and use an optimization technique like gradient descent to tweak the parameters that make up the plane. This shifts the steepness and position of the plane.
By using the chain rule in calculus, we are able to update our parameters slowly and iteratively, shifting the line closer and closer to the data. We stop training when our model/plane has converged or does not change much from iteration to iteration.
Want to Learn ML/AI? Accelerate your learning with our Weekly AI Newsletter—educational, easy to understand, mathematically explained, and completely free (link in bio 🔗).
C: Algoneural
Join our AI community for more posts like this @aibutsimple 🤖
#machinelearning #artificialintelligence #ai #datascience #technology #python #programming #deeplearning #bigdata #coding #tech #computerscience #data #aiart #iot #digitalart #dataanalytics #innovation #software #datascientist #pythonprogramming #business #javascript #developer #analytics #java #programmer #cybersecurity #generativeart #webdevelopment
@aibutsimple
1.4M
ROA journey through "Tech Role Evolution" 🎶 (original song)
Tech roles have evolved, causing overlapping responsibilities and challenges in building effective teams. According to @roberthalf, 95% of tech leaders report challenges finding skilled professionals, putting key business priorities at risk. 🚀 For figuring out how to build your best tech team, check out @roberthalf!
#RobertHalf #ad #TechHistory #TechCareers #TechJobs #BigData #AI #DevOps #DataScience #MachineLearning #Engineering #Developer
@roberthalf
2.6M
SUVideo credits :- @3blue1brown
This reels gives an insight of how ChatGPT works behind the scenes.
GPT-3 (Generative Pre-trained Transformer 3) is a groundbreaking language model developed by OpenAI. Here’s a simplified overview of how it works:
**Architecture:**
GPT-3 is based on the transformer architecture, which consists of:
1. **Encoder:** A stack of identical layers, each comprising self-attention mechanisms and feed-forward networks (FFNs).
2. **Decoder:** Another stack of similar layers, but with an additional output layer to generate text.
**Pre-training:**
GPT-3 was pre-trained on a massive dataset (~45 GB) of text from the internet, including books, articles, and websites. This process involves:
1. **Masked language modeling:** Randomly masking some tokens in the input text and predicting the original token.
2. **Next sentence prediction:** Predicting the next sentence in a sequence given the previous sentences.
**Fine-tuning:**
GPT-3 can be fine-tuned for specific tasks, such as:
1. **Language translation:** Translating text from one language to another.
2. **Summarization:** Summarizing long pieces of text into shorter ones.
3. **Question answering:** Answering questions based on the input text.
**How it works:**
When you give GPT-3 a prompt or input text, it:
1. **Tokenizes** the input into subwords (smaller units of words).
2. **Encodes** the tokenized input using the encoder layers.
3. **Generates** output tokens based on the encoded input and decoder layers.
4. **Post-processes** the generated text to refine its quality.
GPT-3’s abilities are impressive, including:
* Generating coherent text that appears to have been written by a human.
* Understanding natural language and responding accordingly.
* Learning from vast amounts of data and adapting to new tasks.
Video credits :- @3blue1brown
#artificialintelligence #ai #machinelearning #technology #datascience #python #deeplearning #programming #tech #robotics #innovation #bigdata #coding #iot #computerscience #data #dataanalytics #business #engineering #robot #datascientist #art #software #automation #analytics #ml #pythonprogramming #programmer #digitaltransformation #developer
@susmit.eth
45.6M
PRMy Most Satisfying 3D Prints in 60 Seconds #3dprinting #fidgettoys #satisfying
@printsculptors
✨ Guide de Découverte #Bigdata Engineer
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