π₯ 7 Small but Powerful Language Models You Should Know
β‘ google/gemma-3-270M-it
Ultra-light (270M params) βοΈ Runs on low resources, 32K context. Great for Q&A, summarization & reasoning.
π Qwen/Qwen3-0.6B
Efficient 600M model π§ Switches between βthinkingβ (reasoning, coding) & βfastβ chat. Supports 100+ languages.
π‘ HuggingFaceTB/SmolLM3-3B
Open 3B model π Strong in math, coding, multilingual tasks + tool calling. Transparent training & open weights.
π Qwen/Qwen3-4B-Instruct-2507
Instruction-tuned 4B β‘ Optimized for fast, accurate responses (non-thinking mode). Excels in logic, coding & creative tasks.
πΌοΈ google/gemma-3-4b-it
Multimodal 4B ποΈ Handles text + images with 128K context. Great for QA, summarization & fine-tuning.
π€ janhq/Jan-v1-4B
Agentic reasoning model π Built for the Jan app. Tool use + strong reasoning, 91% accuracy on SimpleQA.
π microsoft/Phi-4-mini-instruct
Compact 3.8B π Trained on high-quality data. Excels at math, logic & multilingual. Supports function calling + 128K context.
β‘ google/gemma-3-270M-it
Ultra-light (270M params) βοΈ Runs on low resources, 32K context. Great for Q&A, summarization & reasoning.
π Qwen/Qwen3-0.6B
Efficient 600M model π§ Switches between βthinkingβ (reasoning, coding) & βfastβ chat. Supports 100+ languages.
π‘ HuggingFaceTB/SmolLM3-3B
Open 3B model π Strong in math, coding, multilingual tasks + tool calling. Transparent training & open weights.
π Qwen/Qwen3-4B-Instruct-2507
Instruction-tuned 4B β‘ Optimized for fast, accurate responses (non-thinking mode). Excels in logic, coding & creative tasks.
πΌοΈ google/gemma-3-4b-it
Multimodal 4B ποΈ Handles text + images with 128K context. Great for QA, summarization & fine-tuning.
π€ janhq/Jan-v1-4B
Agentic reasoning model π Built for the Jan app. Tool use + strong reasoning, 91% accuracy on SimpleQA.
π microsoft/Phi-4-mini-instruct
Compact 3.8B π Trained on high-quality data. Excels at math, logic & multilingual. Supports function calling + 128K context.
β€6π₯2
Hey Guysπ,
The Average Salary Of a Data Scientist is 14LPA
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The Average Salary Of a Data Scientist is 14LPA
ππππ¨π¦π π πππ«ππ’ππ’ππ ππππ πππ’ππ§ππ’π¬π ππ§ ππ¨π© ππππ¬π
We help you master the required skills.
Learn by doing, build Industry level projects
π©βπ 1500+ Students Placed
πΌ 7.2 LPA Avg. Package
π° 41 LPA Highest Package
π€ 450+ Hiring Partners
Apply for FREEπ :
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( Limited Slots )
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An Artificial Neuron Network (ANN), popularly known as Neural Network is a computational model based on the structure and functions of biological neural networks. It is like an artificial human nervous system for receiving, processing, and transmitting information in terms of Computer Science.
Basically, there are 3 different layers in a neural network :
Input Layer (All the inputs are fed in the model through this layer)
Hidden Layers (There can be more than one hidden layers which are used for processing the inputs received from the input layers)
Output Layer (The data after processing is made available at the output layer)
Graph data can be used with a lot of learning tasks contain a lot rich relation data among elements. For example, modeling physics system, predicting protein interface, and classifying diseases require that a model learns from graph inputs. Graph reasoning models can also be used for learning from non-structural data like texts and images and reasoning on extracted structures.
Basically, there are 3 different layers in a neural network :
Input Layer (All the inputs are fed in the model through this layer)
Hidden Layers (There can be more than one hidden layers which are used for processing the inputs received from the input layers)
Output Layer (The data after processing is made available at the output layer)
Graph data can be used with a lot of learning tasks contain a lot rich relation data among elements. For example, modeling physics system, predicting protein interface, and classifying diseases require that a model learns from graph inputs. Graph reasoning models can also be used for learning from non-structural data like texts and images and reasoning on extracted structures.
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Here are some essential data science concepts from A to Z:
A - Algorithm: A set of rules or instructions used to solve a problem or perform a task in data science.
B - Big Data: Large and complex datasets that cannot be easily processed using traditional data processing applications.
C - Clustering: A technique used to group similar data points together based on certain characteristics.
D - Data Cleaning: The process of identifying and correcting errors or inconsistencies in a dataset.
E - Exploratory Data Analysis (EDA): The process of analyzing and visualizing data to understand its underlying patterns and relationships.
F - Feature Engineering: The process of creating new features or variables from existing data to improve model performance.
G - Gradient Descent: An optimization algorithm used to minimize the error of a model by adjusting its parameters.
H - Hypothesis Testing: A statistical technique used to test the validity of a hypothesis or claim based on sample data.
I - Imputation: The process of filling in missing values in a dataset using statistical methods.
J - Joint Probability: The probability of two or more events occurring together.
K - K-Means Clustering: A popular clustering algorithm that partitions data into K clusters based on similarity.
L - Linear Regression: A statistical method used to model the relationship between a dependent variable and one or more independent variables.
M - Machine Learning: A subset of artificial intelligence that uses algorithms to learn patterns and make predictions from data.
N - Normal Distribution: A symmetrical bell-shaped distribution that is commonly used in statistical analysis.
O - Outlier Detection: The process of identifying and removing data points that are significantly different from the rest of the dataset.
P - Precision and Recall: Evaluation metrics used to assess the performance of classification models.
Q - Quantitative Analysis: The process of analyzing numerical data to draw conclusions and make decisions.
R - Random Forest: An ensemble learning algorithm that builds multiple decision trees to improve prediction accuracy.
S - Support Vector Machine (SVM): A supervised learning algorithm used for classification and regression tasks.
T - Time Series Analysis: A statistical technique used to analyze and forecast time-dependent data.
U - Unsupervised Learning: A type of machine learning where the model learns patterns and relationships in data without labeled outputs.
V - Validation Set: A subset of data used to evaluate the performance of a model during training.
W - Web Scraping: The process of extracting data from websites for analysis and visualization.
X - XGBoost: An optimized gradient boosting algorithm that is widely used in machine learning competitions.
Y - Yield Curve Analysis: The study of the relationship between interest rates and the maturity of fixed-income securities.
Z - Z-Score: A standardized score that represents the number of standard deviations a data point is from the mean.
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Like if you need similar content ππ
A - Algorithm: A set of rules or instructions used to solve a problem or perform a task in data science.
B - Big Data: Large and complex datasets that cannot be easily processed using traditional data processing applications.
C - Clustering: A technique used to group similar data points together based on certain characteristics.
D - Data Cleaning: The process of identifying and correcting errors or inconsistencies in a dataset.
E - Exploratory Data Analysis (EDA): The process of analyzing and visualizing data to understand its underlying patterns and relationships.
F - Feature Engineering: The process of creating new features or variables from existing data to improve model performance.
G - Gradient Descent: An optimization algorithm used to minimize the error of a model by adjusting its parameters.
H - Hypothesis Testing: A statistical technique used to test the validity of a hypothesis or claim based on sample data.
I - Imputation: The process of filling in missing values in a dataset using statistical methods.
J - Joint Probability: The probability of two or more events occurring together.
K - K-Means Clustering: A popular clustering algorithm that partitions data into K clusters based on similarity.
L - Linear Regression: A statistical method used to model the relationship between a dependent variable and one or more independent variables.
M - Machine Learning: A subset of artificial intelligence that uses algorithms to learn patterns and make predictions from data.
N - Normal Distribution: A symmetrical bell-shaped distribution that is commonly used in statistical analysis.
O - Outlier Detection: The process of identifying and removing data points that are significantly different from the rest of the dataset.
P - Precision and Recall: Evaluation metrics used to assess the performance of classification models.
Q - Quantitative Analysis: The process of analyzing numerical data to draw conclusions and make decisions.
R - Random Forest: An ensemble learning algorithm that builds multiple decision trees to improve prediction accuracy.
S - Support Vector Machine (SVM): A supervised learning algorithm used for classification and regression tasks.
T - Time Series Analysis: A statistical technique used to analyze and forecast time-dependent data.
U - Unsupervised Learning: A type of machine learning where the model learns patterns and relationships in data without labeled outputs.
V - Validation Set: A subset of data used to evaluate the performance of a model during training.
W - Web Scraping: The process of extracting data from websites for analysis and visualization.
X - XGBoost: An optimized gradient boosting algorithm that is widely used in machine learning competitions.
Y - Yield Curve Analysis: The study of the relationship between interest rates and the maturity of fixed-income securities.
Z - Z-Score: A standardized score that represents the number of standard deviations a data point is from the mean.
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Like if you need similar content ππ
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Top 10 Free AI Playgrounds For You to Try
Curious about the future of AI? AI playgrounds are interactive platforms where you can experiment with AI models to create text, code, art, and more. They provide hands-on experience with pre-trained models and visual tools, making it easy to explore AI concepts without complex setup.
1. Hugging Face Space
2. Google AI Test Kitchen
3. OpenAI Playground
4. Replit
5. Cohere
6. AI21 Labs
7. RunwayML
8. PyTorch Playground
9. TensorFlow Playground
10. Google Colaboratory
React β₯οΈ for more
Curious about the future of AI? AI playgrounds are interactive platforms where you can experiment with AI models to create text, code, art, and more. They provide hands-on experience with pre-trained models and visual tools, making it easy to explore AI concepts without complex setup.
1. Hugging Face Space
2. Google AI Test Kitchen
3. OpenAI Playground
4. Replit
5. Cohere
6. AI21 Labs
7. RunwayML
8. PyTorch Playground
9. TensorFlow Playground
10. Google Colaboratory
React β₯οΈ for more
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