๐ How to Get Hired in the AI Era
๐ Category: CAREER ADVICE
๐ Date: 2026-05-01 | โฑ๏ธ Read time: 7 min read
What people actually look for when hiring juniors that stand out.
#DataScience #AI #Python
๐ Category: CAREER ADVICE
๐ Date: 2026-05-01 | โฑ๏ธ Read time: 7 min read
What people actually look for when hiring juniors that stand out.
#DataScience #AI #Python
๐ Churn Without Fragmentation: How a Party-Label Bug Reversed My Headline Finding
๐ Category: DATA SCIENCE
๐ Date: 2026-05-01 | โฑ๏ธ Read time: 11 min read
A data quality case study from English local elections on categorical normalisation, metric validation, andโฆ
#DataScience #AI #Python
๐ Category: DATA SCIENCE
๐ Date: 2026-05-01 | โฑ๏ธ Read time: 11 min read
A data quality case study from English local elections on categorical normalisation, metric validation, andโฆ
#DataScience #AI #Python
๐ Ghost: A Database for Our Times?
๐ Category: AGENTIC AI
๐ Date: 2026-05-01 | โฑ๏ธ Read time: 12 min read
The first database built for AI Agents
#DataScience #AI #Python
๐ Category: AGENTIC AI
๐ Date: 2026-05-01 | โฑ๏ธ Read time: 12 min read
The first database built for AI Agents
#DataScience #AI #Python
โค1
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Softmax vs Sigmoid โ๏ธ Interact ๐ https://byhand.ai/Khlg9b
= Softmax = ๐งฎ
Softmax is how deep networks turn raw scores into a probability distribution โ the final layer of every classifier ๐ฏ, and the core of every attention head in a transformer ๐ค. To see what it does, picture five boba tea shops ๐ง on the same block, all competing for your dollar ๐ฐ. Five candidates: a, b, c, d, e โ different chains, different brewing styles, different pearls. A boba reviewer hands you a ๐ค๐ฉ๐ฆ๐ช๐จ๐ฉ๐ฆ๐ด๐ต ๐ค๐ฐ๐ณ๐ฆ for each โ higher means perfectly chewy "QQ" pearls with the right bite ๐ก (ask a Taiwanese friend to find out what QQ means). Negative scores are real: mushy bobas, overcooked pearls, a batch left sitting too long ๐ฅ.
How do you turn five chewiness scores into an allocation that adds to a whole dollar? You could spend everything at the chewiest shop, but that ignores how good the runners-up are ๐โโ๏ธ. Softmax is the smooth alternative ๐.
Read the diagram left to right โก๏ธ. First, raise each score to e^{x} โ this does two things: it turns negative chewiness into small positives, and it stretches the gaps between scores exponentially ๐. Then sum all five into a single total Z. Finally, divide each e^{x} by Z to get a probability. The five probabilities add up to one, so you can read them as percentages of your dollar ๐. The chewiest shop gets the biggest slice ๐ฐ โ but never the whole dollar. That's the point of softmax: it ranks confidently while still leaving room for the others ๐ค.
= Sigmoid = ๐
Sigmoid squashes any real number into a probability between 0 and 1 โ the classic activation for binary classification โ , and still the gating function inside LSTMs and GRUs. Same boba block as the previous Softmax example, narrowed to just two contenders โ a hot new shop
Sigmoid is just softmax with two players, one of them pinned to zero โ๏ธ.
Read the diagram left to right โก๏ธ. First, raise each score to e^{x} โ for the usual shop
https://t.iss.one/DataScienceM๐
= Softmax = ๐งฎ
Softmax is how deep networks turn raw scores into a probability distribution โ the final layer of every classifier ๐ฏ, and the core of every attention head in a transformer ๐ค. To see what it does, picture five boba tea shops ๐ง on the same block, all competing for your dollar ๐ฐ. Five candidates: a, b, c, d, e โ different chains, different brewing styles, different pearls. A boba reviewer hands you a ๐ค๐ฉ๐ฆ๐ช๐จ๐ฉ๐ฆ๐ด๐ต ๐ค๐ฐ๐ณ๐ฆ for each โ higher means perfectly chewy "QQ" pearls with the right bite ๐ก (ask a Taiwanese friend to find out what QQ means). Negative scores are real: mushy bobas, overcooked pearls, a batch left sitting too long ๐ฅ.
How do you turn five chewiness scores into an allocation that adds to a whole dollar? You could spend everything at the chewiest shop, but that ignores how good the runners-up are ๐โโ๏ธ. Softmax is the smooth alternative ๐.
Read the diagram left to right โก๏ธ. First, raise each score to e^{x} โ this does two things: it turns negative chewiness into small positives, and it stretches the gaps between scores exponentially ๐. Then sum all five into a single total Z. Finally, divide each e^{x} by Z to get a probability. The five probabilities add up to one, so you can read them as percentages of your dollar ๐. The chewiest shop gets the biggest slice ๐ฐ โ but never the whole dollar. That's the point of softmax: it ranks confidently while still leaving room for the others ๐ค.
= Sigmoid = ๐
Sigmoid squashes any real number into a probability between 0 and 1 โ the classic activation for binary classification โ , and still the gating function inside LSTMs and GRUs. Same boba block as the previous Softmax example, narrowed to just two contenders โ a hot new shop
a with chewiness score x, and your usual go-to b whose score is pinned at zero (the neutral baseline you've come to expect) ๐.Sigmoid is just softmax with two players, one of them pinned to zero โ๏ธ.
Read the diagram left to right โก๏ธ. First, raise each score to e^{x} โ for the usual shop
b whose score is zero, this is just e^0 = 1 (the constant baseline) ๐. Then sum the two into a total Z. Finally, divide each e^{x} by Z to get a probability. The two probabilities add up to one โ the new shop wins more of your dollar when its pearls get chewier, and your usual keeps the rest ๐ธ. That's the point of sigmoid: it turns a single chewiness score into a clean 0-to-1 chance you'll try the new place over your usual ๐.https://t.iss.one/DataScienceM
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๐ค What is a perceptron, and how does it work?
Donโt worry, we have an easy-to-understand explanation for you!
Letโs dive in.๐๐ฝ
1๏ธโฃ History
The idea of a perceptron was first presented by Frank Rosenblatt in 1957. It was inspired on the neuron model by McCulloch and Pitt. The concept of the perceptron still forms the basis for modern artificial neural networks today.
2๏ธโฃ Concept of a Single-Layer Perceptron
A perceptron consists of an artificial neuron with adjustable weights and a threshold. The neuron in the perceptron is called a Linear Threshold Unit (LTU) because it uses the step function as its output function and performs a linear separation of the input data.
3๏ธโฃ Detailed view
The figure illustrates a perceptron with an input layer, an artificial neuron, and an output layer. The input layer contains the input value and x_0 as bias. In a neural network, a bias is required to shift the activation function either to the positive or negative side.
The perceptron has weights on its edges. It calculates the weighted sum of input values and weights. It is also known as aggregation. The result a finally serves as input into the activation function. The step function is used as the activation function. Here, all values of a > 0 map to 1, and values a < 0 map to -1.
4๏ธโฃ Limitations
The single-layer Perceptron can only solve linearly separable problems and struggles with complex patterns. The XOR problem, a simple nonlinear classification problem, showed the limitations of the perceptron.
5๏ธโฃ Advancements
The introduction of the multilayer perceptron (MLP) and the backpropagation algorithm led to the ability to solve nonlinear problems.
https://t.iss.one/DataScienceM๐ง
Donโt worry, we have an easy-to-understand explanation for you!
Letโs dive in.๐๐ฝ
1๏ธโฃ History
The idea of a perceptron was first presented by Frank Rosenblatt in 1957. It was inspired on the neuron model by McCulloch and Pitt. The concept of the perceptron still forms the basis for modern artificial neural networks today.
2๏ธโฃ Concept of a Single-Layer Perceptron
A perceptron consists of an artificial neuron with adjustable weights and a threshold. The neuron in the perceptron is called a Linear Threshold Unit (LTU) because it uses the step function as its output function and performs a linear separation of the input data.
3๏ธโฃ Detailed view
The figure illustrates a perceptron with an input layer, an artificial neuron, and an output layer. The input layer contains the input value and x_0 as bias. In a neural network, a bias is required to shift the activation function either to the positive or negative side.
The perceptron has weights on its edges. It calculates the weighted sum of input values and weights. It is also known as aggregation. The result a finally serves as input into the activation function. The step function is used as the activation function. Here, all values of a > 0 map to 1, and values a < 0 map to -1.
4๏ธโฃ Limitations
The single-layer Perceptron can only solve linearly separable problems and struggles with complex patterns. The XOR problem, a simple nonlinear classification problem, showed the limitations of the perceptron.
5๏ธโฃ Advancements
The introduction of the multilayer perceptron (MLP) and the backpropagation algorithm led to the ability to solve nonlinear problems.
https://t.iss.one/DataScienceM
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AI content often feels a bit off even when itโs correct. AIToHuman rewrites it so your message sounds natural and human while keeping your ideas exactly the same. Make your text better in seconds. Go try it โ https://aitohuman.com
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Forwarded from Machine Learning with Python
Hugging Face has literally gathered all the key "secrets". ๐ค
It's important to understand the evaluation of large language models.๐
While you're working with language models:
> training or retraining your models,๐
> selecting a model for a task, ๐ฏ
> or trying to understand the current state of the field,๐
the question almost inevitably arises:
how to understand that a model is good?โ
The answer is quality evaluation. It's everywhere:
> leaderboards with model ratings,๐
> benchmarks that supposedly measure reasoning,๐ง
> knowledge, coding or mathematics,๐จโ๐ป
> articles with claimed new best results.๐
But what is evaluation actually?๐คทโโ๏ธ
And what does it really show?๐
This guide helps to understand everything.๐
https://huggingface.co/spaces/OpenEvals/evaluation-guidebook#what-is-model-evaluation-about
What is model evaluation all about๐ค
Basic concepts of large language models for understanding evaluation ๐๏ธ
Evaluation through ready-made benchmarks ๐
Creating your own evaluation system๐ง
The main problem of evaluation โ ๏ธ
Evaluation of free text๐
Statistical correctness of evaluation๐
Cost and efficiency of evaluation๐ฐ
https://t.iss.one/CodeProgrammer๐ข
It's important to understand the evaluation of large language models.
While you're working with language models:
> training or retraining your models,
> selecting a model for a task, ๐ฏ
> or trying to understand the current state of the field,
the question almost inevitably arises:
how to understand that a model is good?
The answer is quality evaluation. It's everywhere:
> leaderboards with model ratings,
> benchmarks that supposedly measure reasoning,
> knowledge, coding or mathematics,
> articles with claimed new best results.
But what is evaluation actually?
And what does it really show?
This guide helps to understand everything.
https://huggingface.co/spaces/OpenEvals/evaluation-guidebook#what-is-model-evaluation-about
What is model evaluation all about
Basic concepts of large language models for understanding evaluation ๐๏ธ
Evaluation through ready-made benchmarks ๐
Creating your own evaluation system
The main problem of evaluation โ ๏ธ
Evaluation of free text
Statistical correctness of evaluation
Cost and efficiency of evaluation
https://t.iss.one/CodeProgrammer
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๐ ๐๐๐ฒ๐จ๐ง๐ ๐ญ๐ก๐ ๐๐ซ๐๐๐ข๐๐ง๐ญ: ๐๐ก๐ ๐๐๐ญ๐ก๐๐ฆ๐๐ญ๐ข๐๐ฌ ๐๐๐ก๐ข๐ง๐ ๐๐จ๐ฌ๐ฌ ๐
๐ฎ๐ง๐๐ญ๐ข๐จ๐ง๐ฌ
ML engineers often treat loss functions as โset-and-forgetโ hyperparameters. But the loss is not just a training detail; it is the mathematical statement of what the model is supposed to care about.
โก๏ธ In ๐ซ๐๐ ๐ซ๐๐ฌ๐ฌ๐ข๐จ๐ง, ๐๐๐ pushes the model to reduce large errors aggressively, which makes it sensitive to outliers, while ๐๐๐ treats all errors more evenly and is often more robust.
โณ ๐๐ฎ๐๐๐ซ ๐ฅ๐จ๐ฌ๐ฌ sits between the two, using squared error for small deviations and absolute error for larger ones.
โณ ๐๐ฎ๐๐ง๐ญ๐ข๐ฅ๐ ๐ฅ๐จ๐ฌ๐ฌ becomes useful when the goal is not a single prediction, but an interval or asymmetric risk, and ๐๐จ๐ข๐ฌ๐ฌ๐จ๐ง ๐ฅ๐จ๐ฌ๐ฌ fits naturally when the target is a count or rate.
โก๏ธ In ๐๐ฅ๐๐ฌ๐ฌ๐ข๐๐ข๐๐๐ญ๐ข๐จ๐ง, ๐๐ซ๐จ๐ฌ๐ฌ-๐๐ง๐ญ๐ซ๐จ๐ฉ๐ฒ remains the core objective because it trains the model to produce good probabilities, not just correct labels.
โณ ๐๐ข๐ง๐๐ซ๐ฒ ๐๐ซ๐จ๐ฌ๐ฌ-๐๐ง๐ญ๐ซ๐จ๐ฉ๐ฒ is the natural choice for two-class or multi-label settings, while ๐๐๐ญ๐๐ ๐จ๐ซ๐ข๐๐๐ฅ ๐๐ซ๐จ๐ฌ๐ฌ-๐๐ง๐ญ๐ซ๐จ๐ฉ๐ฒ extends that idea to multi-class softmax outputs.
โณ ๐๐ ๐๐ข๐ฏ๐๐ซ๐ ๐๐ง๐๐ is especially important when the task involves matching distributions, such as distillation, variational inference, or probabilistic modeling.
โณ ๐๐ข๐ง๐ ๐ ๐ฅ๐จ๐ฌ๐ฌ and squared hinge loss reflect the margin-based logic behind SVM-style learning, and focal loss is particularly valuable when easy examples dominate and the hard cases need more attention.
โก๏ธ In ๐ฌ๐ฉ๐๐๐ข๐๐ฅ๐ข๐ณ๐๐ ๐ญ๐๐ฌ๐ค๐ฌ, the choice of loss becomes even more meaningful.
โณ ๐๐ข๐๐ ๐ฅ๐จ๐ฌ๐ฌ works well in segmentation because it focuses on overlap and helps with class imbalance.
โณ ๐๐๐ ๐ฅ๐จ๐ฌ๐ฌ drives the generatorโdiscriminator game in adversarial learning.
โณ ๐๐ซ๐ข๐ฉ๐ฅ๐๐ญ ๐ฅ๐จ๐ฌ๐ฌ and contrastive loss shape embedding spaces so that similarity is learned directly.
โณ ๐๐๐ ๐ฅ๐จ๐ฌ๐ฌ solves alignment problems in sequence tasks like speech recognition and OCR, where labels are unsegmented.
โณ ๐๐จ๐ฌ๐ข๐ง๐ ๐ฉ๐ซ๐จ๐ฑ๐ข๐ฆ๐ข๐ญ๐ฒ is useful when vector direction matters more than magnitude.
๐ก ๐ป๐๐ ๐๐๐๐๐๐ ๐๐๐๐๐๐๐๐: ๐โ๐ ๐๐๐ ๐ ๐๐ข๐๐๐ก๐๐๐ ๐๐๐๐๐๐๐ ๐ฆ๐๐ข๐ ๐๐ ๐ ๐ข๐๐๐ก๐๐๐๐ ๐๐๐๐ข๐ก ๐กโ๐ ๐๐๐๐๐๐๐. ๐ผ๐ก ๐๐๐๐๐๐ก๐ ๐๐๐๐ฃ๐๐๐๐๐๐๐, ๐ ๐ก๐๐๐๐๐๐ก๐ฆ, ๐๐๐๐๐๐๐๐ก๐๐๐, ๐๐๐๐ข๐ ๐ก๐๐๐ ๐ , ๐๐๐ ๐๐๐๐๐๐๐๐๐ง๐๐ก๐๐๐; ๐ ๐๐๐๐ก๐๐๐๐ ๐๐ข๐ ๐ก ๐๐ ๐๐ข๐โ ๐๐ ๐กโ๐ ๐๐๐โ๐๐ก๐๐๐ก๐ข๐๐ ๐๐ก๐ ๐๐๐.
โ ๐๐ ๐กโ๐ ๐๐๐๐ ๐๐ข๐๐ ๐ก๐๐๐ ๐๐ ๐๐๐ก ๐๐๐๐ฆ โ๐โ๐๐โ ๐๐๐๐๐ ๐ โ๐๐ข๐๐ ๐ผ ๐ข๐ ๐?โ
โ ๐ผ๐ก ๐๐ ๐๐๐ ๐: โ๐โ๐๐ก ๐๐โ๐๐ฃ๐๐๐ ๐๐ ๐กโ๐๐ ๐๐๐ ๐ ๐๐๐๐๐ข๐๐๐๐๐๐?โ
https://t.iss.one/MachineLearning9
ML engineers often treat loss functions as โset-and-forgetโ hyperparameters. But the loss is not just a training detail; it is the mathematical statement of what the model is supposed to care about.
โก๏ธ In ๐ซ๐๐ ๐ซ๐๐ฌ๐ฌ๐ข๐จ๐ง, ๐๐๐ pushes the model to reduce large errors aggressively, which makes it sensitive to outliers, while ๐๐๐ treats all errors more evenly and is often more robust.
โณ ๐๐ฎ๐๐๐ซ ๐ฅ๐จ๐ฌ๐ฌ sits between the two, using squared error for small deviations and absolute error for larger ones.
โณ ๐๐ฎ๐๐ง๐ญ๐ข๐ฅ๐ ๐ฅ๐จ๐ฌ๐ฌ becomes useful when the goal is not a single prediction, but an interval or asymmetric risk, and ๐๐จ๐ข๐ฌ๐ฌ๐จ๐ง ๐ฅ๐จ๐ฌ๐ฌ fits naturally when the target is a count or rate.
โก๏ธ In ๐๐ฅ๐๐ฌ๐ฌ๐ข๐๐ข๐๐๐ญ๐ข๐จ๐ง, ๐๐ซ๐จ๐ฌ๐ฌ-๐๐ง๐ญ๐ซ๐จ๐ฉ๐ฒ remains the core objective because it trains the model to produce good probabilities, not just correct labels.
โณ ๐๐ข๐ง๐๐ซ๐ฒ ๐๐ซ๐จ๐ฌ๐ฌ-๐๐ง๐ญ๐ซ๐จ๐ฉ๐ฒ is the natural choice for two-class or multi-label settings, while ๐๐๐ญ๐๐ ๐จ๐ซ๐ข๐๐๐ฅ ๐๐ซ๐จ๐ฌ๐ฌ-๐๐ง๐ญ๐ซ๐จ๐ฉ๐ฒ extends that idea to multi-class softmax outputs.
โณ ๐๐ ๐๐ข๐ฏ๐๐ซ๐ ๐๐ง๐๐ is especially important when the task involves matching distributions, such as distillation, variational inference, or probabilistic modeling.
โณ ๐๐ข๐ง๐ ๐ ๐ฅ๐จ๐ฌ๐ฌ and squared hinge loss reflect the margin-based logic behind SVM-style learning, and focal loss is particularly valuable when easy examples dominate and the hard cases need more attention.
โก๏ธ In ๐ฌ๐ฉ๐๐๐ข๐๐ฅ๐ข๐ณ๐๐ ๐ญ๐๐ฌ๐ค๐ฌ, the choice of loss becomes even more meaningful.
โณ ๐๐ข๐๐ ๐ฅ๐จ๐ฌ๐ฌ works well in segmentation because it focuses on overlap and helps with class imbalance.
โณ ๐๐๐ ๐ฅ๐จ๐ฌ๐ฌ drives the generatorโdiscriminator game in adversarial learning.
โณ ๐๐ซ๐ข๐ฉ๐ฅ๐๐ญ ๐ฅ๐จ๐ฌ๐ฌ and contrastive loss shape embedding spaces so that similarity is learned directly.
โณ ๐๐๐ ๐ฅ๐จ๐ฌ๐ฌ solves alignment problems in sequence tasks like speech recognition and OCR, where labels are unsegmented.
โณ ๐๐จ๐ฌ๐ข๐ง๐ ๐ฉ๐ซ๐จ๐ฑ๐ข๐ฆ๐ข๐ญ๐ฒ is useful when vector direction matters more than magnitude.
๐ก ๐ป๐๐ ๐๐๐๐๐๐ ๐๐๐๐๐๐๐๐: ๐โ๐ ๐๐๐ ๐ ๐๐ข๐๐๐ก๐๐๐ ๐๐๐๐๐๐๐ ๐ฆ๐๐ข๐ ๐๐ ๐ ๐ข๐๐๐ก๐๐๐๐ ๐๐๐๐ข๐ก ๐กโ๐ ๐๐๐๐๐๐๐. ๐ผ๐ก ๐๐๐๐๐๐ก๐ ๐๐๐๐ฃ๐๐๐๐๐๐๐, ๐ ๐ก๐๐๐๐๐๐ก๐ฆ, ๐๐๐๐๐๐๐๐ก๐๐๐, ๐๐๐๐ข๐ ๐ก๐๐๐ ๐ , ๐๐๐ ๐๐๐๐๐๐๐๐๐ง๐๐ก๐๐๐; ๐ ๐๐๐๐ก๐๐๐๐ ๐๐ข๐ ๐ก ๐๐ ๐๐ข๐โ ๐๐ ๐กโ๐ ๐๐๐โ๐๐ก๐๐๐ก๐ข๐๐ ๐๐ก๐ ๐๐๐.
โ ๐๐ ๐กโ๐ ๐๐๐๐ ๐๐ข๐๐ ๐ก๐๐๐ ๐๐ ๐๐๐ก ๐๐๐๐ฆ โ๐โ๐๐โ ๐๐๐๐๐ ๐ โ๐๐ข๐๐ ๐ผ ๐ข๐ ๐?โ
โ ๐ผ๐ก ๐๐ ๐๐๐ ๐: โ๐โ๐๐ก ๐๐โ๐๐ฃ๐๐๐ ๐๐ ๐กโ๐๐ ๐๐๐ ๐ ๐๐๐๐๐ข๐๐๐๐๐๐?โ
https://t.iss.one/MachineLearning9
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They cover the entire spectrum: classic ML, LLM, and generative models โ with theory and practice.
tags: #python #ML #LLM #AI
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