Harvard has made its textbook on ML systems publicly available. It's extremely practical: not just about how to train models, but how to build production systems around them - what really matters.
The topics there are really top-notch:
> Building autograd, optimizers, attention, and mini-PyTorch from scratch to understand how the framework is structured internally. (This is really awesome)
> Basic things about DL: batches, computational accuracy, model architectures, and training
> Optimizing ML performance, hardware acceleration, benchmarking, and efficiency
So this isn't just an introductory course on ML, but a complete cycle from start to practical application. You can already read the book and view the code for free. For 2025, this is one of the strongest textbooks to have been released, so it's best not to miss out.
The repository is here, with a link to the book inside👏
👉 @codeprogrammer
The topics there are really top-notch:
> Building autograd, optimizers, attention, and mini-PyTorch from scratch to understand how the framework is structured internally. (This is really awesome)
> Basic things about DL: batches, computational accuracy, model architectures, and training
> Optimizing ML performance, hardware acceleration, benchmarking, and efficiency
So this isn't just an introductory course on ML, but a complete cycle from start to practical application. You can already read the book and view the code for free. For 2025, this is one of the strongest textbooks to have been released, so it's best not to miss out.
The repository is here, with a link to the book inside
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How to test code without a real database
It is much better to mock the call to
Example function:
Test with mock:
This way you test only the business logic — quickly, reliably, and without unnecessary dependencies
https://t.me/CodeProgrammer
During unit testing, connecting to a real DB is unnecessary:
• tests run slowly
• become unstable
• require a working server
It is much better to mock the call to
pandas.read_sql and return dummy dataExample function:
def query_user_data(user_id):
query = f"SELECT id, name FROM users WHERE id = {user_id}"
return pd.read_sql(query, "postgresql://localhost/mydb")
Test with mock:
from unittest.mock import patch
import pandas as pd
@patch("pandas.read_sql")
def test_database_query_mocked(mock_read_sql):
mock_read_sql.return_value = pd.DataFrame(
{"id": [123], "name": ["Alice"]}
)
result = query_user_data(user_id=123)
assert result["name"].iloc[0] == "Alice"
This way you test only the business logic — quickly, reliably, and without unnecessary dependencies
https://t.me/CodeProgrammer
❤5👍2🔥2
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