einsum.com

What It Is

Based on the extracted main content, the current page on einsum.com is an English illustrated tutorial titled “The Joy of einsum.” Its core goal is to help readers understand NumPy’s np.einsum, which uses Einstein summation notation to express matrix and tensor operations through strings. It feels more like a high-quality technical tutorial or quick reference than a full online course.

Core Content and Format

The content is organized around several typical scenarios: matrix multiplication, batched matrix multiplication, outer products, matrix trace, bilinear forms, attention scores in Transformers, and high-dimensional tensor contractions. Each scenario first shows the conventional NumPy approach, such as dot, matmul, tensordot, transpose, and sum, then presents the corresponding einsum version, emphasizing the idea that “the subscript string is the mathematical formula.”

The page also provides a Rosetta Stone-style cheat sheet covering common operations such as vector dot products, outer products, transposition, summation, diagonals, batched outer products, and the Frobenius norm. It then summarizes three rules: repeated indices are summed over, output indices determine the shape, and free indices are used for broadcasting. Finally, it adds practical advice on using optimize=True, applying einsum across NumPy/PyTorch/JAX/TensorFlow, and when not to use einsum.

Pricing and Certificates

The main text does not mention pricing, subscriptions, paywalls, certificates, or accreditation, so the page appears to be freely readable. It also does not show information about instructors, institutional background, study duration, assignments, or completion certificates.

Pros and Cons

Its strengths are clear structure and practical examples, making it especially suitable for learners who are easily confused by tensor dimensions and contractions. By comparing traditional implementations with einsum notation, readers can quickly appreciate its readability advantages. The drawbacks are also clear: it covers only the single topic of einsum and does not constitute a systematic course; there are no interactive exercises, video explanations, Q&A support, or information about a Chinese version. For readers with no foundation in linear algebra or NumPy, there may still be a learning curve.

Who It’s For and Access from China

It is suitable for Python/NumPy users, machine learning and deep learning learners, and engineers or researchers who need to understand multidimensional array operations. It is especially useful as a quick reference for attention, batch matmul, and tensor contraction. The main text does not provide information about access from mainland China, so it is not possible to confirm whether it can be accessed directly; this is marked as unknown.