/
Courses
NumPy Course: from the first array to the neural network
NumPy Course: from the first array to the neural network, in three levels, with integrative projects and an executable file per chapter.
StartIntroduction
Environment
Basic
- 5Creating arrays5 sections
- 6Anatomy of an array2 sections
- 7Data types: dtype5 sections
- 8Indexing and slicing5 sections
- 9Boolean and fancy indexing4 sections
- 10Copies and views5 sections
- 11Reshape and transpose4 sections
- 12Vectorization5 sections
- 13Broadcasting4 sections
- 14Aggregations and the axis parameter6 sections
- 15Universal functions (ufuncs)4 sections
- 16Random numbers with Generator4 sections
- 17Tensors: from scalar to batch of images4 sections
- 18Measure real performance4 sections
Intermediate
- 19Join, stack and split5 sections
- 20Sort, search and count5 sections
- 21Vectorized conditions: where, select and clip4 sections
- 22Statistics: percentiles, histograms and correlation5 sections
- 23Linear algebra5 sections
- 24Read and write arrays3 sections
- 25Missing values: NaN and masked arrays5 sections
- 26Structured types and dates4 sections
- 27Advanced indexing and grids5 sections
- 28Sliding windows and series4 sections
Advanced
- 29Memory from the inside: strides, contiguity, and order5 sections
- 30einsum and tensordot4 sections
- 31Vectorizing difficult problems5 sections
- 32Performance: temporaries, in-place and dtypes4 sections
- 33Files larger than memory: memmap5 sections
- 34Ufuncs from the inside4 sections
- 35Advanced randomness5 sections
- 36Numerical tests and stability6 sections
- 37Typing, API and NumPy 24 sections