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Costom made Batik Messenger Bag
Malaysian Batik is batik textile art in Malaysia, especially on the east coast of Malaysia (Kelantan). The most popular motifs are leaves and flowers. The process is complicated due to all the pattern are hand stamp and hand painted on the fabric. The colour didn#39;t mix together as the stamp is dip into bee wax first to make a barrier between others colour before it dries under the sun twice to make sure colour wont fade away when wash.
**The measurement is at the 4th picture, it can fit note book,ipad 6th generation, phone, pen, wallet and can hook a bunch of key beside the bag.
**The strap been reinforce with double layer and inside also been added another layer.
Nearly every scientist working in Python draws on the power of NumPy.
NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant.
|Quantum Computing||Statistical Computing||Signal Processing||Image Processing||Graphs and Networks||Astronomy Processes||Cognitive Psychology|
|QuTiP||Pandas||SciPy||Circle Tree Slice Watercolor Painting- Wood Slice Art Print- Tre||NetworkX||AstroPy||PsychoPy|
|Bioinformatics||Bayesian Inference||Mathematical Analysis||Chemistry||Geoscience||Geographic Processing||Architecture & Engineering|
|Scikit-Bio||handmade pocket knife, edc kiridashi, fixed blade knife, neck kn||SymPy||MDAnalysis||Simpeg||GeoPandas||City Energy Analyst|
|PyEnsembl||ArviZ||Black Boho Basket, Plant basket, Fabric planter, Storage basket,||RDKit||ObsPy||Folium||Sverchok|
|ETE||emcee||FEniCS||Fatiando a Terra|
NumPy's API is the starting point when libraries are written to exploit innovative hardware, create specialized array types, or add capabilities beyond what NumPy provides.
|Array Library||Capabilities & Application areas|
|Dask||Distributed arrays and advanced parallelism for analytics, enabling performance at scale.|
|CuPy||NumPy-compatible array library for GPU-accelerated computing with Python.|
|JAX||Composable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU.|
|Xarray||Labeled, indexed multi-dimensional arrays for advanced analytics and visualization|
|Sparse||NumPy-compatible sparse array library that integrates with Dask and SciPy's sparse linear algebra.|
|PyTorch||Deep learning framework that accelerates the path from research prototyping to production deployment.|
|TensorFlow||An end-to-end platform for machine learning to easily build and deploy ML powered applications.|
|MXNet||Deep learning framework suited for flexible research prototyping and production.|
|Search Engine Optimization | On Page Optimization | Technical Op||A cross-language development platform for columnar in-memory data and analytics.|
|xtensor||Multi-dimensional arrays with broadcasting and lazy computing for numerical analysis.|
|XND||Develop libraries for array computing, recreating NumPy's foundational concepts.|
|uarray||Python backend system that decouples API from implementation; unumpy provides a NumPy API.|
|tensorly||Tensor learning, algebra and backends to seamlessly use NumPy, MXNet, PyTorch, TensorFlow or CuPy.|
NumPy lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:
NumPy forms the basis of powerful machine learning libraries like scikit-learn and SciPy. As machine learning grows, so does the list of libraries built on NumPy. Rossini Pendant and brooch 925 sterling silver, silk lanyard, av deep learning capabilities have broad applications — among them speech and image recognition, text-based applications, time-series analysis, and video detection. 10, 20, or 50 Santa Hat Charms, Silver, Christmas Charms Pendant, another deep learning library, is popular among researchers in computer vision and natural language processing. MXNet is another AI package, providing blueprints and templates for deep learning.
NumPy’s accelerated processing of large arrays allows researchers to visualize datasets far larger than native Python could handle.