TFLearn
NumPy
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TFLearn | NumPy | |
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2 | 272 | |
9,606 | 26,290 | |
0.0% | 1.6% | |
0.0 | 10.0 | |
5 months ago | 5 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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TFLearn
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Beginner Friendly Resources to Master Artificial Intelligence and Machine Learning with Python (2022)
TFLearn – Deep learning library featuring a higher-level API for TensorFlow
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Base ball
Both the teams in a game are given their individual ID values and are made into vectors. Relevant data like the home and away team, home runs, RBI’s, and walk’s are all taken into account and passed through layers. There’s no need to reinvent the wheel here, there's a multitude of libraries that enable a coder to implement machine learning theories efficiently. In this case we will be using a library called TFlearn, documentation available from http://tflearn.org. The program will output the home and away teams as well as their respective score predictions.
NumPy
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Dot vs Matrix vs Element-wise multiplication in PyTorch
In NumPy with @, dot() or matmul():
- NumPy 2.0.0 Beta1
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Element-wise vs Matrix vs Dot multiplication
In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.
- JSON dans les projets data science : Trucs & Astuces
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JSON in data science projects: tips & tricks
Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:
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Introducing Flama for Robust Machine Learning APIs
numpy: A library for scientific computing in Python
- help with installing numpy, please
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A Comprehensive Guide to NumPy Arrays
Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.
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Why do all the popular projects use relative imports in __init__ files if PEP 8 recommends absolute?
I was looking at all the big projects like numpy, pytorch, flask, etc.
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NumPy 2.0 development status & announcements: major C-API and Python API cleanup
I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]
What are some alternatives?
Keras - Deep Learning for humans
SymPy - A computer algebra system written in pure Python
tensorflow - An Open Source Machine Learning Framework for Everyone
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
scikit-learn - scikit-learn: machine learning in Python
blaze - NumPy and Pandas interface to Big Data
skflow - Simplified interface for TensorFlow (mimicking Scikit Learn) for Deep Learning
SciPy - SciPy library main repository
NuPIC - Numenta Platform for Intelligent Computing is an implementation of Hierarchical Temporal Memory (HTM), a theory of intelligence based strictly on the neuroscience of the neocortex.
Numba - NumPy aware dynamic Python compiler using LLVM
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
Nim - Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).