If you’re looking for a comprehensive guide to artificial intelligence, you’ve come to the right place. The below list includes explanations and/or code snippets for important topics in AI, so you can quickly get up to speed on the essentials.
Neural Networks
Neural Networks Graphs
Machine Learning Overview
Machine Learning: Scikit-learn algorithm
This machine learning cheat sheet will assist you with the most challenging element of the job — locating the best estimator for the task. You can use the flowchart to examine each estimator’s documentation and preliminary instructions to learn more about the issues and how to address them.
Scikit-Learn
For the Python programming language, Scikit-learn (formerly scikits.learn) is a free machine learning library. Support vector machines, random forests, gradient boosting, k-means, and DBSCAN are just a few of the classification, regression, and clustering algorithms it offers. It is also built to work with Python’s NumPy and SciPy scientific and numerical libraries.
MACHINE LEARNING : ALGORITHM CHEAT SHEET
You may choose the best machine learning algorithms for your predictive analytics solution with this cheat sheet from Microsoft Azure. The cheat sheet will first inquire as to the nature of the data before recommending the most appropriate algorithm.
Python for Data Science
TensorFlow
Google announced the availability of the TPUs in Google Compute Engine in May 2017 as well as the release of the TPU’s second generation. The performance of the second-generation TPUs can reach 180 teraflops, and when they are grouped into clusters of 64 TPUs, they can reach 11.5 petaflops.
Keras
The Google TensorFlow team chose to include Keras support in TensorFlow’s core library in 2017. Keras was designed as an interface rather than an entire machine learning platform, according to Chollet. Regardless of the backend scientific computing library, it presents a higher-level, more understandable set of abstractions that make it simple to configure neural networks.
Numpy
NumPy targets the non-optimizing CPython bytecode interpreter, which is the Python reference implementation.
Pandas
Panel data, an econometrics term for multidimensional structured data sets, is where the word “Pandas” originates.
Scipy
SciPy is based on the NumPy array object and is a component of the NumPy stack, which also contains the scientific computing libraries SymPy, pandas, and Matplotlib. Users of this NumPy stack are comparable to those of programmes like MATLAB, GNU Octave, and Scilab. The SciPy stack is a different name for the NumPy stack.
Matplotlib
Matplotlib is a plotting library. It offers a general-purpose GUI toolkit API for embedding plots into applications. It is not recommended to use the procedural “pylab” interface, which is based on a state machine (similar to OpenGL) and was created to closely mimic the MATLAB interface. Matplotlib is used by SciPy.
Matplotlib’s pyplot module offers an interface akin to MATLAB.
With the benefit of using Python and being free, matplotlib is made to be as useable as MATLAB.
Data Visualization
PySpark
Big-O
