Over the past few months, we totally redesigned the cheat sheets so they are in high definition and downloadable. The goal was to make them easy to read and beautiful so you will want to look at them, print them and share them.
Without further ado, let’s begin.
Part 1: Neural Networks Cheat Sheets
Neural Networks Cheat Sheets
Neural Networks Basics
Neural Networks Basics Cheat Sheet
An Artificial Neuron Network (ANN), popularly known as Neural Network is a computational model based on the structure and functions of biological neural networks. It is like an artificial human nervous system for receiving, processing, and transmitting information in terms of Computer Science.
Neural Networks Graphs
Neural Networks Graphs Cheat Sheet
Graph data can be used with a lot of learning tasks contain a lot rich relation data among elements. For example, modeling physics system, predicting protein interface, and classifying diseases require that a model learns from graph inputs. Graph reasoning models can also be used for learning from non-structural data like texts and images and reasoning on extracted structures.
Part 2: Machine Learning Cheat Sheets
Machine Learning Cheat Sheets
Machine Learning with Emojis
Machine Learning with Emojis Cheat Sheet
Machine Learning: Scikit Learn Cheat Sheet
Scikit Learn Cheat Sheet
Scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines is a simple and efficient tools for data mining and data analysis. It’s built on NumPy, SciPy, and matplotlib an open source, commercially usable — BSD license
Scikit-learn Algorithm Cheat Sheet
This machine learning cheat sheet will help you find the right estimator for the job which is the most difficult part. The flowchart will help you check the documentation and rough guide of each estimator that will help you to know more about the problems and how to solve it.
Machine Learning: Scikit-Learn Algorythm for Azure Machine Learning Studios
Scikit-Learn Algorithm for Azure Machine Learning Studios Cheat Sheet
Part 3: Data Science with Python
Data Science with Python Cheat Sheets
Data Science: TensorFlow Cheat Sheet
TensorFlow Cheat Sheet
TensorFlow is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library, and is also used for machine learning applications such as neural networks.
Data Science: Python Basics Cheat Sheet
Python Basics Cheat Sheet
Python is one of the most popular data science tool due to its low and gradual learning curve and the fact that it is a fully fledged programming language.
Data Science: PySpark RDD Basics Cheat Sheet
PySpark RDD Basics Cheat Sheet
“At a high level, every Spark application consists of a driver program that runs the user’s
main function and executes various parallel operations on a cluster. The main abstraction Spark provides is a resilient distributed dataset (RDD), which is a collection of elements partitioned across the nodes of the cluster that can be operated on in parallel. RDDs are created by starting with a file in the Hadoop file system (or any other Hadoop-supported file system), or an existing Scala collection in the driver program, and transforming it. Users may also ask Spark to persist an RDD in memory, allowing it to be reused efficiently across parallel operations. Finally, RDDs automatically recover from node failures.” via Spark.Aparche.Org
Data Science: NumPy Basics Cheat Sheet
NumPy Basics Cheat Sheet
NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.
Data Science: Bokeh Cheat Sheet
Bokeh Cheat Sheet
“Bokeh is an interactive visualization library that targets modern web browsers for presentation. Its goal is to provide elegant, concise construction of versatile graphics, and to extend this capability with high-performance interactivity over very large or streaming datasets. Bokeh can help anyone who would like to quickly and easily create interactive plots, dashboards, and data applications.” from Bokeh.Pydata.com
Data Science: Karas Cheat Sheet
Karas Cheat Sheet
Keras is an open-source neural-network library written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, Theano, or PlaidML. Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible.
Data Science: Padas Basics Cheat Sheet
Padas Basics Cheat Sheet
Pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license.
Pandas Cheat Sheet: Data Wrangling in Python
Pandas Cheat Sheet: Data Wrangling in Python
The term “data wrangler” is starting to infiltrate pop culture. In the 2017 movie Kong: Skull Island, one of the characters, played by actor Marc Evan Jackson is introduced as “Steve Woodward, our data wrangler”.
Data Science: Data Wrangling with Pandas Cheat Sheet
Data Wrangling with Pandas Cheat Sheet
“Why Use tidyr & dplyr
Data Science: Data Wrangling with ddyr and tidyr
Data Wrangling with ddyr and tidyr Cheat Sheet
If you like these cheat sheets, you can let me know here.
Data Science: Scipy Linear Algebra
Scipy Linear Algebra Cheat Sheet
SciPy builds on the NumPy array object and is part of the NumPy stack which includes tools like Matplotlib, pandas and SymPy, and an expanding set of scientific computing libraries. This NumPy stack has similar users to other applications such as MATLAB, GNU Octave, and Scilab. The NumPy stack is also sometimes referred to as the SciPy stack.
Data Science: Matplotlib Cheat Sheet
Matplotlib Cheat Sheet
Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented APIfor embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK+. There is also a procedural “pylab” interface based on a state machine (like OpenGL), designed to closely resemble that of MATLAB, though its use is discouraged. SciPy makes use of matplotlib.
Pyplot is a matplotlib module which provides a MATLAB-like interface matplotlib is designed to be as usable as MATLAB, with the ability to use Python, with the advantage that it is free.
Data Science: Data Visualization with ggplot2 Cheat Sheet
Data Visualization with ggplot2 Cheat Sheet
Data Science: Big-O Cheat Sheet
Big-O Cheat Sheet
Special thanks to DataCamp, Asimov Institute, RStudios and the open source community for their content contributions. You can see originals here:
Big-O Algorithm Cheat Sheet: http://bigocheatsheet.com/
Bokeh Cheat Sheet: https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Bokeh_Cheat_Sheet.pdf
Data Science Cheat Sheet: https://www.datacamp.com/community/tutorials/python-data-science-cheat-sheet-basics
Data Wrangling Cheat Sheet: https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf
Data Wrangling: https://en.wikipedia.org/wiki/Data_wrangling
Ggplot Cheat Sheet: https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf
Keras Cheat Sheet: https://www.datacamp.com/community/blog/keras-cheat-sheet#gs.DRKeNMs
Machine Learning Cheat Sheet: https://ai.icymi.email/new-machinelearning-cheat-sheet-by-emily-barry-abdsc/
Machine Learning Cheat Sheet: https://docs.microsoft.com/en-in/azure/machine-learning/machine-learning-algorithm-cheat-sheet
ML Cheat Sheet:: http://peekaboo-vision.blogspot.com/2013/01/machine-learning-cheat-sheet-for-scikit.html
Matplotlib Cheat Sheet: https://www.datacamp.com/community/blog/python-matplotlib-cheat-sheet#gs.uEKySpY
Neural Networks Cheat Sheet: http://www.asimovinstitute.org/neural-network-zoo/
Neural Networks Graph Cheat Sheet: http://www.asimovinstitute.org/blog/
Neural Networks: https://www.quora.com/Where-can-find-a-cheat-sheet-for-neural-network
Numpy Cheat Sheet: https://www.datacamp.com/community/blog/python-numpy-cheat-sheet#gs.AK5ZBgE
Pandas Cheat Sheet: https://www.datacamp.com/community/blog/python-pandas-cheat-sheet#gs.oundfxM
Pandas Cheat Sheet: https://www.datacamp.com/community/blog/pandas-cheat-sheet-python#gs.HPFoRIc
Pyspark Cheat Sheet: https://www.datacamp.com/community/blog/pyspark-cheat-sheet-python#gs.L=J1zxQ
Scikit Cheat Sheet: https://www.datacamp.com/community/blog/scikit-learn-cheat-sheet
Scikit-learn Cheat Sheet: http://peekaboo-vision.blogspot.com/2013/01/machine-learning-cheat-sheet-for-scikit.html
Scipy Cheat Sheet: https://www.datacamp.com/community/blog/python-scipy-cheat-sheet#gs.JDSg3OI
TesorFlow Cheat Sheet: https://www.altoros.com/tensorflow-cheat-sheet.html
Tensor Flow: https://en.wikipedia.org/wiki/TensorFlow
Original article source at https://becominghuman.ai
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This is very useful for the Data science learners with full packages. For Data science projects: https://morioh.com/p/fe289410b559
Thanks and btw I have completed my Data science course in Bangalore from Learnbay with python language. Very interesting and after that only many offer I received and capable to attend many interviews. So learn and use the opportunities well
This cheat sheet helps you to choose the proper estimate for the task that is the hardest portion of the work. With modern computer technology, today’s machine learning isn’t like machine learning from the past.
The notion that computer may learn without being trained to do certain tasks came from pattern recognition researchers interested in artificial intelligence sought to explore if computers could learn from the information.
The iterative component of machine education is crucial because they may adjust autonomously when models are exposed to fresh data. From past calculations, they learn to create dependable, repeatable judgments and results. It’s not a new science, but a new one.
The usage of programming and even equipment is automation for computerized commands. AI, again, is the robots’ ability to reproduce human habits and thinking and get more clever all the time. It is important, while a misleadingly sharp computer may learn and modify its job as it receives new information, it cannot completely replace people. Everything is equal, it’s a resource, not a risk.
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AI, big data, and machine learning have witnessed exponential growth over the past few years. With the evolving technology, businesses realize the importance of adopting AI and big data in their operations. AI, big data, and machine learning create exciting new opportunities for companies and entrepreneurs. But this rapid adoption is also partnered with several complexities and risks, hence, comes the need for regulations.
Regulators and policymakers find it difficult to keep track of the constant developments in technology and AI systems. Regulators on the global governance level are trying to keep themselves updated with the growing number of AI developments to ensure the laws and regulations stay relevant with new challenges and inventions.
The regulations define the enhancement of the public sector policies and laws for the use and promotion of AI, big data, and machine learning technologies. The laws and regulations are mandatory to manage the associated risks with AI and big data. The primary approach of these regulations is towards the financial and technical implications of the use of AI. The focus is on the underlying AI technologies like machine learning algorithms, big data analytics, the level of data input, insights, algorithm testing, and others alike.
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If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
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We live in a world where billions of data points are generated every single day from different sources, such as banks, telecommunication companies, industries, tourism, the agriculture sector, educational institutions (primary, secondary, colleges, and universities), and mobile devices. Any organization can start using their data to make data-driven decision-making that is effective and supportive of their mission and vision.
Regardless of the size of the business you’re running, you need valuable data to provide you with business insights. The insights help you to know your target audience and their preferences, and as a result, your business will be able to anticipate their needs. You can use insights from big data to outperform your competition by capturing and innovating through big data.
Companies like Google and Alibaba are using it to discover flaws in their services and products, suppliers and buyers, and consumer intent and preferences so they can create newer, better ones.
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Traditional data processing application has limitations of its own in terms of processing the large chunk of complex data and this is where the big data processing application comes into play. Big data processing app can easily process complex and large information with their advanced capabilities.
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