Cheat Sheets for Artificial Intelligence, Neural Networks, Machine Learning, Deep Learning

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.

MACHINE LEARNING: ALGORITHM CHEAT SHEET

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.

Python for Data Science

A language of programming is a batch of instructions producing input, which is termed output productivity. Languages of programming are built on algorithms and establish a framework for maximizing access and progress. Essentially, apps, websites, and programs are valued for development. Python is the best language for Data Science and it has several syntactic words and conditions. Specific experiences include being a knowledgeable coder.

• TensorFlow
• Scikit-Learn
• Keras
• Numpy
• Data Wrangling
• Scipy
• Matplotlib:
• Data Visualization
• PySpark
• Big-O
• Neural Networks

#artificial-intelligence #machine-learning #deep-learning #big-data #deep learning #machine learning

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Cheat Sheets for Artificial Intelligence, Neural Networks, Machine Learning, Deep Learning

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.

MACHINE LEARNING: ALGORITHM CHEAT SHEET

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.

Python for Data Science

A language of programming is a batch of instructions producing input, which is termed output productivity. Languages of programming are built on algorithms and establish a framework for maximizing access and progress. Essentially, apps, websites, and programs are valued for development. Python is the best language for Data Science and it has several syntactic words and conditions. Specific experiences include being a knowledgeable coder.

• TensorFlow
• Scikit-Learn
• Keras
• Numpy
• Data Wrangling
• Scipy
• Matplotlib:
• Data Visualization
• PySpark
• Big-O
• Neural Networks

#artificial-intelligence #machine-learning #deep-learning #big-data #deep learning #machine learning

How are deep learning, artificial intelligence and machine learning related

So you find yourself saying “Well, it’s time we step on to digital transformation for our organization. Let’s look at the technologies we can implement.”

When you complete saying that sentence, the first thing that comes to your mind is Artificial Intelligence(AI) systems. You think of intelligence machines that can execute tasks on their human and make insightful decisions — like Sophie or Watson.

“So artificial intelligence(AI) is what we need.”, you say to yourself

Yes and No. Yes, in the sense that AI machines are useful for digital transformation.

No in the sense that you will integrate Artificial Intelligence with the help of algorithms which build the foundation for these systems. So you are not integrating AI but the algorithms that make AI machines work.

Here’s a simple explanation — The process that you want to improve through digital transformation will be optimized through AI machines.

These machines will be developed using a subset of AI — Machine Learning Algorithms. To go further deep — your organization can also implement Deep Learning — a subset of Machine Learning.

#artificial-intelligence #machine-intelligence #deep-learning #machine-learning #machine-learning-ai

Deep Learning Explained in Layman's Terms

In this post, you will get to learn deep learning through a simple explanation (layman terms) and examples.

Deep learning is part or subset of machine learning and not something that is different than machine learning. Many of us, when starting to learn machine learning, try and look for the answers to the question, “What is the difference between machine learning and deep learning?” Well, both machine learning and deep learning are about learning from past experience (data) and make predictions on future data.

Deep learning can be termed as an approach to machine learning where learning from past data happens based on artificial neural networks (a mathematical model mimicking the human brain). Here is the diagram representing the similarity and dissimilarity between machine learning and deep learning at a very high level.

#machine learning #artificial intelligence #deep learning #neural networks #deep neural networks #deep learning basics

How Artificial Intelligence Is Reshaping the IT Industry

Artificial Intelligence has powerfully penetrated the way we live. It doesn’t only change the way we work but also reshaped how we used to live. Speaking of AI, it is one of the most interesting technologies that we’ve ever encountered.

Without a doubt, AI is contributing a lot in boosting business and IT productivity. Therefore, in this blog, I will highlight important insights on how AI is reshaping IT. Before digging deeper into details, let’s start with some basics on AI and how it works.

#learn-artificial-intelligence #iot-and-artificial-intelligence #artificial-intelligence-trends #artificial-intelligence-danger #machine-learning #deep-learning

Convolutional Neural Networks

CNN’s are a special type of ANN which accepts images as inputs. Below is the representation of a basic neuron of an ANN which takes as input X vector. The values in the X vector is then multiplied by corresponding weights to form a linear combination. To thus, a non-linearity function or an activation function is imposed so as to get the final output.

Neuron representation, Image by author

Why CNN?

Talking about grayscale images, they have pixel ranges from 0 to 255 i.e. 8-bit pixel values. If the size of the image is NxM, then the size of the input vector will be NM. For RGB images, it would be NM*3. Consider an RGB image with size 30x30. This would require 2700 neurons. An RGB image of size 256x256 would require over 100000 neurons. ANN takes a vector of inputs and gives a product as a vector from another hidden layer that is fully connected to the input. The number of weights, parameters for 224x224x3 is very high. A single neuron in the output layer will have 224x224x3 weights coming into it. This would require more computation, memory, and data. CNN exploits the structure of images leading to a sparse connection between input and output neurons. Each layer performs convolution on CNN. CNN takes input as an image volume for the RGB image. Basically, an image is taken as an input and we apply kernel/filter on the image to get the output. CNN also enables parameter sharing between the output neurons which means that a feature detector (for example horizontal edge detector) that’s useful in one part of the image is probably useful in another part of the image.

Convolutions

Every output neuron is connected to a small neighborhood in the input through a weight matrix also referred to as a kernel or a weight matrix. We can define multiple kernels for every convolution layer each giving rise to an output. Each filter is moved around the input image giving rise to a 2nd output. The outputs corresponding to each filter are stacked giving rise to an output volume.

Convolution operation, Image by indoml

Here the matrix values are multiplied with corresponding values of kernel filter and then summation operation is performed to get the final output. The kernel filter slides over the input matrix in order to get the output vector. If the input matrix has dimensions of Nx and Ny, and the kernel matrix has dimensions of Fx and Fy, then the final output will have a dimension of Nx-Fx+1 and Ny-Fy+1. In CNN’s, weights represent a kernel filter. K kernel maps will provide k kernel features.

#artificial-neural-network #artificial-intelligence #convolutional-network #deep-learning #machine-learning #deep learning