Artificial Intelligence

This Artificial Intelligence tutorial gives you an introduction to AI right from the basics. We shall be covering Machine Learning, Deep Learning and various application areas of AI, Python, various packages available in it, Tensorflow, Keras, Neural networks, Multilayer perceptron, Convolution neural networks, Recurrent neural networks, Long short term memory and OpenCV.

AI Tutorial Video:https://www.youtube.com/watch?v=W7N6LPp0SmY

What are the Goals of AI?To create machines which can do better performance than the previous version.To add new features which human possess.

But what is Artificial Intelligence?Artificial Intelligence is all around us. Artificial Intelligence creates a higher degree of efficiency and productivity by automating the repetitive task and creating immersive and responsive experience and understanding human sentiments and even emotions.This Artificial Intelligence tutorial will help you master AI by taking you through a step-by-step approach while learning AI and Machine learning concepts.

AI is able to think like the way we humans do, is able to solve problems without the explicit inputs form us, can deal with abstract concepts like ideas, and this technology is truly at attempt to understand randomness and creativity.

Artificial Intelligence Course:https://intellipaat.com/blog/tutorial/artificial-intelligence-tutorial/

AudienceThis Artificial Intelligence tutorial has been prepared to help you learn Artificial Intelligence the right way and is meant for the beginners as well as for the professionals to help them in understanding basic-to-advanced concepts related to AI. This Artificial Intelligence tutorial will help you in understanding about AI from where you will be able to take yourself to a higher level of expertise when you learn Artificial Intelligence from this tutorial.

PrerequisitesBefore going through this tutorial you should have a fundamental knowledge of information technologies such as Computers, Internet and basic working knowledge on Data. Such basic concepts will help you in understanding the AI concepts in a better way and will move you faster on the learning track.

Artificial Intelligence TrainingThis AI Tutorial covers Introduction of AI, History, Goals, Application areas, AI vs ML vs DL, Python and its installation, various data science packages, installation of python and keras, tensorflow objects, Artificial Neural networks, Multilayer perceptron, problem of overfitting, underfitting, Convolution neural networks, Recurrent neural networks, Long short term memory, OpenCV and GAN.

Understanding Deep LearningParameter InitializationFeedforward PropagationBackpropagation

Parameter Initialization: In this, parameters, i.e., weights and biases, associated with an artificial neuron are randomly initialized. After receiving the input, the network feed forwards the input and it makes associations with weights and biases to give the output. The output associated to those random values is most probably not correct. So, next, we will see feedforward propagation.

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Feedforward propagation: After initialization, when the input is given to the input layer, it propagates the input into hidden units at each layer. The nodes here do their job without being aware whether results produced are accurate or not (i.e., they don’t re-adjust according to the results produced). Then, finally, the output is produced at the output layer. This is called feedforward propagation.

Back propagation in Neural Networks: The principle behind back propagation algorithm is to reduce the error values in randomly allocated weights and biases such that it produces the correct output. The system is trained in the supervised learning method, where the error between the system’s output and a known expected output is presented to the system and used to modify its internal state. We need to update the weights such that we get the global loss minimum. This is how back propagation in neural networks works

Originally published at www.intellipaat.com on August 21, 2019.

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