The Artificial Intelligence tutorial is an introduction to AI and helps you learn Artificial Intelligence from basics of AI, Machine Learning, Deep Learning, 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.
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.
This 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.
Before going through this tutorial you should have 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.
This 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.
Artificial Intelligence(AI) is everywhere around us, say voice recognition software in your mobile phone or navigations in car. Even Google uses Artificial Intelligence for its search engine. Netflix, amazon prime movie suggestions are based on Artificial Intelligence. Interacting with Siri, Alexa, Google assistant is also a form of Artificial Intelligence. Artificial Intelligence is a broad area of Read More
Chat bots : Replacement of humans by chat bots. They feed the manual conversations to the AI, and it is trained to interact to human. Ex. Companies providing chat support by bots instead of human. Sentiment analysis : Find the buying behavior and patterns of people and sales predictions. Self driven cars : Making Read More
ANN was developed considering the same as of our brain, same how our brain works was taken into account. It was inspired by the way neurons work, the major task is to process information. The architecture of neural network is similar to neurons. Frank Rosenblatt in 1958 invented ANN and built the machine learning algorithm. Each neural Read More
The input layer is usually a vector, the neural network learns the pattern by learning the weights. The architecture, activation functions, layers in it, dropouts, weights of each epoch is saved in pickle file. There are also the biases stored. In the first phase, we do a Forward pass. The input for is We need to Read More
The neural nets exists and in addition to that an image is convoluted, converted in pixel level and studied, converted and a max pooling, this entire thing is known as convolution + pooling layers. A fully connected layers of flattened structure of numpy array and a hidden layer is then classified into various classes as binary or Read More
In this tutorial we will see about deep learning with Recurrent Neural Network, architecture of RNN, comparison between NN & RNN, variants of RNN, applications of AE, Autoencoders - architecture and application. Deep neural networks Convolutional neural networks Recurrent neural networks Provides lift for classification and forecasting Read More
In this tutorial we are going to see about the machine learning flow from development to release phase, what is the need of saving a model and basics of OpenCV, GAN We need to save the models whenever we run as pickle file. Machine learning workflow is these following steps - Understanding Problem statement Gathering the Read More
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