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Artificial Intelligence and Machine Learning Fundamentals

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Artificial Intelligence and Machine Learning Fundamentals Machine learning and neural networks are fast becoming pillars on which you can build intelligent applications. The course will begin by introducing . Description Machine learning and neural networks are fast becoming pillars on which you can build intelligent applications. The course will begin by introducing you to Python and discussing using AI search algorithms. You will learn math-heavy topics, such as regression and classification, illustrated by Python examples.


Deep Learning And Neural Networks

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If you've been following developments over the last few years, you may have noticed that deep learning and neural networks have grown wildly. Neural network architecture is able to make predictive judgments in in sports, medicine and the financial sector.


Building Convolutional Neural Networks with Tensorflow

@machinelearnbot

In the past year I have also worked with Deep Learning techniques, and I would like to share with you how to make and train a Convolutional Neural Network from scratch, using tensorflow. Later on we can use this knowledge as a building block to make interesting Deep Learning applications. The pictures here are from the full article. Source code is also provided. Before you continue, make sure you understand how a convolutional neural network works.


Deep Neural Network from Scratch in Python

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In this video we build on last week Multilayer perceptrons to allow for more flexibility in the architecture! However, we need to be careful about the layer of abstraction we put in place in order to facilitate the work of the user who want to simply fit and predict. Here we make use of the following three concept: Network, Layer and Neuron. These three components will be composed together to make a fully connected feedforward neural network neural network. For those who don't know a fully connected feedforward neural network is defined as follows (From Wikipedia): "A feedforward neural network is an artificial neural network wherein connections between the nodes do not form a cycle. As such, it is different from its descendant: recurrent neural networks. The feedforward neural network was the first and simplest type of artificial neural network devised. In this network, the information moves in only one direction, forward, from the input nodes, through the hidden nodes (if any) and to the output nodes. There are no cycles or loops in the network."


Neural Network and Deep Learning For Beginners

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Neural networks and Deep Learning, the words when witnessed, fascinate the viewers, both complement each other as they fall under the umbrella of Artificial Intelligence. This article is concentred on the discussion of above-mentioned trending and thriving technologies. You will gain some basic knowledge for commencing your learning about Neural networks and Deep Learning. It'll be also very helpful if you are looking to make the career in the field of Artificial Intelligence and Machine Learning. Basically, A Neural Network is a chain or series of algorithms that aims to recognize the relationships in a set of known data provided to us through a process that mimics the way human brain operates and analyze.