The ‘Hello World’ Of Neural Networks
Tensorflow for Artificial intelligence
Recently i completed the course about Tensor flow for Artificial Intelligence offered by Deep Learning AI, Thought to write my learned concept about this course .
Machine learning is all about a computer learning the patterns that distinguish things.Like for activity recognition, it was the pattern of walking, running and biking that can be learned from various sensors on a device.here’s our first line of code.
This is written using Python and TensorFlow and an API in TensorFlow called keras.Keras makes it really easy to define neural networks.A neural network is basically a set of functions which can learn patterns.The simplest possible neural network is one that has only one neuron in it,and that’s what this line of code does.In keras, you use the word dense to define a layer of connected neurons.There’s only one dense here.So there’s only one layer and there’s only one unit in it,so it’s a single neuron.The nice thing for now about TensorFlow and keras is that a lot of that math is implemented for you in functions.There are two function roles that you should be aware of though and these are loss functions and optimizer.
The loss function measures the data values and then gives the data to the optimizer which figures out the next guess.If you want to learn more about these particular functions, as well as the other options that might be better in other scenarios,check out the TensorFlow documentation.
Epoch is one complete cycle on all training data in neural network.When using neural networks,as they try to figure out the answers for everything, they deal in probability. You’ll see that a lot and you’ll have to adjust how you handle answers to fit.Stay tune for next article about this course .
