Thomashenson.com Website Review

but what do they really do? basically a data scientist’s job is to find correlation in data that might be able to predict outcomes. most of the time their job is spent data cleansing and building models using their heavy math skills. the development and architecture you like math and love data then data scientist might be the right career path for you. recently de*p learning has emerged as a hot field within the data science community. let’s explore some of the basic terms around de*p learning. de*p learning is a form of artificial intelligence where data scientist use neural networks to train models. neural networks are comprised of 3 layers that allow for models to be trained by mimicking the way our brains learn. in de*p learning the features and weights of the features are not explicitly programmed, but learned by the neural network. if you are looking to compare machine learning to de*p learning just remember, machine learning is where we define the features, but de*p learning is where the neural network will decide the features. for example a machine learning dog breed detection model would require us to program the features like ear length, nose size, color, height, weight, fur, etc. in de*p learning we would allow the neural network to decide the features and weights for those features. most of the de*p learning environment will use gpus to take advantage gpus ability to quickly compute computations the easiest way to understand training in de*p learning is to think of it as testing. in software development we talk about test environments and how you never code in production right? de*p learning we refer to test as our training environment where we allow our models to learn. if you were creating a model to identify breeds of dogs, the training phase is where you would feed the input layer with millions and millions of images. during this phase both forward and backward propagation allow the model to be developed. then it’s on the #2…