Building Classification Models with TensorFlow

MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 3 Hours 16M | 351 MB

Genre: eLearning | Language: English

This course will teach you the finer points of building such models as well the logistic regression, nearest-neighbor methods, and metrics for evaluating classifiers such as accuracy, precision, and recall.

TensorFlow is a great way to implement powerful classification models such as Convolutional Neural Networks and Recurrent Neural Networks. In this coures, Building Classification Models with TensorFlow, you'll learn the finer points of building models with TensorFlow. First, you'll explore the logistic regression in TensorFlow. Next, you'll discover nearest-neighbor methods. Finally, you'll learn the metrics for evaluating classifiers such as accuracy, precision, and recall. Recurrent Neural Networks (RNNs) are a versatile and powerful form of NN that is fast gaining popularity in applications that need to consider context. RNNs are ideal for considering sequences of data - frames in a movie, sentences in a paragraph, or stock returns in a period. Convolutional Neural Networks (CNNs) are a class of deep, feed-forward artificial neural network that has successfully been applied to analyzing visual imagery. CNNs are widely used in image and video recognition. By the end of this course, you'll have a better understanding on how to build classification models with TensorFlow.