Deep Dive into Python Machine Learning

Deep learning is currently one of the best providers of solutions for problems in image recognition, speech recognition, object recognition, and natural language with its growing number of Python library collections. The purpose of deep learning is to develop deep neural networks by increasing and improving the number of training layers for each network so that a machine learns more about the data until it is as accurate as possible. Developers can use the techniques provided by deep learning to complete complex machine learning tasks, and train AI networks to develop deep levels of perceptual recognition.

Deep learning is the next step to machine learning with a more advanced implementation. Currently, it’s not established as an industry standard, but is headed in that direction, and puts strong promise of being a game changer when handling raw unstructured data. Deep learning is currently one of the best providers of solutions for problems in image recognition, speech recognition, object recognition, and natural language processing. Developers can benefit from building AI programs that, instead of using hand coded rules, learn from examples of how to solve complex tasks. With deep learning being used by many data scientists, deeper neural networks are evaluated for accurate results.

This course takes you from basic calculus knowledge to understanding backpropagation and its application for training in neural networks for deep learning and understanding automatic differentiation. Through the course, we will cover thorough training in convolutional, recurrent neural networks and build the theory that focuses on supervised learning and integrate into your product offerings such as search, image recognition, and object processing. Also, we will examine the performance of the sentimental analysis model and conclude with the introduction of Tensorflow. 

By the end of this course, you can start working with deep learning right away. This course will make you confident in its implementation in your current work as well as further research.