Deep Learning in Practice I: Tensorflow Basics and Datasets
Tensorflow is the subject of several tutorials and courses. In contrast, certain practical skills are hard to come by in this vast collection of deep learning resources, and acquiring them would take a lot of time. So you're the target audience for this Tensorflow course! The student will be introduced to the fundamentals of executing a classification project using deep neural networks before learning how to create a dataset for professional deep learning projects at an industrial level. The learner will discover how to use Tensorflow and Keras to create a face recognition software.
This course covers that gap by offering a number of hands-on projects and practical lectures in which the teacher presents the best practices that deep learning practitioners need to be aware of in order to carry out deep learning projects. The detailed explanations provided by the hands-on projects cover every step of the deep learning process, from data collection through data loading, preprocessing, training, and assessment.
Requirements:
- Understand the basic concepts of machine learning (recommended, but not required)
- Be familiar with Python programming language and data structures (Numpy, Pandas)
- Understand the basic concepts of neural networks (recommended, but not required)
Who this course is for:
- Someone who learned the concepts of deep learning, but want to master the practical aspects of deep learning projects
- PhD and Master students doing thesis on deep learning
- Any enthusiast about artificial intelligence and deep learning
- Computer vision practitioners
- Anyone who would like to learn about best practices in deep learning
- Anyone who like to quickly start with deep learning without having a background in it
Course Rating: 4.7/5
Enroll here: https://www.udemy.com/course/deep-learning-in-practice-i/
