TensorFlow 101: Introduction to Deep Learning
What you'll learn
- You will be able to build deep learning models for different business domains in TensorFlow
- You can distinguish classification and regression problems, apply supervised learning, and can develop solutions
- You can also apply segmentation analysis through unsupervised learning and clustering
- You can consume TensorFlow via Keras in easier way.
- Informed about tuning machine learning models to produce more successful results
- Learn how face recognition works
Requirements
- Familiar with machine learning concepts
- Basic Python
Description
This course provides you to be able to build Deep Neural Networks models for different business domains with one of the most common machine learning library TensorFlow provided by Google AI team. The both concept of deep learning and its applications will be mentioned in this course. Also, we will focus on Keras.
We will also focus on the advanced topics in this lecture such as transfer learning, autoencoders, face recognition (including those models: VGG-Face, Google FaceNet, OpenFace and Facebook DeepFace).
This course appeals to ones who interested in Machine Learning, Data Science and AI. Also, you don't have to be attend any ML course before.
Who this course is for:
- One who interested in Machine Learning, Data Science and AI
- Anyone who would like to learn TensorFlow framework
Instructor
- 4.2 Instructor Rating
- 583 Reviews
- 7,002 Students
- 6 Courses
Serengil received his MSc in Computer Science from Galatasaray University in 2011.
He has been working as a software developer since 2010.
His current research interests are Machine Learning, particularly applications of Deep Learning and Cryptography in particular Elliptic Curve cryptosystems.
Serengil contributed many open source projects such as DeepFace, RetinaFace and ChefBoost. These repositories got thousands of stars on GitHub, millions of installations on pip; and 300+ citations in academic research papers and articles.