Mastering Keras: Design and train advanced Deep Learning models for semi-supervised learning, object detection and much more

Mastering Keras: Design and train advanced Deep Learning models for semi-supervised learning, object detection and much more
Mastering Keras: Design and train advanced Deep Learning models for semi-supervised learning, object detection and much more
English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 5h 17m | 1.06 GB

Explore powerful deep learning techniques using Keras

Successful data scientists need to be able to work with the most powerful tools to solve the most challenging problems. As deep learning becomes ever more entrenched as the gold-standard tool for a wide variety of advanced data analytics and Artificial Intelligence problems, it is essential for you as a data scientist or analyst to be comfortable wielding these powerful techniques on an ever-expanding array of problems.

TensorFlow (and its easy-to-learn deep learning wrapper Keras) have become game-changers in permitting simple implementations of the most complex of deep learning techniques.

In this course, we teach you to go beyond your working knowledge of Keras, begin to wield its full power, and unleash the amazing potential of advanced deep learning on your data science problems. You’ll learn to design and train deep learning models for synthetic data generation, object detection, one-shot learning, and much more.

By the end of this course, you will be able to implement many advanced deep learning modelling algorithms and adapt them to your own purposes. Perhaps the next great breakthrough will come from you?

Please note that familiarity with machine learning and deep learning approaches, together with practical experience with Keras and Python programming, are assumed for taking this course.

Learn

  • Use the powerfully functional Keras API to design and implement advanced deep learning techniques
  • Design and implement advanced Convolutional Neural Networks for powerful image classification
  • Design and implement object detection networks to identify objects present in images and their location
  • Work with deep generative neural networks for synthetic data generation and semi-supervised learning
  • Develop a stable deep reinforcement-learning system and learn to make optimal decisions via feedback from their environment
  • Implement deep one-shot learning systems that can classify new instances of a class after a single exposure to such an object