The Deep Learning with Python training course takes you from basic calculus knowledge to its application in Python for training neural networks for deep learning. Through the course, we will cover thorough training in convolutional, recurrent neural networks and build up the theory that focuses on supervised learning and integrate into our product offerings such as search, image recognition, and object processing. Also, we will examine the performance of the sentimental analysis model and will conclude with the introduction of the popular Python framework, TensorFlow. By the end of this course, you can start working with deep learning right away. This course will make you confident about its implementation in your current work as well as further research.
By attending Deep Learning with Python Workshop, delegates will learn:
- Quick introduction to the deep learning algorithms and techniques
- Understand the strategies to use deep learning algorithms and libraries in the real world
- Explore popular Python packages such as TensorFlow, Caffe, and Keras
- Implement autoencoders and Restricted Boltzmann Machines
- Dive into Deep Belief Nets and Deep Neural Networks
- Discover more deep learning algorithms with Dropout and Convolutional Neural Networks
Data Science practitioners as well as machine learning practitioners who aspire to get a fundamental understanding of deep learning will find this course to be useful. Knowledge of Python programming is required.