What you will learn:
- Fundamental concepts of Deep Learning, including various Neural Networks for supervised and unsupervised learning.
- Build, train, and deploy different types of Deep Architectures, including Convolutional Networks, Recurrent Networks, and Autoencoders.
- Application of Deep Learning to real-world scenarios such as object recognition and Computer Vision, image and video processing, text analytics, Natural Language Processing, recommender systems, and other types of classifiers.
- Master Deep Learning at scale with accelerated hardware and GPUs.
- Use of popular Deep Learning libraries such as Keras, PyTorch, and Tensorflow applied to industry problems.
This course is the first part in a two part course and will teach you the fundamentals of PyTorch. In this course you will implement classic machine learning algorithms, focusing on how PyTorch creates and optimizes models. You will quickly iterate through different aspects of PyTorch giving you strong [...]
This course is the second part of a two-part course on how to develop Deep Learning models using Pytorch. In the first course, you learned the basics of PyTorch; in this course, you will learn how to build deep neural networks in PyTorch. Also, you will learn how [...]
New to deep learning? Start with this course, that will not only introduce you to the field of deep learning but give you the opportunity to build your first deep learning model using the popular Keras library. Looking to kickstart a career in deep learning? Look no further. This [...]
In this capstone project, you will apply your newly acquired deep learning knowledge and expertise to a real world challenge. In this capstone project, you'll use a Deep Learning library of your choice to develop, train, and test a Deep Learning model. Load and preprocess data for a real [...]
Training complex deep learning models with large datasets takes a long time. In this course, you will learn how to use accelerated GPU hardware to overcome the scalability problem in deep learning. Training a complex deep learning model with a very large dataset can take hours, days and occasionally [...]