About this Course
Deep learning is a subfield of artificial intelligence that is inspired by how the human brain works, a concept often referred to as neural networks. In the last decade we’ve seen significant development of deep learning methods that enable state-of-the-art performance for many tasks, including image classification, audio classification and natural language processing. In this course, you’ll gain hands-on experience in both feedforward and recurrent neural networks.
WHAT YOU’LL LEARN
- Techniques for constructing multilayer perceptron models, embedding models, and recurrent and convolutional neural network models
- Ways to use regularization, dropout, and batch normalization to improve generalization
- How to apply gradient descent and adaptive gradient descent
GET HANDS-ON EXPERIENCE
- Gain experience in both R and Python programming
- Apply techniques using popular open-source tools, including scikit-learn, Anaconda3, Apache Spark, Tensorflow and Keras.