Artificial neural networks are the architecture that make Apple's Siri recognize your voice, Tesla's self-driving cars know where to turn, Google Translate learn new languages, and so many more technological features you have quite possibly taken for granted. The data science that unites all of them is Deep Learning. In this course, you'll build your very first neural network, going beyond basic models to build networks that automatically learn features.
Like what you're learning? Try out the The Advanced Guide to Deep Learning and Artificial Intelligence next.
- Access 37 lectures & 4 hours of content 24/7
- Extend the binary classification model to multiple classes uing the softmax function
- Code the important training method, backpropagation, in Numpy
- Implement a neural network using Google's TensorFlow library
- Predict user actions on a website given user data using a neural network
- Use Deep Learning for facial expression recognition
- Learn some of the newest development in neural networks
The Lazy Programmer is a data scientist, big data engineer, and full stack software engineer. For his master's thesis he worked on brain-computer interfaces using machine learning. These assist non-verbal and non-mobile persons to communicate with their family and caregivers.
He has worked in online advertising and digital media as both a data scientist and big data engineer, and built various high-throughput web services around said data. He has created new big data pipelines using Hadoop/Pig/MapReduce, and created machine learning models to predict click-through rate, news feed recommender systems using linear regression, Bayesian Bandits, and collaborative filtering and validated the results using A/B testing.
He has taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Humber College, and The New School.
Details & Requirements
- Length of time users can access this course: lifetime
- Access options: web streaming, mobile streaming
- Certification of completion not included
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: intermediate, but you must have some knowledge of calculus, linear algebra, probability, Python, and Numpy
- All code for this course is available for download here, in the directory ann_class
- Unredeemed licenses can be returned for store credit within 30 days of purchase. Once your license is redeemed, all sales are final.