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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.

Multiple businesses have benefitted from his web programming expertise. He does all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies he has used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases he has used MySQL, Postgres, Redis, MongoDB, and more.

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.

Multiple businesses have benefitted from his web programming expertise. He does all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies he has used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases he has used MySQL, Postgres, Redis, MongoDB, and more.

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

Compatibility

- Internet required

Terms

- Instant digital redemption

- What is a neural network?
- Introduction and Outline (3:45)
- Neural Networks with No Math (4:20)
- Where does this course fit into your deep learning studies? (4:57)
- Introduction to the E-Commerce Course Project (8:53)

- Classifying more than 2 things at a time
- From Logistic Regression to Neural Networks (5:12)
- Softmax (2:54)
- Sigmoid vs. Softmax (1:30)
- Where to get the code for this course (1:30)
- Softmax in Code (3:39)
- Building an entire feedforward neural network in Python (6:23)
- E-Commerce Course Project: Pre-Processing the Data (5:24)
- E-Commerce Course Project: Making Predictions (3:55)
- Absence of non-linearities

- Training a neural network
- Backpropagation Intro (11:50)
- Backpropagation - what does the weight update depend on? (4:47)
- Backpropagation - recursiveness (4:38)
- Backpropagation in Code (17:07)
- The WRONG Way to Learn Backpropagation (3:52)
- E-Commerce Course Project: Training Logistic Regression with Softmax (8:11)
- E-Commerce Course Project: Training a Neural Network (6:19)
- Backpropagation for binary output

- Practical Machine Learning
- Donut and XOR Review (1:06)
- Donut and XOR Revisited (4:21)
- Common nonlinearities and their derivatives (1:26)
- Hyperparameters and Cross-Validation (4:11)
- Manually Choosing Learning Rate and Regularization Penalty (4:08)

- TensorFlow, exercises, practice, and what to learn next
- TensorFlow plug-and-play example (7:31)
- Visualizing what a neural network has learned using TensorFlow Playground (11:35)
- Where to go from here (3:41)
- You know more than you think you know (4:52)
- How to get good at deep learning + exercises (5:07)

- Project: Facial Expression Recognition
- Facial Expression Recognition Problem Description (12:21)
- The class imbalance problem
- Utilities walkthrough (5:45)
- Facial Expression Recognition in Code (Binary / Sigmoid) (12:13)
- Facial Expression Recognition in Code (Logistic Regression Softmax) (8:57)
- Facial Expression Recognition in Code (ANN Softmax) (10:44)

- Appendix
- How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow (17:22)
- Gradient Descent Tutorial (4:30)

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lifetime

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4 Hours