Size: 1.02 GB
Computer Vision and Data Science and Machine Learning combined! In Theano and TensorFlow.
What Will I Learn?
- Understand convolution
Understand how convolution can be applied to audio effects
Understand how convolution can be applied to image effects
- Implement Gaussian blur and edge detection in code
- Implement a simple echo effect in code
- Understand how convolution helps image classification
- Understand and explain the architecture of a convolutional neural network (CNN)
- Implement a convolutional neural network in Theano
- Implement a convolutional neural network in TensorFlow
- Install Python, Numpy, Scipy, Matplotlib, Scikit Learn, Theano, and TensorFlow
- Learn about backpropagation from Deep Learning in Python part 1
- Learn about Theano and TensorFlow implementations of Neural Networks from Deep Learning part 2
DescriptionThis is the 3rd part in my Data Science and Machine Learning series on Deep Learning in Python. At this point, you already know a lot about neural networks and deep learning, including not just the basics like backpropagation, but how to improve it using modern techniques like momentum and adaptive learning rates. You’ve already written deep neural networks in Theano and TensorFlow, and you know how to run code using the GPU. This course is all about how to use deep learning for computer vision using convolutional neural networks. These are the state of the art when it comes to image classification and they beat vanilla deep networks at tasks like MNIST.
- linear algebra
- Python coding: if/else, loops, lists, dicts, sets
- Numpy coding: matrix and vector operations, loading a CSV file
- Can write a feedforward neural network in Theano and TensorFlow
- Watch it at 2x.
- Take handwritten notes. This will drastically increase your ability to retain the information.
- Write down the equations. If you don’t, I guarantee it will just look like gibberish.
- Ask lots of questions on the discussion board. The more the better!
- Realize that most exercises will take you days or weeks to complete.
- Write code yourself, don’t just sit there and look at my code.
- Check out the lecture “What order should I take your courses in?” (available in the Appendix of any of my courses, including the free Numpy course)
- Students and professional computer scientists
- Software engineers
- Data scientists who work on computer vision tasks
- Those who want to apply deep learning to images
- Those who want to expand their knowledge of deep learning past vanilla deep networks
- People who don’t know what backpropagation is or how it works should not take this course, but instead, take parts 1 and 2.
- People who are not comfortable with Theano and TensorFlow basics should take part 2 before taking this course.