Deep Learning Convolutional Neural Networks in Python. Use CNNs for Image Recognition, Natural Language Processing (NLP) +More! For Data Science, Machine Learning, and AI
Learn about one of the most powerful Deep Learning architectures yet!
The Convolutional Neural Network (CNN) has been used to obtain state-of-the-art results in computer vision tasks such as object detection, image segmentation, and generating photo-realistic images of people and things that don’t exist in the real world!
This course will teach you the fundamentals of convolution and why it’s useful for deep learning and even NLP (natural language processing).
You will learn about modern techniques such as data augmentation and batch normalization, and build modern architectures such as VGG yourself.
Deep Learning Convolutional Neural Networks in Python Description
Basic math (taking derivatives, matrix arithmetic, probability) is helpful
Python, Numpy, Matplotlib
What you’ll learn
Understand convolution and why it’s useful for Deep Learning
Understand and explain the architecture of a convolutional neural network (CNN)
Implement a CNN in TensorFlow 2
Apply CNNs to challenging Image Recognition tasks
Apply CNNs to Natural Language Processing (NLP) for Text Classification (e.g. Spam Detection, Sentiment Analysis)
Who this course is for:
- Students, professionals, and anyone else interested in Deep Learning, Computer Vision, or NLP
- Software Engineers and Data Scientists who want to level up their career