Deep Learning: Convolutional Neural Networks in Python Course
TensorFlow 2 CNNs for Computer Vision, Natural Language Processing (NLP) +More! For Data Science & Machine Learning
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.
All of the materials required for this course can be downloaded and installed for FREE. We will do most of our work in NumPy, Matplotlib, and TensorFlow. I am always available to answer your questions and help you along your data science journey.
This course focuses on “how to build and understand”, not just “how to use”. Anyone can learn to use an API in 15 minutes after reading some documentation. It’s not about “remembering facts”, it’s about “seeing for yourself” via experimentation. It will teach you how to visualize what’s happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.
Best Seller Course: Deep Learning Prerequisites: Linear Regression in Python
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)
Recommended Course: Deep Learning A-Z™ 2023: Neural Networks, AI & ChatGPT Bonus
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