Convolutional Neural Networks in TensorFlow

via Coursera

Overview

Unlock the power of AI with "Convolutional Neural Networks in TensorFlow," a crucial installment in the Machine Learning in TensorFlow Specialization offered by deeplearning.ai on Coursera. Ideal for software developers keen on crafting scalable AI-powered algorithms, this course dives deep into the best practices for deploying TensorFlow, the widely acclaimed open-source machine learning framework.

In this advanced second course of the TensorFlow Specialization, participants will refine the computer vision models they developed previously, mastering techniques to handle real-world images of various shapes and sizes. Discover the fascinating process of how images traverse through convolutions, offering a glimpse into the computer's perception of information. Gain hands-on experience in visualizing loss and accuracy metrics, and learn innovative strategies to combat overfitting, such as augmentation and dropout. Additionally, the course unveils the potential of transfer learning, demonstrating how to leverage previously learned features from models to enhance your projects.

Brought to you by Andrew Ng's Machine Learning and Deep Learning courses, this specialization lays a robust foundation in the critical principles of these fields. By exploring TensorFlow through this specialization, you're not just learning to apply these models; you're gaining a deeper insight into the mechanics of neural networks. To further solidify your understanding, we also recommend the Deep Learning Specialization as a subsequent venture.

Provided by Coursera, this course is categorized under Machine Learning, Neural Networks, TensorFlow, and Convolutional Neural Networks (CNN), marking a pivotal step for those aspiring to excel in the rapidly evolving domain of machine learning.