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Updated in [February 21st, 2023]

This course provides an introduction to Keras and TensorFlow, two powerful tools for deep learning and neural networks. It covers the fundamentals of deep learning, data processing for neural network training, creating and training an artificial neural network, building a validation set, making predictions, creating a confusion matrix, saving and loading a model, image preparation for CNNs, building and training a CNN, making predictions with a CNN, building a fine-tuned neural network, training a fine-tuned neural network, predicting with a fine-tuned neural network, MobileNet image classification, processing images for fine-tuned MobileNet, fine-tuning MobileNet on a custom data set, and data augmentation.
Possible Development Paths include becoming a deep learning engineer, data scientist, or machine learning engineer. Learners can also pursue further education in the field of deep learning, such as a master's degree in artificial intelligence or a PhD in computer science.
Learning Suggestions for learners include taking courses in related subjects such as Python programming, machine learning, and data science. Learners should also practice coding and building projects with Keras and TensorFlow to gain hands-on experience. Additionally, learners should stay up to date with the latest developments in deep learning and neural networks.