Description
This course explores deep learning concepts using Google’s TensorFlow framework. Students begin with artificial neural networks (ANNs) and progress to more advanced architectures like convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Key topics include forward/backward propagation, loss functions, optimization algorithms, and hyperparameter tuning. Hands-on labs involve image recognition, sentiment analysis, and time-series forecasting. The course emphasizes using Keras for rapid prototyping and provides tools for model visualization and evaluation. Learners also explore deployment options using TensorFlow Lite or TensorFlow Serving. Perfect for aspiring AI developers and researchers aiming to build and deploy deep learning models in real-world scenarios.
吉田 美香
A well-structured course that covers all the important topics.
Benjamin Lee
初心者にもやさしい説明で、助かりました。
伊藤 健一
実践的な内容が多く、すぐに使えるスキルが学べました。
Sophia Turner
Loved the examples and hands-on exercises.