zmyzheng/Neural-Networks-and-Deep-Learning
Deep learning projects including applications (face recognition, neural style transfer, autonomous driving, sign language reading, music generation, translation, speech recognition and NLP) and theories (CNNs, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, hyperparameter tuning, regularization, optimization, Residual Networks). Deep Learning Specialization by Andrew Ng, deeplearning.ai
This collection of projects is designed to help those learning about deep neural networks apply theoretical concepts to real-world problems. You'll gain practical experience in various applications like image classification, face recognition, music generation, and language translation. This resource is for students, researchers, or anyone seeking hands-on learning in the field of artificial intelligence and machine learning.
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Use this if you are studying deep learning and want concrete examples to understand how neural networks are built and applied across different domains.
Not ideal if you are looking for ready-to-use, deployable applications or production-ready code for specific AI tasks.
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Last pushed
Oct 03, 2018
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