Bài giảng Máy học nâng cao: Deep learning an introduction cung cấp cho người học các kiến thức: Introduction, applications, convolutional neural networks and recurrent neural networks, hardware and software. Mời các bạn cùng tham khảo nội dung chi tiết.
Trịnh Tấn Đạt Khoa CNTT – Đại Học Sài Gòn Email: trinhtandat@sgu.edu.vn Website: https://sites.google.com/site/ttdat88/ Contents Introduction Applications Convolutional Neural Networks vs Recurrent Neural Networks Hardware and Software Introduction to Deep Learning Introduction to Deep Learning Why Deep Learning? Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed Methods that can learn from and make predictions on data Why Deep Learning? Why Deep Learning? Why Deep Learning? Can we learn the underlying features directly from data? Why Deep Learning? ML vs Deep Learning: Most machine learning methods work well because of human-designed representations and input features ML becomes just optimizing weights to best make a final prediction Software Software Tensorflow Software Software Software Software Software Keras: High-Level Software TensorFlow: Pretrained Models tf.keras: (https://www.tensorflow.org/api_docs/python/tf/keras/applications) TF-Slim: (https://github.com/tensorflow/models/tree/master/research/slim) Software Bài Tập 1) Cài đặt chương trình demo MNIST - image classification dùng convolutional neural network (CNN) MNIST - image classification Add DATA: Kaggle MNIST dataset MNIST dataset Model – Ví dụ Training loss/Valid loss ...Contents Introduction Applications Convolutional Neural Networks vs Recurrent Neural Networks Hardware and Software Introduction to Deep Learning Introduction to Deep Learning Why Deep Learning? ... Why Deep Learning? Why Deep Learning? Why Deep Learning? Can we learn the underlying features directly from data? Why Deep Learning? ML vs Deep Learning: Most machine learning methods work... to introduce non-linearities into the network Introduction to Deep Learning Activation function Introduction to Deep Learning Neural Network Adjustements Introduction to Deep Learning How