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 ... functions is to introduce non-linearities into the network Introduction to Deep Learning  Activation function Introduction to Deep Learning  Neural Network Adjustements Introduction to Deep Learning. ..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