Learning Deep learning and latest AI technologies<\/h3>\n
Deep learning has\u00a0become one of the most innovative\u00a0technologies and our society is facing to the change coming\u00a0from its\u00a0big impact. In the future, no matter what kind of job \/ what kind of you\u00a0are involved in, you will be influenced by the impact of advanced AI technologies. Deep Learning JP is offering a series of educational programs including this course named “Deep learning basics<\/strong>” as a preparation for such future.<\/p>\n<\/div><\/div> Deep Learning Basics<\/strong><\/p>\n This course is designed to cover wide range of Deep learning technologies which are commonly used in latest researches \/ developments. It starts from basic topics on machine learning such as Logistic regression, MLP and basics of neural networks. After mastering the basic topics, it moves on to more advanced topics such as\u00a0autoencoders, CNNs, generative models, RNNs and language models. One of the most important characteristics of this course is that it’s a fully implementation-oriented course with practical exercises. In each chapter, you need to\u00a0tackle on a number of problems and home works that we provide. To allow you to focus on learning the principles of deep learning technologies rather than managing computational environment, we provide\u00a0iLect.net<\/a>,\u00a0an online GPU programming environment on fully-virtualized servers.<\/p>\n Agenda<\/strong><\/p>\n\n
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\nWhat’s Deep Learning? The impact, influence for our society. Explanation of the course.<\/li>\n
\nPython and Linear Algebra, Matrix and Tensor, etc.<\/li>\n
\nk-NN, Logistic Regression, Softmax, train\/dev\/test Data set, Learning Process<\/li>\n
\nGradient Descent, MLP, Logistic Regression, Softmax<\/li>\n
\nTensorFlow Basics, Optimizers, etc.<\/li>\n
\nDeep Learning, Feature Extraction, Autoencoder, dA, SdA, Sparse Coding, GPU<\/li>\n
\nCNN Basics, Convolution, Pooling<\/li>\n
\nImage Processing, Data Augmentation, Batch Normalization, Visualization, etc.<\/li>\n
\nSeries data and RNN<\/li>\n
\nEmbedding, Projection, Word2vec, sequence-to-sequence<\/li>\n
\nAttention, Image Caption<\/li>\n
\nAdvanced Topics<\/li>\n<\/ol>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/p>","protected":false},"excerpt":{"rendered":"","protected":false},"author":14,"featured_media":0,"parent":552,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"tags":[],"class_list":["post-812","page","type-page","status-publish","hentry"],"acf":[],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\t\n\n\t\t\n