Deep Learning Classic Network Architecture Practical Series
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Deep learning classic model architecture actual combat series of courses are designed to help students quickly master the major classic network architectures of deep learning, interpret the core knowledge points and application fields of each module in detail, and demonstrate how to use each model for actual combat tasks. The debug mode is used in the source code. Interpret the role of each line of code. It is suitable for students who are preparing to engage in deep learning related fields and make advanced improvements.
Chapter 1 The actual classification of medical data sets based on Resnet
Chapter 2 EfficientNet network architecture analysis
Chapter 3 EfficientNet model source code interpretation
Chapter 4 Mobilenet Network Model Architecture
Chapter 5 Interpretation of MobileNet Upgraded Version
Chapter 6 3D Point Cloud PointNet Algorithm
Chapter 7 PointNet++ Algorithm Interpretation
Chapter 8 Pointnet++ Project Actual Combat
Chapter 9 Unet series algorithm explanation
Chapter 10 Unet Medical Cell Segmentation Actual Combat
Chapter 11 resnest thesis and its application strength
Chapter 12 Q-learning and DQN Algorithm
Chapter 13 DQN algorithm example demonstration
Chapter 14 Adversarial Generation Network Architecture Principles and Actual Combat Analysis
Chapter 15 Actual image synthesis based on CycleGan open source project
Chapter 16 seq2seq sequence network model
Chapter 17 Actual Combat of LAS Speech Recognition Model
Chapter 18 Interpretation of the BERT Principle of the General Framework of Natural Language Processing
Chapter 19 Interpretation and Application Examples of BERT Source Code of Google Open Source Project
Chapter 20 Basic Supplement-PyTorch Framework Basic Processing Operations
Chapter 21 Basic Supplement-Interpretation of Essential Core Modules of PyTorch Framework