The latest Python deep learning technology advancements and applications (graph neural network)
In recent years, with the rapid development of deep learning represented by convolutional neural networks (CNN), artificial intelligence has entered the third wave of development, and AI technology has been increasingly used in various fields. In order to help students learn more deeply about the new theories and new technologies in the field of artificial intelligence in the past 3-5 years, Ai Shang Training has launched a new "Python Deep Learning Advanced and Application" training course, allowing you to systematically master the new theories and new technologies of AI. Method and its Python code implementation. Attention mechanism, Transformer model (BERT, GPT-1/2/3/3.5/4, DETR, ViT, Swin Transformer, etc.), generative model (variational autoencoder VAE, generative adversarial network GAN, diffusion model Diffusion Model, etc.), target detection algorithms (R-CNN, Fast R-CNN, Faster R-CNN, YOLO, SDD, etc.), graph neural networks (GCN, GAT, GIN, etc.), reinforcement learning (Q-Learning, DQN, etc.) , the basic principles of deep learning model interpretability and visualization methods (CAM, Grad-CAM, LIME, t-SNE, etc.) and the implementation methods of Python code .
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Note: Please prepare your own computer and install the required software in advance.