1-【宣导片】开启人工智能论文学习之旅.mp4 17.80M
10-07-CV Transformer-Vit-实验及结论.mp4 33.19M
100-IMAGEBIND-05-对比实验.mp4 52.49M
101-IMAGEBIND-06-消融实验.mp4 22.17M
102-IMAGEBIND-07-结论和展望.mp4 10.65M
103-IMAGEBIND-08-代码讲解(1).mp4 15.76M
104-IMAGEBIND-09-代码讲解(2).mp4 13.70M
105-IMAGEBIND-10-代码讲解(3).mp4 10.43M
106-IMAGEBIND-11-代码讲解(4).mp4 37.11M
107-IMAGEBIND-12-代码讲解(5).mp4 17.15M
108-IMAGEBIND-13-代码讲解(6).mp4 14.16M
109-IMAGEBIND-14-代码讲解(7).mp4 13.37M
11-08-CV Transformer-Vit-拓展及总结.mp4 53.54M
110-HuggingGPT-01-论文摘要.mp4 12.61M
111-HuggingGPT-02-研究背景.mp4 39.17M
112-HuggingGPT-03-方法.mp4 47.27M
113-HuggingGPT-04-实验.mp4 51.25M
114-HuggingGPT-05-结论.mp4 12.45M
115-HuggingGPT-06-代码讲解(1).mp4 15.18M
116-HuggingGPT-07-代码讲解(2).mp4 32.91M
117-HuggingGPT-08-代码讲解(3).mp4 33.01M
118-HuggingGPT-09-代码讲解(4).mp4 34.93M
119-HuggingGPT-10-代码讲解(5).mp4 13.08M
12-09-CV Transformer-Vit-代码前言.mp4 25.26M
120-GAN-01-论文摘要.mp4 30.62M
121-GAN-02-论文背景.mp4 34.92M
122-GAN-03-论文泛读.mp4 55.71M
123-GAN-04-价值函数.mp4 53.43M
124-GAN-05-训练流程&理论证明.mp4 80.64M
125-GAN-06-实验结果&总结展望.mp4 62.75M
126-GAN-07-代码分析综述.mp4 22.57M
127-GAN-08-代码分析精讲.mp4 71.16M
128-ITGAN-01-论文摘要&论文背景.mp4 62.56M
129-ITGAN-02-论文泛读.mp4 94.67M
13-10-CV Transformer-Vit-参数.mp4 9.69M
130-ITGAN-03-GAN的训练改进.mp4 68.40M
131-ITGAN-04-图像质量评价&半监督学习.mp4 47.63M
132-ITGAN-05-实验结果&论文总结.mp4 89.61M
133-ITGAN-06-代码讲解1.mp4 52.08M
134-ITGAN-07-代码讲解2.mp4 57.44M
135-ITGAN-08-代码讲解3.mp4 70.25M
136-pix2pix-01-论文摘要&论文背景.mp4 76.06M
137-pix2pix-02-论文成果及意义&论文泛读1.mp4 56.34M
138-pix2pix-03-论文泛读2.mp4 76.18M
139-pix2pix-04-目标函数&模型结构及训练参数.mp4 69.45M
14-11-CV Transformer-图像切分重排einops实现.mp4 24.87M
140-pix2pix-05-评价方式&目标函数分析&模型分析.mp4 59.13M
141-pix2pix-06-应用分析&论文总结.mp4 68.01M
142-pix2pix-07-代码讲解1.mp4 59.07M
143-pix2pix-08-代码讲解2.mp4 67.77M
144-cyclegan-01-论文摘要&研究背景.mp4 55.92M
145-cyclegan-02-论文成果及意义&论文泛读.mp4 119.20M
146-cyclegan-03-目标函数.mp4 55.10M
147-cyclegan-04-模型结构与训练参数&模型评价与比较&损失.mp4 89.40M
148-cyclegan-05-应用分析&论文总结.mp4 92.27M
149-cyclegan-06-代码讲解1.mp4 30.57M
15-12-CV Transformer-图像切分重排conv2d实现.mp4 22.59M
150-cyclegan-07-代码讲解2.mp4 66.90M
151-cyclegan-08-代码讲解3.mp4 51.22M
152-StyleGAN-01-论文摘要&研究背景.mp4 67.92M
153-StyleGAN-02-论文成果及意义&论文泛读.mp4 68.10M
154-StyleGAN-03-基于样式的生成器架构.mp4 38.00M
155-StyleGAN-04-实验结果&生成器的属性分析.mp4 71.67M
156-StyleGAN-05-隐变量解耦1.mp4 48.58M
157-StyleGAN-06-隐变量解耦2&论文总结.mp4 60.12M
158-StyleGAN-07-代码讲解1.mp4 33.81M
159-StyleGAN-08-代码讲解2.mp4 49.87M
16-13-CV Transformer-cls_token.mp4 25.35M
160-StyleGAN-09-代码讲解3.mp4 37.14M
161-YOLO-01-Anchors Base原理.mp4 28.28M
162-YOLO-02-YOLOV1.mp4 25.52M
163-YOLO-03-YOLOV2.mp4 29.75M
164-YOLO-04-YOLOV3.mp4 29.56M
165-YOLO-05-YOLOV4.mp4 22.53M
166-YOLO-06-回顾.mp4 20.80M
167-YOLO-07-YOLOV5.mp4 34.25M
168-YOLO-08-YOLOVX.mp4 23.54M
169-YOLO-09-YOLOV6.mp4 33.56M
17-14-CV Transformer-transblock.mp4 15.17M
170-YOLO-10-YOLOV7.mp4 6.71M
171-YOLO-11-各领域应用.mp4 29.88M
172-yolox-01-前言.mp4 27.49M
173-yolox-02-背景.mp4 58.32M
174-yolox-03-概览.mp4 8.81M
175-yolox-04-详解-模型框架.mp4 80.50M
176-yolox-05-详解simOTA.mp4 59.86M
177-yolox-06-详解-模型结构.mp4 50.98M
178-yolox-07-详解-预处理.mp4 51.37M
179-yolox-08-训练及总结.mp4 22.22M
18-15-CV Transformer-MHSA-实现1.mp4 32.15M
180-yolox代码-09-前言.mp4 16.47M
181-yolox代码-10-预处理-mosaic.mp4 70.77M
182-yolox代码-11-预处理-randomaffine.mp4 39.26M
183-yolox代码-12-预处理-mixup.mp4 24.96M
184-yolox代码-13-backbone.mp4 66.14M
185-yolox代码-14-pafpn.mp4 64.07M
186-yolox代码-15-bbox decode.mp4 30.31M
187-yolox代码-16-simOTA.mp4 81.54M
188-yolox代码-17 总结.mp4 25.00M
189-YOLOv5-01-目标检测技术与YOLO系列.mp4 13.01M
19-16-CV Transformer-MSHA-实现2.mp4 20.19M
190-YOLOv5-02-YOLOV3回顾.mp4 34.70M
191-YOLOv5-03-YOLOV5核心知识点1.mp4 11.52M
192-YOLOv5-04-YOLOV5核心知识点1.mp4 24.15M
193-YOLOv5-05-YOLOV5核心知识点2.mp4 60.24M
194-YOLOv5-06-YOLOV5代码讲解1.mp4 26.49M
195-YOLOV5-07-YOLOV5代码讲解2.mp4 27.99M
196-YOLOV5-08-YOLOV5代码讲解3.mp4 48.58M
197-YOLOV5-09-YOLOV5代码讲解4.mp4 53.85M
198-YOLOv5-10-YOLOV5-代码讲解5.mp4 88.80M
199-YOLOv5-11-YOLOV5-代码讲解6.mp4 30.45M
2-【宣导片2】15w同学在这征服论文.mp4 8.90M
20-17-CV Transformer-vit前向回顾.mp4 10.66M
200-YOLOv5-12-YOLOV5-代码讲解7.mp4 113.23M
201-YOLOv5-13-YOLOV5-代码讲解8.mp4 85.88M
202-YOLOv5-14-YOLOV5-代码讲解9.mp4 126.16M
203-Faster R-CNN-01-RCNN演变.mp4 39.05M
204-Faster R-CNN-02-摘要和网络结构.mp4 70.55M
205-Faster R-CNN-03-结构总览.mp4 37.69M
206-Faster R-CNN-04-背景介绍.mp4 37.15M
207-Faster R-CNN-05-RPN与rpn_loss.mp4 112.27M
208-Faster R-CNN-06-RPN训练.mp4 66.23M
209-Faster R-CNN-07-实验和结论.mp4 100.57M
21-18-CV Transformer-猫狗大战例子.mp4 39.62M
210-Faster R-CNN-08-Anchor和RPN.mp4 35.58M
211-Faster R-CNN-09-网络细节.mp4 39.25M
212-Faster R-CNN-10-训练VOC数据集.mp4 57.94M
213-Faster R-CNN-11-backbone网络讲解.mp4 56.72M
214-Faster R-CNN-12-RPN.mp4 13.43M
215-Faster R-CNN-13-数据和标签的同步处理.mp4 44.55M
216-Faster R-CNN-14-建议框的生成.mp4 56.01M
217-Faster R-CNN-15-Anchor box的生成和正负.mp4 55.27M
218-FPN-01-背景介绍.mp4 24.89M
219-FPN-02-论文泛读.mp4 46.29M
22-19-CV Transformer-总结.mp4 26.54M
220-FPN-03-背景及意义.mp4 68.42M
221-FPN-04-特征金字塔的生成.mp4 81.88M
222-FPN-05-特征金字塔与RPN及Fast Rcnn的结合.mp4 73.22M
223-FPN-06-实验和消融实验.mp4 98.43M
224-FPN-07-精读PPT补充知识.mp4 50.73M
225-FPN-08-模型训练.mp4 47.57M
226-FPN-09-网络结构总览.mp4 50.26M
227-FPN-10-Backbone_resnet50讲解.mp4 76.18M
228-FPN-11-FPN特征金字塔构建.mp4 41.87M
229-FPN-12-多尺度特征图上AnchorBox的生成.mp4 35.45M
23-swin01-前言.mp4 5.81M
230-FPN-13-RPN结构.mp4 93.12M
231-RetinaNet-01-论文背景.mp4 37.42M
232-RetinaNet-02-摘要及总结.mp4 46.39M
233-RetinaNet-03-研究背景及相关工作.mp4 89.85M
234-RetinaNet-04-Focal loss及Retinane.mp4 62.20M
235-RetinaNet-05-Retinanet核心知识归纳.mp4 42.38M
236-RetinaNet-06-训练.mp4 69.27M
237-RetinaNet-07-训练数据格式转化生成.mp4 59.00M
238-RetinaNet-08-Dataset和Dataloader.mp4 48.47M
239-RetinaNet-09-网络结构.mp4 60.00M
24-swin02- 论文简介.mp4 76.60M
240-RetinaNet-10-Anchors的生成.mp4 75.73M
241-RetinaNet-11-Focal Loss代码.mp4 27.90M
242-CenterNet-01-背景介绍.mp4 46.22M
243-CenterNet-02-摘要.mp4 49.29M
244-CenterNet-03-模型结构.mp4 69.46M
245-CenterNet-04-模型结构2.mp4 68.05M
246-CenterNet-05-实验设置.mp4 73.48M
247-CenterNet-06-实验结论和总结.mp4 85.55M
248-CenterNet-07-训练数据和参数设置.mp4 43.36M
249-CenterNet-08-网络主体结构.mp4 48.71M
25-swin03- 论文详解-金字塔结构.mp4 37.66M
250-CenterNet-09-解码与预测.mp4 62.11M
251-FaceNet-01-研究意义背景介绍.mp4 28.51M
252-FaceNet-02-摘要.mp4 37.32M
253-FaceNet-03-介绍.mp4 76.86M
254-FaceNet-04-相关工作和总结预告.mp4 49.13M
255-FaceNet-05-TripletLoss讲解与推导.mp4 54.84M
256-FaceNet-06-TripletSelection讲解.mp4 81.87M
257-FaceNet-07-网络架构.mp4 113.23M
258-FaceNet-08-实验part1.mp4 89.77M
259-FaceNet-09-实验part2.mp4 79.78M
26-swin04- 论文详解-wmsa.mp4 33.04M
260-FaceNet-10-实验part3.mp4 37.47M
261-FaceNet-11-实验part4_总结.mp4 68.62M
262-Center Loss-01-研究成果以及意义.mp4 42.25M
263-Center Loss-02-摘要以及介绍.mp4 109.57M
264-Center Loss-03-相关工作以及总结预告.mp4 19.15M
265-Center Loss-04-推导part1.mp4 74.02M
266-Center Loss-05-实验讲解part1.mp4 66.50M
267-Center Loss-06-推导part2.mp4 78.96M
268-Center Loss-07-实验讲解part2.mp4 88.05M
269-Center Loss-08-结果分析以及总结.mp4 90.74M
27-swin05- 论文详解-sw-msa.mp4 42.83M
270-SphereFace-01-研究背景成果意义.mp4 26.83M
271-SphereFace-02-摘要和介绍.mp4 152.02M
272-SphereFace-03-相关工作.mp4 44.24M
273-SphereFace-04-ASotfmax详解_part1.mp4 122.79M
274-SphereFace-05-ASoftmax详解_part2.mp4 76.62M
275-SphereFace-06-Asoftmax详解_part3.mp4 43.92M
276-SphereFace-07-实验代码讲解.mp4 65.28M
277-SphereFace-08-结果分析与总.mp4 130.29M
278-FCN-01-语意分割简介.mp4 54.96M
279-FCN-02-常用数据集、指标、研究成果.mp4 48.19M
28-swin06- 论文详解-实验结果.mp4 35.49M
280-FCN-03-论文摘要精读.mp4 76.90M
281-FCN-04-论文引言、全局信息及部分信息.mp4 115.09M
282-FCN-05-感受域&平移不变性.mp4 104.54M
283-FCN-06-经典算法&本文算法、上采样.mp4 50.48M
284-FCN-07-算法架构.mp4 100.26M
285-FCN-08-训练技巧&实验结果及分析.mp4 127.20M
286-FCN-09-讨论&总结.mp4 30.32M
287-FCN-10-代码实现.mp4 50.95M
288-FCN-11-数据预处理.mp4 117.71M
289-FCN-12-模型搭建.mp4 129.99M
29-swin07- 论文详解-拓展1.mp4 61.35M
290-FCN-13-训练、验证&预测函数搭建.mp4 89.66M
291-FCN-14-损失函数.mp4 77.42M
292-FCN-15-指标计算.mp4 111.09M
293-01-Unet-论文总览和摘要精读.mp4 90.78M
294-02-Unet-医学分割相关背景和取得的成果及意义.mp4 89.21M
295-03-Unet-两篇论文相互补充.mp4 76.04M
296-04-Unet-回顾医学图像分析及CNN的发展历程.mp4 158.93M
297-05-Unet-先验知识补充.mp4 59.65M
298-06-Unet-算法架构和实验结果及分析.mp4 87.52M
299-07-Unet-试验设置及结果分析.mp4 39.21M
3-【先导课】效率提高3倍的论文阅读方法.mp4 78.42M
30-swin08- 论文详解-拓展2.mp4 52.66M
300-08-Unet-代码精读.mp4 144.35M
301-DeepLab-01论文背景、研究成果及意义.mp4 62.46M
302-DeepLab-02-摘要.mp4 109.52M
303-DeepLab-03-v1论文精读.mp4 85.94M
304-DeepLab-04-v1论文精读2.mp4 83.34M
305-DeepLab-05-v1-论文精读3总结.mp4 45.77M
306-DeepLab-06-v2论文精读1.mp4 178.52M
307-DeepLab-07-v2-论文精读2.mp4 108.98M
308-DeepLab-08-v2论文精读3总结.mp4 67.08M
309-DeepLab-09-v3论文精读1.mp4 137.14M
31-swin09-代码-前言.mp4 11.74M
310-DeepLab-10-v3-算法及实验部分.mp4 134.87M
311-DeepLab-11-论文精讲v3+.mp4 66.46M
312-DeepLab-12-v3+深度可分离卷积.mp4 96.41M
313-DeepLab-13-v3+算法和实验、论文总结.mp4 65.54M
314-DeepLab-14-代码复现.mp4 97.48M
315-DeepLab-15-算法架构.mp4 136.21M
316-BiSeNet-01-分割常用损失函数(上).mp4 78.56M
317-BiSeNet-02-分割常用损失函数(中).mp4 66.14M
318-BiSeNet-03-分割常用损失函数(下)&分类器评价标准.mp4 91.93M
319-BiSeNet-04-引言.mp4 110.64M
32-swin10-代码-参数.mp4 36.93M
320-BiSeNet-05-相关工作&算法架构总览.mp4 79.02M
321-BiSeNet-06-算法结构详解&实验.mp4 137.55M
322-BiSeNet-07-模型代码定义.mp4 106.25M
323-BiSeNet-08-cityscapes数据集.mp4 114.50M
324-gcn-01-研究背景.mp4 34.00M
325-gcn-02-gcn模型简介.mp4 29.10M
326-gcn-03-研究成果研究意义.mp4 35.66M
327-gcn-04-模型总览.mp4 34.46M
328-gcn-05-RGCN模型简介.mp4 70.95M
329-gcn-06-拉普拉斯矩阵.mp4 27.50M
33-swin11-代码-swin大框架.mp4 45.71M
330-gcn-07-图的频域变换.mp4 30.16M
331-gcn-08-Chebyshev卷积核.mp4 28.16M
332-gcn-09-gcn频域公式推导.mp4 54.87M
333-gcn-10-实验分析.mp4 58.36M
334-gcn-11-论文总结.mp4 38.22M
335-gcn-12-代码介绍.mp4 32.91M
336-gcn-13-读图预处理.mp4 45.21M
337-gcn-14-gcn模型实现及代码总结.mp4 38.89M
338-gat-01-研究背景.mp4 36.88M
339-gat-02-图卷积消息传递.mp4 35.09M
34-swin12-代码-basic_layer.mp4 44.19M
340-gat-03-研究成果研究意义.mp4 39.38M
341-gat-04-gnn核心框架.mp4 91.76M
342-gat-05-gat算法讲解.mp4 55.88M
343-gat-06-各种attention总结.mp4 51.43M
344-gat-07-multi-head起源简介.mp4 29.19M
345-gat-08-GAT算法总结和实验设置.mp4 133.98M
346-gat-09-论文总结.mp4 53.38M
347-gat-10-代码介绍.mp4 73.24M
348-gat-11-代码设置参数&读图.mp4 71.15M
349-gat-12-邻接矩阵归一化.mp4 46.91M
35-swin13-代码-block详解-wmsa&相对位置编码.mp4 104.09M
350-gat-13-gat模型实现.mp4 87.82M
351-gat-14-gat模型训练及代码总结.mp4 53.94M
352-node2vec-01-研究背景.mp4 20.25M
353-node2vec-02-研究成果.mp4 31.10M
354-node2vec-03-图的应用.mp4 31.59M
355-node2vec-04-模型结构&BFS&DFS.mp4 79.10M
356-node2vec-05-模型算法&alias算法.mp4 125.54M
357-node2vec-06-实验分析.mp4 101.10M
358-node2vec-07-论文总结.mp4 59.65M
359-node2vec-08-代码整体介绍.mp4 69.57M
36-swin14-代码-swmsa.mp4 40.55M
360-node2vec-09-代码节点和边的alias实现.mp4 71.33M
361-node2vec-10-代码有偏随机游走和模型训练.mp4 30.71M
362-node2vec-11-代码结果展示和总结.mp4 15.42M
363-metapath2vec-01-研究背景.mp4 40.78M
364-metapath2vec-02-研究成果.mp4 65.62M
365-metapath2vec-03-异质网络skip2gram.mp4 55.27M
366-metapath2vec-04-算法细节.mp4 84.52M
367-metapath2vec-05-实验分析.mp4 90.42M
368-metapath2vec-06-论文总结.mp4 50.86M
369-metapath2vec-07-代码dgl平台介绍.mp4 29.39M
37-swin15-代码-swin代码整体回顾.mp4 38.97M
370-metapath2vec-08-代码生成meta-path训练集.mp4 82.12M
371-metapath2vec-09-代码模型实现.mp4 73.63M
372-metapath2vec-10-代码模型训练.mp4 70.25M
373-3DMM-01-摘要.mp4 70.32M
374-3DMM-02-介绍.mp4 78.60M
375-3DMM-03-相关工作.mp4 126.44M
376-3DMM-04-算法详细讲解_part1.mp4 112.24M
377-3DMM-05-算法详细讲解_part2.mp4 111.74M
378-3DMM-06-算法详细讲解_part3.mp4 103.83M
379-3DMM-07-实验一_part1.mp4 59.02M
38-swin16-代码-dwconv与wmsa.mp4 25.23M
380-3DMM-08-实验一_part2.mp4 59.47M
381-3DMM-09-实验一_part3.mp4 62.30M
382-3DMM-10-实验一_part4.mp4 41.87M
383-3DMM-11-实验二_part1.mp4 62.84M
384-3DMM-12-实验二_part2.mp4 82.92M
385-3DMM-13-实验二_part3.mp4 55.92M
386-3DMM-14-实验二_part4.mp4 83.10M
387-3DDFA-01-摘要.mp4 42.71M
388-3DDFA-02-介绍.mp4 153.28M
389-3DDFA-03-相关工作.mp4 175.29M
39-swin17-代码-总结.mp4 7.98M
390-3DDFA-04-算法详细讲解part1.mp4 117.95M
391-3DDFA-05-算法详细讲解part2.mp4 105.86M
392-3DDFA-06-算法详细讲解part2.mp4 106.16M
393-3DDFA-07-算法详细讲解part4.mp4 112.68M
394-3DDFA-08-算法详细讲解part5.mp4 89.99M
395-3DDFA-09-实验讲解part1.mp4 115.66M
396-3DDFA-10-实验讲解part2.mp4 76.34M
397-3DDFA-11-实验讲解part3.mp4 74.78M
398-3DDFA-12-结果分析与总结.mp4 104.96M
399-MVSNet-01-论文泛读part1.mp4 130.66M
4-01-CV Transformer-Vit-前言.mp4 8.04M
40-DETR-01-论文讲解.mp4 86.10M
400-MVSNet-02-论文泛读part2.mp4 35.82M
401-MVSNet-03-算法详解part1.mp4 127.25M
402-MVSNet-04-算法i详解part2.mp4 107.14M
403-MVSNet-05-实验讲解part1.mp4 140.71M
404-MVSNet-06-实验讲解part2.mp4 46.30M
405-squeezenet-01-研究背景&成果&意义.mp4 32.03M
406-squeezenet-02-结构&泛读.mp4 69.84M
407-squeezenet-03-cnn结构设计策略&Fire Mod.mp4 70.80M
408-squeezenet-04-网络架构及细节&试验结果及分析.mp4 99.49M
409-squeezenet-05-模型预处理、加载.mp4 67.45M
41-DETR-02-论文讲解.mp4 89.03M
410-squeezenet-06-模型结构构造.mp4 34.90M
411-squeezenet-07-模型评估.mp4 46.73M
412-knowledge distillation-01-论文泛读.mp4 50.45M
413-knowledge distillation-02-集成模型思想.mp4 25.62M
414-knowledge distillation-03-知识蒸馏思想.mp4 72.57M
415-knowledge distillation-04-专家集成模型.mp4 25.51M
416-knowledge distillation-05-项目代码总览.mp4 33.03M
417-knowledge distillation-06-网络构建.mp4 41.40M
418-knowledge distillation-07-网络训练.mp4 42.14M
419-knowledge distillation-08-知识蒸馏训练.mp4 39.85M
42-DETR-03-论文讲解.mp4 124.08M
420-a-t-01-研究背景&成果&摘要.mp4 50.70M
421-a-t-02-计算机视觉中的注意力转移.mp4 46.69M
422-a-t-03-基于激活&梯度&的注意力图&实验.mp4 41.56M
423-a-t-04-搭建训练教师模型&学生模型.mp4 59.31M
424-a-t-05-实现基于激活&梯度注意力图&训练学生.mp4 80.45M
425-elmo-01-elmo的下游任务介绍.mp4 39.27M
426-elmo-02-feature_based和fine_tunin.mp4 37.94M
427-elmo-03-word2vec和charcnn回顾.mp4 38.48M
428-elmo-04-Bidirectional_language.mp4 35.95M
429-elmo-05-how to use emol.mp4 33.62M
43-DETR-04-代码讲解.mp4 137.23M
430-elmo-06-论文回顾.mp4 83.03M
431-elmo-07-代码预处理部分.mp4 85.44M
432-elmo-08-代码模型结构部分.mp4 103.18M
433-elmo-09-代码crf流程.mp4 45.98M
434-elmo-10-代码crf实现.mp4 137.03M
435-bert-01-bert的背景和glue benchmark.mp4 41.35M
436-bert-02-论文导读和bert 衍生模型.mp4 46.43M
437-bert-03-bert、gtp、elmo的比较.mp4 29.12M
438-bert-04-model和pre-training部分.mp4 48.61M
439-bert-05-bert的fine-tuning部分.mp4 35.75M
44-DETR-05-代码讲解.mp4 159.07M
440-bert-06-代码fine-tuning数据预处理和model.mp4 50.18M
441-bert-07-代码fine-tuning训练部分.mp4 30.63M
442-bert-08-代码bert pretrain的NSP.mp4 54.79M
443-bert-09-代码pertrain预处理.mp4 75.65M
444-bert-10-代码bert-pretrain的transfor.mp4 90.84M
445-bert-11-代码bert pretrain的loss计算.mp4 106.30M
446-xlnet-01-xlnet背景介绍.mp4 43.60M
447-xlnet-02-AR和AE的比较.mp4 58.21M
448-xlnet-03-排列lm部分.mp4 40.13M
449-xlnet-04-排列lm的mask实现.mp4 37.68M
45-DETR-06-代码讲解.mp4 163.51M
450-xlnet-05-传统lm存在的问题.mp4 27.47M
451-xlnet-06-Two Stream Self-attenti.mp4 55.06M
452-xlnet-07-xlnet论文回顾.mp4 44.03M
453-xlnet-08-代码xlnet的fine-tuning.mp4 45.28M
454-xlnet-09-代码xlnet的mask.mp4 139.48M
455-xlnet-10-代码xlnet的self attention.mp4 114.89M
456-DQN-01-论文泛读开场白.mp4 10.31M
457-DQN-02-研究背景及意义.mp4 14.38M
458-DQN-03-背景知识补充.mp4 7.43M
459-DQN-04-论文泛读.mp4 28.62M
46-Han-Attention-01-前期储备知识介绍.mp4 57.18M
460-DQN-05-泛读总结及下节预告.mp4 5.10M
461-DQN-06-论文精读开场白.mp4 5.96M
462-DQN-07-论文模型.mp4 14.37M
463-DQN-08-论文细节一 图像预处理.mp4 21.63M
464-DQN-09-论文细节二 ReplayBuffer.mp4 20.68M
465-DQN-10-论文细节三 SemiGradientMethod.mp4 18.76M
466-DQN-11-实验结果分析.mp4 19.96M
467-DQN-12-论文精读总结.mp4 9.93M
468-DQN-13-代码课整体介绍.mp4 15.11M
469-DQN-14-gym介绍.mp4 48.40M
47-Han-Attention-02-研究背景成果及意义.mp4 84.54M
470-DQN-15-图像预处理代码.mp4 44.73M
471-DQN-16-DQN核心功能实现.mp4 104.35M
472-DQN-17-代码结构及实验结果分析.mp4 38.31M
473-PPO-01-开场白.mp4 9.60M
474-PPO-02-研究背景.mp4 11.36M
475-PPO-03-论文泛读.mp4 26.19M
476-PPO-04-本节回顾下节预告.mp4 3.96M
477-PPO-05-论文精读结构介绍.mp4 4.85M
478-PPO-06-Clipped Surrogate Loss.mp4 33.55M
479-PPO-07-Adaptive KL.mp4 20.53M
48-Han-Attention-03-论文总览.mp4 108.89M
480-PPO-08-Advantage Function.mp4 20.16M
481-PPO-09-算法分析.mp4 31.01M
482-PPO-10-实验结果分析.mp4 23.67M
483-PPO-11-本节回顾下节预告.mp4 5.76M
484-PPO-12-代码部分结构.mp4 11.95M
485-PPO-13-计算Loss Function.mp4 57.49M
486-PPO-14-拓展到连续型action空间.mp4 33.12M
487-PPO-15-代码结构.mp4 34.62M
488-PPO-16-代码运行结果.mp4 29.65M
489-PPO-17-算法之外的技巧.mp4 38.75M
49-Han-Attention-04-模型详解.mp4 72.90M
490-DDPG-01-开场白.mp4 8.54M
491-DDPG-02-研究背景成果和意义.mp4 4.04M
492-DDPG-03-背景知识补充.mp4 3.04M
493-DDPG-04-论文泛读.mp4 41.53M
494-DDPG-05-本节回顾下节预告.mp4 4.21M
495-DDPG-06-论文精读结构.mp4 5.18M
496-DDPG-07-从DQN到DDPG.mp4 34.40M
497-cnn_for_re-01-前言.mp4 16.99M
498-cnn_for_re-02-论文介绍-研究背景.mp4 15.51M
499-cnn_for_re-03-论文介绍-相关工作1.mp4 27.37M
5-02-CV Transformer-Vit-论文简介.mp4 49.18M
50-Han-Attention-05-实验结果及论文总结.mp4 210.32M
500-cnn_for_re-04-论文介绍-相关工作2.mp4 55.17M
501-cnn_for_re-05-论文介绍-相关工作3.mp4 41.20M
502-cnn_for_re-06-论文泛读.mp4 44.62M
503-cnn_for_re-07-论文精读1.mp4 44.93M
504-cnn_for_re-08-论文精读2.mp4 50.44M
505-cnn_for_re-09-论文精读3.mp4 30.70M
506-cnn_for_re-10-实验结果分析.mp4 13.16M
507-cnn_for_re-11-论文总结.mp4 15.95M
508-cnn_for_re-12-代码讲解1.mp4 20.03M
509-cnn_for_re-13-代码讲解2.mp4 58.01M
51-Han-Attention-06-数据读取.mp4 91.69M
510-cnn_for_re-14-代码讲解3.mp4 33.82M
511-cnn_for_re-15-代码讲解4.mp4 41.65M
512-cnn_for_re-16-代码讲解5.mp4 63.86M
513-cnn_for_re-17-代码讲解6.mp4 80.05M
514-cnn_for_re-18-代码讲解7.mp4 52.05M
515-BiLSTM-CRF-01-论文研究背景.mp4 61.05M
516-BiLSTM-CRF_02关键算法.mp4 76.13M
517-BiLSTM-CRF_03论文模型.mp4 47.88M
518-BiLSTM-CRF_04损失函数.mp4 43.63M
519-BiLSTM-CRF_05实验结果与总结.mp4 29.03M
52-Han-Attention-07-模型实现及训练和测试.mp4 100.34M
520-BiLSTM-CRF_06代码讲解.mp4 46.29M
521-BIDAF-01-背景意义.mp4 71.18M
522-BIDAF-02-相关工作+小结.mp4 55.73M
523-BIDAF-03-模型结构.mp4 60.48M
524-BIDAF-04-实验分析.mp4 39.12M
525-BIDAF-05-数据读取-jupyter.mp4 106.98M
526-BIDAF-06-数据读取-pycharm.mp4 149.01M
527-BIDAF-07-模型构建.mp4 207.51M
528-BIDAF-08-训练加预测.mp4 80.08M
529-BIDAF-09-评测指标计算.mp4 63.94M
53-LLaMa-01-论文泛读.mp4 111.10M
530-AlexNet-01-研究背景.mp4 48.95M
531-AlexNet-02- 研究成果意义.mp4 24.46M
532-AlexNet-03-论文结构.mp4 53.17M
533-AlexNet-04-结构.mp4 48.90M
534-AlexNet-05网络结构特点-20210224.mp4 45.64M
535-AlexNet-06-训练技巧.mp4 25.75M
536-AlexNet-07实验结果及分析.mp4 28.81M
537-AlexNet-08-论文总结.mp4 24.33M
538-AlexNet-09-准备工作&代码结构.mp4 40.60M
539-AlexNet-10-代码结构2.mp4 73.43M
54-LLaMa-02-论文精读.mp4 126.90M
540-AlexNet-11-代码结构3.mp4 32.00M
541-AlexNet-12-代码结构4&训练方法v2.0.mp4 96.73M
542-ResNet-01-背景成果意义.mp4 63.82M
543-ResNet-02-论文泛读.mp4 59.35M
544-ResNet-03-残差结构.mp4 90.43M
545-ResNet-04-ResNet结构.mp4 85.21M
546-ResNet-05-论文总结.mp4 66.62M
547-ResNet-06-ResNet推理.mp4 98.94M
548-ResNet-07-结构搭建详解.mp4 101.76M
549-ResNet-08-训练及实验分析.mp4 122.81M
55-LLaMa-03-代码讲解.mp4 159.21M
550-googlenet-v4-01-背景成果意义.mp4 71.71M
551-googlenet-v4-02-论文泛读.mp4 117.69M
552-googlenet-v4-03-inception-v4.mp4 87.70M
553-googlenet-v4-04-inception-resnet.mp4 84.79M
554-googlenet-v4-05-实验结果论文总结.mp4 56.03M
555-googlenet-v4-06-inceptionv4代码（上）.mp4 85.45M
556-googlenet-v4-07-inceptionv4代码（下）.mp4 48.30M
557-googlenet-v4-08-inception-resnet.mp4 97.06M
558-ResNeXt-01-背景意义成果.mp4 58.25M
559-ResNeXt-02-论文泛读.mp4 86.61M
56-GLM-130B-01-论文泛读.mp4 114.65M
560-ResNeXt-03-聚合变换分析.mp4 76.60M
561-ResNeXt-04-分组卷积与ResNeXt.mp4 51.05M
562-ResNeXt-05-实验结果与论文总结.mp4 64.33M
563-ResNeXt-06-ResNeXt50-inference.mp4 94.91M
564-ResNeXt-07-ResNeXt-50_32x4d-网络搭建.mp4 92.98M
565-ResNeXt-08-ResNeXt-29训练.mp4 85.96M
566-ResNeXt-09-分组卷积.mp4 35.25M
567-word2vec-01-背景知识.mp4 82.54M
568-word2vec-02-论文泛读.mp4 68.08M
569-word2vec-03-对比模型.mp4 65.72M
57-GLM-130B-02-论文精读.mp4 154.56M
570-word2vec-04-原理.mp4 41.88M
571-word2vec-05-关键技术.mp4 53.74M
572-word2vec-06-模型复杂度.mp4 26.45M
573-word2vec-07-实验结果.mp4 65.50M
574-word2vec-08-代码部分上.mp4 100.65M
575-word2vec-9-代码部分下.mp4 126.49M
576-glove-01-_背景介绍.mp4 61.66M
577-glove-02-_研究成果及意义.mp4 27.98M
578-glove-03-论文概述.mp4 170.59M
579-glove-04-模型精讲.mp4 95.06M
58-GLM-130B-03-代码讲解.mp4 213.66M
580-glove-05-实验分析.mp4 47.58M
581-glove-06-数据处理.mp4 49.55M
582-glove-07-型及训练测试.mp4 52.55M
59-Self-Instruct-01-论文泛读.mp4 127.53M
6-03-CV Transformer-Vit-vit整体结构.mp4 24.22M
60-Self-Instruct-02-论文精读.mp4 235.81M
61-Self-Instruct-03-代码讲解.mp4 161.14M
62-CLIP-01-前言.mp4 58.54M
63-CLIP-02-background.mp4 60.67M
64-CLIP-03-model01.mp4 68.29M
65-CLIP-04-model02.mp4 99.95M
66-CLIP-05-experiement.mp4 53.73M
67-CLIP-06-code.mp4 122.17M
68-m4c-01-摘要.mp4 38.05M
69-m4c-02-intro(1).mp4 31.30M
7-04-CV Transformer-attention机制.mp4 25.51M
70-m4c-03-intro(2).mp4 33.71M
71-m4c-04-related泛读扩展.mp4 81.20M
72-m4c-05-论文精读.mp4 166.40M
73-m4c-06-论文精读.mp4 60.31M
74-m4c-07-代码.mp4 54.88M
75-BLIP-01-泛读mp4.mp4 60.19M
76-BLIP-02-精读01.mp4 88.12M
77-BLIP-03-精读02.mp4 93.16M
78-BLIP-04-代码讲解.mp4 68.77M
79-LLAVA-01-泛读.mp4 114.55M
8-05-vit-multihead attention.mp4 73.33M
80-LLAVA-02-数据.mp4 75.26M
81-LLAVA-03-训练实验.mp4 147.55M
82-AIGC基础知识-01.mp4 65.96M
83-AIGC基础知识-02.mp4 39.71M
84-Latent Diffusion-03-论文摘要.mp4 7.85M
85-Latent Diffusion-04-研究背景.mp4 21.09M
86-Latent Diffusion-05-扩散模型.mp4 29.69M
87-Latent Diffusion-06-模型结构.mp4 20.36M
88-Latent Diffusion-07-实验.mp4 24.68M
89-Latent Diffusion-08-结论和展望.mp4 5.25M
9-06-CV Transformer-Vit-输入端适配.mp4 49.47M
90-Latent Diffusion-09-代码讲解(1).mp4 47.17M
91-Latent Diffusion-10-代码讲解(2).mp4 21.53M
92-Latent Diffusion-11-代码讲解(3).mp4 25.93M
93-Latent Diffusion-12-代码讲解(4).mp4 64.48M
94-Latent Diffusion-13-代码讲解(5).mp4 80.53M
95-Latent Diffusion-14-代码讲解(6).mp4 17.12M
96-IMAGEBIND-01-论文摘要.mp4 14.93M
97-IMAGEBIND-02-研究背景.mp4 47.88M
98-IMAGEBIND-03-数据集.mp4 19.12M
99-IMAGEBIND-04-方法.mp4 36.35M
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