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| 1.1 03+Model+building+-+Seq+vs+Functional+API.ipynb |
405.95KB |
| 1.1 cat_dog_sub.zip |
128.08MB |
| 1.1 dogs.jpg |
438.21KB |
| 1.1 FaceRecognition DeepFace.ipynb |
2.47MB |
| 1.1 Hardhat - Vest.v1i.yolov5pytorch.zip |
96.53MB |
| 1.1 video based Action_Recognition_(UCF101_Dataset)ipynb.original |
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| 1.1 YOLOv8_object_tracking.ipynb |
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| 1.1 YOLO-WORLD.ipynb |
3.58MB |
| 1.2 Fast_sam (1).ipynb |
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| 1.2 TRAIN_YOLO_V5 objectDetection_ON_vest & hardhat_DATASET.ipynb |
71.24MB |
| 1.3 FastSAM-x.pt |
138.23MB |
| 1. 74 Stable Diffusion.mp4 |
183.65MB |
| 1. Cat Dog Images Datasets.html |
30B |
| 1. CNN INTRO.mp4 |
291.55MB |
| 1. DeepFake Generation Project 12.mp4 |
173.78MB |
| 1. Face Recognition Using DeepFace Project 11.mp4 |
270.20MB |
| 1. Fast SAM (Segment Anything Model).mp4 |
126.20MB |
| 1. GANs Introduction.mp4 |
78.47MB |
| 1. Human Action Recognition Project 10.mp4 |
394.23MB |
| 1. Image Annotation Tools.mp4 |
313.72MB |
| 1. Introduction.mp4 |
20.01MB |
| 1. Intuition Neural Networks.mp4 |
48.72MB |
| 1. Keras Preprocessing Layers Intro.mp4 |
59.49MB |
| 1. LSTM GRU Introduction.mp4 |
91.21MB |
| 1. Object Detection Part start.mp4 |
4.71MB |
| 1. Sequential Vs Functional API.mp4 |
62.24MB |
| 1. YOLOV5 Hardhat & Vest object detection Project-6.mp4 |
318.02MB |
| 1. YOLOV8 object Tracking.mp4 |
81.32MB |
| 1. YOLO-WORLD demo.mp4 |
197.91MB |
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| 10. Loss functions.mp4 |
119.85MB |
| 10. SSD.mp4 |
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| 11. Performance Metrics.mp4 |
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| 2.1 05+CNN+with+CIFAR10.ipynb |
95.80KB |
| 2.1 06+Keras+Preprocessing+Layers.ipynb |
1.54MB |
| 2.1 moondream1.ipynb |
1.23MB |
| 2.1 test+&+Validation+for+Xray+Dataset.zip |
76.85MB |
| 2.1 Track and count vehicles.ipynb |
1.60MB |
| 2.2 train_Normal+for+Xray+Dataset.zip |
778.03MB |
| 2.3 train_PNEUMONIA+for+Xray+Dataset.zip |
313.92MB |
| 2. 75 clip and unet for stable diffusion.mp4 |
83.31MB |
| 2. CNN_Implementation.mp4 |
281.36MB |
| 2. GAN COMPONENTS.mp4 |
64.52MB |
| 2. Keras Preprocessing Layers Image Augmentation Code.mp4 |
157.14MB |
| 2. Moondream1.mp4 |
118.36MB |
| 2. Neural Networks.mp4 |
230.53MB |
| 2. Object Tracking & Counting Project-9.mp4 |
310.06MB |
| 2. Past Present Future Trends.mp4 |
44.98MB |
| 2. Semantic segmentation vs instance segmentation.mp4 |
75.48MB |
| 2. Sequential API code.mp4 |
279.22MB |
| 2. Xray DataSet.html |
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| 2. YOLOv8 intro.mp4 |
53.35MB |
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| 3.1 Proj_04_Weather_yolo_img_clsf_data.ipynb |
867.33KB |
| 3.2 Weather_yolo_img_clsf_data (1).zip |
498.23MB |
| 3. 76 Stable diffusion tools.mp4 |
319.74MB |
| 3. Applications.mp4 |
20.70MB |
| 3. Approach to deep learning problems.mp4 |
24.11MB |
| 3. CNN Exercise -1 Problem.mp4 |
5.02MB |
| 3. Functional API Code.mp4 |
149.70MB |
| 3. GANs Training.mp4 |
119.24MB |
| 3. Keras Preprocessing Layers Exercise-3.mp4 |
2.59MB |
| 3. Types of Segmentation.mp4 |
125.65MB |
| 3. YOLOv8 classification Project-7.mp4 |
344.93MB |
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| 4.1 config (1).yaml |
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| 4.1 Exercise+2.+Mnist+classifier+using+CNN.ipynb |
49.07KB |
| 4.1 Exercise-4+Keras+Preprocessing+Layer+cats+n+dogs.ipynb |
2.08MB |
| 4.2 Instance Segmentation yolov8.ipynb |
1.71MB |
| 4.3 Pothole segmentation yolov8 (1).zip |
49.67MB |
| 4. 77 Stable diffusion tools.mp4 |
111.96MB |
| 4. CNN Exercise -1 Solution.mp4 |
190.48MB |
| 4. GANs Applications Pros _ Cons.mp4 |
42.58MB |
| 4. Image Processing basics.mp4 |
28.23MB |
| 4. Instance segmentation using YOLOV8-seg Project -8.mp4 |
153.48MB |
| 4. Keras Preprocessing Layers Solution-3.mp4 |
57.67MB |
| 4. Lifecycle of model 5 steps.mp4 |
39.41MB |
| 4. ML problem Cost Gradient CV.mp4 |
141.62MB |
| 4. Two step object detection.mp4 |
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| 5.1 13+GAN+fashion+MNIST+Generator.ipynb |
35.35KB |
| 5.1 Pose -keypoint detection.ipynb |
1.39MB |
| 5.2 street.jpg |
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| 5.3 street2.jpg |
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| 5. 78 stable diffusion resources.mp4 |
263.87MB |
| 5. Activation Functions.mp4 |
160.94MB |
| 5. CNN Exercise -2 Problem.mp4 |
2.76MB |
| 5. Color Spaces.mp4 |
32.97MB |
| 5. GAN Implementation.mp4 |
258.47MB |
| 5. Keypoint detection using YOLOV8-pose.mp4 |
129.48MB |
| 5. RCNN Architecture.mp4 |
53.00MB |
| 5. Transfer Learning Introduction.mp4 |
126.40MB |
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| 6.1 07+Transfer+Learning+on+Cats+and+dogs+dataset.ipynb |
512.21KB |
| 6.1 Exercise+3.+Fashion-MNIST+Classifier+using+CNN.ipynb |
93.67KB |
| 6.1 image_caption_map (2).csv |
312.11KB |
| 6.1 Yolo Video run.ipynb |
835.16KB |
| 6.2 images (2).zip |
267.87MB |
| 6.2 YOLO Videos for inference.zip |
388.43MB |
| 6. 79 STABLE DIFFUSION code.mp4 |
180.35MB |
| 6. CNN Exercise -2 Solution.mp4 |
120.14MB |
| 6. Fast RCNN.mp4 |
62.80MB |
| 6. Project Image Captioning Problem-5.mp4 |
36.18MB |
| 6. Sequential Vs Functional API.mp4 |
64.27MB |
| 6. transfer learning code.mp4 |
289.17MB |
| 6. YOLO on videos.mp4 |
202.47MB |
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| 7.1 Project_Image_Captioning_with_Googles_conceptual_captions_data (1).ipynb |
2.86MB |
| 7. 80 stable diffusion UI.mp4 |
147.53MB |
| 7. Faster RCNN.mp4 |
34.00MB |
| 7. Project image captioning solution Part- 1.mp4 |
139.40MB |
| 7. Tips for Improving Model Performance.mp4 |
55.55MB |
| 7. Transfer Learning Exercise 4 -XrayDataset.mp4 |
4.61MB |
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| 8.1 04+Feed+forward+networks.ipynb |
146.97KB |
| 8.1 Exercise+5-+transfer_learning+Chest+Xray+Dataset.ipynb |
735.30KB |
| 8. 81 stable cascade.mp4 |
130.10MB |
| 8. Feed Forward Network Implementation and Keras Callbacks.mp4 |
293.27MB |
| 8. Mask RCNN.mp4 |
35.49MB |
| 8. Project image captioning solution Part- 2.mp4 |
205.16MB |
| 8. Transfer learning Exercise-4 Solution.mp4 |
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| 9. 82 forge setup.mp4 |
93.27MB |
| 9. Intro to YOLO.mp4 |
204.32MB |
| 9. Optimizers.mp4 |
231.21MB |
| 9. Project Image captioning solution Part- 3.mp4 |
127.99MB |
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| TutsNode.org.txt |
59B |