|
Обратите внимание, что наш сайт не размещает какие-либо файлы из списка. Вы не можете скачать
эти файлы или скачать torrent-файл.
|
| [TGx]Downloaded from torrentgalaxy.to .txt |
585б |
| 0 |
688.43Кб |
| 1 |
978.91Кб |
| 1.1 python-for-deep-learning-and-ai.zip |
74.67Мб |
| 1. Classical Machine Learning Introduction.mp4 |
31.98Мб |
| 1. Introduction to Computer Vision with Deep Learning.mp4 |
42.97Мб |
| 1. Introduction to NLP.mp4 |
22.55Мб |
| 1. Jupyter Notebook Introduction.mp4 |
103.08Мб |
| 1. Machine Learning Process Introduction.mp4 |
37.98Мб |
| 1. Overview of Image Classification using CNNs.mp4 |
44.06Мб |
| 1. Python Introduction Part 1.mp4 |
33.55Мб |
| 1. Transfer Learning Introduction.mp4 |
57.43Мб |
| 1. What is Convolutional Neural Network.mp4 |
64.20Мб |
| 1. What is Neuron.mp4 |
20.85Мб |
| 1. What is Overfitting.mp4 |
42.76Мб |
| 10 |
503.13Кб |
| 10. CNN Parameter Calculations Part 2.mp4 |
43.15Мб |
| 10. Code Along in Python Part 1.mp4 |
34.36Мб |
| 10. Data Visualization Part 2.mp4 |
107.25Мб |
| 10. Deep Learning Tools.mp4 |
31.78Мб |
| 10. LeNet-5 Architecture Explained.mp4 |
71.12Мб |
| 10. Pair Plot.mp4 |
41.94Мб |
| 10. Seaborn Introduction Part 2.mp4 |
59.72Мб |
| 10. TensorFlow TFDS and Cats vs Dogs Data Download.mp4 |
42.39Мб |
| 10. Train Model with TFDS Data Without Saving Locally Part 2.mp4 |
38.45Мб |
| 100 |
353.76Кб |
| 101 |
583.29Кб |
| 102 |
657.83Кб |
| 103 |
302.42Кб |
| 104 |
457.96Кб |
| 105 |
798.54Кб |
| 106 |
348.65Кб |
| 107 |
652.85Кб |
| 108 |
941.61Кб |
| 109 |
19.37Кб |
| 11 |
610.13Кб |
| 11. AlexNet Architecture Explained.mp4 |
98.72Мб |
| 11. CNN Parameter Calculations Part 3.mp4 |
61.42Мб |
| 11. Code Along in Python Part 2.mp4 |
59.15Мб |
| 11. Data Preprocessing.mp4 |
36.38Мб |
| 11. import VGG16 from Keras.mp4 |
51.02Мб |
| 11. MLops with AWS.mp4 |
21.64Мб |
| 11. Store Data in Local Directory.mp4 |
53.07Мб |
| 11. Train Test Split.mp4 |
8.74Мб |
| 110 |
227.67Кб |
| 111 |
505.41Кб |
| 112 |
425.09Кб |
| 113 |
812.29Кб |
| 114 |
398.02Кб |
| 115 |
332.16Кб |
| 116 |
704.37Кб |
| 117 |
1023.54Кб |
| 118 |
28.20Кб |
| 119 |
185.41Кб |
| 12 |
62.23Кб |
| 12. Code Along in Python Part 3.mp4 |
44.10Мб |
| 12. Data Augmentation for Training.mp4 |
25.63Мб |
| 12. GoogLeNet (Inception V1) Architecture Explained.mp4 |
68.39Мб |
| 12. Import Neural Networks APIs.mp4 |
37.05Мб |
| 12. Load Dataset for Baseline Classifier.mp4 |
82.94Мб |
| 12. Model Training.mp4 |
58.94Мб |
| 12. TF-IDF Vectorization.mp4 |
34.71Мб |
| 120 |
381.15Кб |
| 121 |
468.25Кб |
| 122 |
470.04Кб |
| 123 |
506.49Кб |
| 124 |
55.65Кб |
| 125 |
384.36Кб |
| 126 |
668.00Кб |
| 127 |
457.20Кб |
| 128 |
753.06Кб |
| 129 |
953.96Кб |
| 13 |
811.88Кб |
| 13. Building Baseline CNN Classifier.mp4 |
41.62Мб |
| 13. Code Along in Python Part 4.mp4 |
66.67Мб |
| 13. How to Get Input Shape and Class Weights.mp4 |
21.19Мб |
| 13. Make CNN Model with VGG16 Transfer Learning.mp4 |
63.92Мб |
| 13. Model Evaluation and Prediction on Real Data.mp4 |
22.26Мб |
| 13. Model Load and Save.mp4 |
32.08Мб |
| 13. RestNet Architecture Explained.mp4 |
56.84Мб |
| 130 |
367.48Кб |
| 131 |
824.96Кб |
| 132 |
149.61Кб |
| 133 |
999.75Кб |
| 134 |
731.21Кб |
| 135 |
887.02Кб |
| 136 |
265.88Кб |
| 137 |
449.80Кб |
| 138 |
507.25Кб |
| 139 |
532.77Кб |
| 14 |
613.06Кб |
| 14. How to Calculate Size of Output Layers of CNN and MaxPool.mp4 |
61.32Мб |
| 14. Image Class Prediction.mp4 |
52.31Мб |
| 14. MobileNet Architecture Explained.mp4 |
121.33Мб |
| 14. Model Load and Store.mp4 |
22.07Мб |
| 14. Model Training for Better Accuracy.mp4 |
23.35Мб |
| 14. Neural Network Model Building.mp4 |
60.87Мб |
| 140 |
747.67Кб |
| 141 |
906.56Кб |
| 142 |
705.46Кб |
| 143 |
158.50Кб |
| 144 |
554.63Кб |
| 15 |
467.27Кб |
| 15. EfficientNet Architecture Explained.mp4 |
104.30Мб |
| 15. How to Calculate Number of Parameters in CNN and FCN.mp4 |
68.68Мб |
| 15. Model Summary Explanation.mp4 |
48.80Мб |
| 15. Train Any Model for Transfer Learning.mp4 |
63.27Мб |
| 16 |
998.44Кб |
| 16. Model Training.mp4 |
56.27Мб |
| 16. Model Training and Layers Analysis.mp4 |
39.91Мб |
| 16. Save and Load Model with Class Names.mp4 |
40.38Мб |
| 17 |
338.62Кб |
| 17. Model Evaluation.mp4 |
16.11Мб |
| 17. Model Training and Validation Accuracy Plot.mp4 |
25.97Мб |
| 17. Online Prediction of Flowers Classes.mp4 |
96.93Мб |
| 18 |
362.44Кб |
| 18. Building Dataset for Regularized CNN.mp4 |
17.50Мб |
| 18. Model Save and Load.mp4 |
23.62Мб |
| 19 |
655.34Кб |
| 19. Prediction on Real-Life Data.mp4 |
50.94Мб |
| 19. Regularized CNN Model Building and Training.mp4 |
42.45Мб |
| 2 |
767.17Кб |
| 2. 5 Steps of Computer Vision Model Building.mp4 |
27.68Мб |
| 2. Introduction to TensorFlow Datasets (TFDS).mp4 |
74.65Мб |
| 2. L1, L2 and Early Stopping Regularization.mp4 |
44.80Мб |
| 2. Load Flowers Dataset for Classification.mp4 |
67.98Мб |
| 2. Logistic Regression.mp4 |
34.43Мб |
| 2. Multi-Layer Perceptron.mp4 |
55.11Мб |
| 2. Python Introduction Part 2.mp4 |
37.83Мб |
| 2. Types of Machine Learning.mp4 |
19.29Мб |
| 2. What are Key NLP Techniques.mp4 |
39.58Мб |
| 2. Working Principle of CNN.mp4 |
80.21Мб |
| 20 |
674.53Кб |
| 20. Training Log Analysis.mp4 |
25.54Мб |
| 21 |
353.11Кб |
| 21. Load Model and Do the Prediction.mp4 |
83.40Мб |
| 22 |
899.99Кб |
| 22. CNN Model Visualization.mp4 |
14.31Мб |
| 23 |
976.60Кб |
| 24 |
926.51Кб |
| 25 |
332.61Кб |
| 26 |
626.19Кб |
| 27 |
15.67Кб |
| 28 |
125.51Кб |
| 29 |
336.75Кб |
| 3 |
713.51Кб |
| 3. Convolutional Filters.mp4 |
114.04Мб |
| 3. Download Flowers Data.mp4 |
50.00Мб |
| 3. Download Humans or Horses Dataset Part 1.mp4 |
56.15Мб |
| 3. Fashion MNIST Dataset Download.mp4 |
63.20Мб |
| 3. How Dropout and Batch Normalization Prevents Overfitting.mp4 |
42.73Мб |
| 3. Overview of NLP Tools.mp4 |
64.53Мб |
| 3. Python Introduction Part 3.mp4 |
34.65Мб |
| 3. Shallow vs Deep Neural Networks.mp4 |
13.85Мб |
| 3. Supervised Learning.mp4 |
25.54Мб |
| 3. Support Vector Machine - SVM.mp4 |
37.66Мб |
| 30 |
31.49Кб |
| 31 |
244.94Кб |
| 32 |
483.78Кб |
| 33 |
823.61Кб |
| 34 |
942.61Кб |
| 35 |
86.06Кб |
| 36 |
191.58Кб |
| 37 |
744.69Кб |
| 38 |
822.22Кб |
| 39 |
588.98Кб |
| 4 |
939.67Кб |
| 4. Activation Function.mp4 |
40.33Мб |
| 4. Common Challenges in NLP.mp4 |
19.13Мб |
| 4. Decision Tree.mp4 |
25.51Мб |
| 4. Download Humans or Horses Dataset Part 2.mp4 |
76.02Мб |
| 4. Fashion MNIST Dataset Analysis.mp4 |
87.76Мб |
| 4. Feature Maps.mp4 |
66.88Мб |
| 4. Flowers Data Visualization.mp4 |
48.69Мб |
| 4. Numpy Introduction Part 1.mp4 |
40.09Мб |
| 4. Unsupervised Learning.mp4 |
43.24Мб |
| 4. What is Data Augmentation [Theory].mp4 |
48.57Мб |
| 40 |
695.22Кб |
| 41 |
130.77Кб |
| 42 |
332.76Кб |
| 43 |
285.63Кб |
| 44 |
871.73Кб |
| 45 |
62.26Кб |
| 46 |
581.51Кб |
| 47 |
161.97Кб |
| 48 |
744.31Кб |
| 49 |
868.75Кб |
| 5 |
683.91Кб |
| 5. Bag of Words - The Simples Word Embedding Technique.mp4 |
27.31Мб |
| 5. Numpy Introduction Part 2.mp4 |
36.30Мб |
| 5. Padding and Strides.mp4 |
102.33Мб |
| 5. Preparing Data with Image Data Generator.mp4 |
51.15Мб |
| 5. Random Forest.mp4 |
17.48Мб |
| 5. Reinforcement Learning.mp4 |
16.27Мб |
| 5. Sample Data Load with ImageDataGenerator for Augmentation.mp4 |
71.05Мб |
| 5. Train Test Split for Data.mp4 |
25.82Мб |
| 5. Use of Image Data Generator.mp4 |
73.36Мб |
| 5. What is Back Propagation.mp4 |
79.40Мб |
| 50 |
182.15Кб |
| 51 |
447.69Кб |
| 52 |
913.51Кб |
| 53 |
952.39Кб |
| 54 |
992.07Кб |
| 55 |
702.32Кб |
| 56 |
949.87Кб |
| 57 |
866.47Кб |
| 58 |
1007.48Кб |
| 59 |
62.14Кб |
| 6 |
287.64Кб |
| 6. Baseline CNN Model Building.mp4 |
46.36Мб |
| 6. Data Display in Subplots Matrix.mp4 |
89.22Мб |
| 6. Deep Neural Network Model Building.mp4 |
36.53Мб |
| 6. L2 Regularization.mp4 |
38.30Мб |
| 6. Optimizers in Deep Learning.mp4 |
52.07Мб |
| 6. Pandas Introduction.mp4 |
49.56Мб |
| 6. Pooling Layers.mp4 |
86.51Мб |
| 6. Random Rotation Augmentation.mp4 |
55.82Мб |
| 6. Term Frequency - Inverse Document Frequency (TF-IDF).mp4 |
20.02Мб |
| 6. What is Deep Learning and ML.mp4 |
30.21Мб |
| 60 |
803.42Кб |
| 61 |
1021.32Кб |
| 62 |
453.09Кб |
| 63 |
208.86Кб |
| 64 |
316.47Кб |
| 65 |
440.66Кб |
| 66 |
655.46Кб |
| 67 |
670.91Кб |
| 68 |
61.90Кб |
| 69 |
230.61Кб |
| 7 |
75.52Кб |
| 7. Activation Function.mp4 |
72.66Мб |
| 7. CNN Introduction.mp4 |
53.03Мб |
| 7. How to Calculate Number of Parameters in CNN.mp4 |
63.81Мб |
| 7. L1 Regularization.mp4 |
18.74Мб |
| 7. Load Spam Dataset.mp4 |
18.56Мб |
| 7. Matplotlib Introduction Part 1.mp4 |
64.76Мб |
| 7. Model Summary and Training.mp4 |
64.97Мб |
| 7. Random Shift Augmentation.mp4 |
45.94Мб |
| 7. Steps to Build Neural Network.mp4 |
64.08Мб |
| 7. What is Neural Network.mp4 |
33.22Мб |
| 70 |
208.09Кб |
| 71 |
221.80Кб |
| 72 |
322.60Кб |
| 73 |
925.30Кб |
| 74 |
963.62Кб |
| 75 |
778.35Кб |
| 76 |
868.50Кб |
| 77 |
33.95Кб |
| 78 |
244.64Кб |
| 79 |
277.51Кб |
| 8 |
796.86Кб |
| 8. Baseline CNN Model Training.mp4 |
46.34Мб |
| 8. Building CNN Model.mp4 |
60.68Мб |
| 8. Customer Churn Dataset Loading.mp4 |
26.00Мб |
| 8. Discovering Overfitting - Early Stopping.mp4 |
77.54Мб |
| 8. Dropout.mp4 |
32.36Мб |
| 8. How Deep Learning Process Works.mp4 |
23.95Мб |
| 8. Matplotlib Introduction Part 2.mp4 |
70.10Мб |
| 8. Model Evaluation.mp4 |
31.51Мб |
| 8. Other Types of Data Augmentation.mp4 |
73.34Мб |
| 8. Text Preprocessing.mp4 |
45.77Мб |
| 80 |
564.32Кб |
| 81 |
624.93Кб |
| 82 |
64.77Кб |
| 83 |
392.65Кб |
| 84 |
718.49Кб |
| 85 |
630.12Кб |
| 86 |
689.70Кб |
| 87 |
929.95Кб |
| 88 |
95.45Кб |
| 89 |
434.74Кб |
| 9 |
248.49Кб |
| 9. All Types of Augmentation at Once.mp4 |
32.66Мб |
| 9. Application of Deep Learning.mp4 |
28.61Мб |
| 9. CNN Architectures Comparison.mp4 |
55.56Мб |
| 9. CNN Parameter Calculation.mp4 |
44.78Мб |
| 9. Data Visualization Part 1.mp4 |
50.22Мб |
| 9. Feature Engineering.mp4 |
33.70Мб |
| 9. Model Save and Load for Prediction.mp4 |
44.68Мб |
| 9. ROC-AUC Curve.mp4 |
13.46Мб |
| 9. Seaborn Introduction Part 1.mp4 |
30.58Мб |
| 9. Train Model with TFDS Data Without Saving Locally Part 1.mp4 |
41.30Мб |
| 90 |
561.79Кб |
| 91 |
713.16Кб |
| 92 |
19.63Кб |
| 93 |
176.41Кб |
| 94 |
345.22Кб |
| 95 |
967.85Кб |
| 96 |
477.45Кб |
| 97 |
638.67Кб |
| 98 |
713.36Кб |
| 99 |
295.82Кб |
| TutsNode.net.txt |
63б |