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0
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688.4 KB
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1. Classical Machine Learning Introduction.mp4
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MP4
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32 MB
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1. Introduction to Computer Vision with Deep Learning.mp4
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MP4
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43 MB
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1. Introduction to NLP.mp4
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MP4
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22.6 MB
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1. Jupyter Notebook Introduction.mp4
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MP4
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103.1 MB
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1. Machine Learning Process Introduction.mp4
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MP4
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38 MB
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1. Overview of Image Classification using CNNs.mp4
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MP4
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44.1 MB
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1. Python Introduction Part 1.mp4
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MP4
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33.6 MB
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1. Transfer Learning Introduction.mp4
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MP4
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57.4 MB
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1. What is Neuron.mp4
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MP4
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20.9 MB
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1
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978.9 KB
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1. What is Convolutional Neural Network.mp4
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MP4
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64.2 MB
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1. What is Overfitting.mp4
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MP4
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42.8 MB
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1.1 python-for-deep-learning-and-ai.zip
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ZIP
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74.7 MB
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2
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767.2 KB
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3
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713.5 KB
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4
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939.7 KB
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5
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683.9 KB
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10. CNN Parameter Calculations Part 2.mp4
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MP4
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43.2 MB
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10. Code Along in Python Part 1.mp4
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MP4
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34.4 MB
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10. Data Visualization Part 2.mp4
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MP4
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107.3 MB
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10. Deep Learning Tools.mp4
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MP4
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31.8 MB
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10. LeNet-5 Architecture Explained.mp4
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MP4
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71.1 MB
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10. Pair Plot.mp4
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MP4
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41.9 MB
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10. Seaborn Introduction Part 2.mp4
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MP4
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59.7 MB
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10. TensorFlow TFDS and Cats vs Dogs Data Download.mp4
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MP4
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42.4 MB
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10. Train Model with TFDS Data Without Saving Locally Part 2.mp4
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MP4
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38.5 MB
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11. AlexNet Architecture Explained.mp4
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MP4
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98.7 MB
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11. CNN Parameter Calculations Part 3.mp4
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MP4
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61.4 MB
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11. Code Along in Python Part 2.mp4
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MP4
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59.1 MB
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11. Data Preprocessing.mp4
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MP4
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36.4 MB
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11. MLops with AWS.mp4
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MP4
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21.6 MB
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11. Store Data in Local Directory.mp4
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MP4
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53.1 MB
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11. Train Test Split.mp4
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MP4
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8.7 MB
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11. import VGG16 from Keras.mp4
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MP4
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51 MB
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12. Code Along in Python Part 3.mp4
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MP4
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44.1 MB
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12. Data Augmentation for Training.mp4
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MP4
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25.6 MB
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12. GoogLeNet (Inception V1) Architecture Explained.mp4
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MP4
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68.4 MB
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12. Import Neural Networks APIs.mp4
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MP4
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37.1 MB
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12. Load Dataset for Baseline Classifier.mp4
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MP4
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82.9 MB
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12. Model Training.mp4
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MP4
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58.9 MB
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12. TF-IDF Vectorization.mp4
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MP4
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34.7 MB
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13. Building Baseline CNN Classifier.mp4
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MP4
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41.6 MB
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13. Code Along in Python Part 4.mp4
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MP4
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66.7 MB
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13. How to Get Input Shape and Class Weights.mp4
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MP4
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21.2 MB
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13. Make CNN Model with VGG16 Transfer Learning.mp4
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MP4
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63.9 MB
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13. Model Evaluation and Prediction on Real Data.mp4
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MP4
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22.3 MB
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13. Model Load and Save.mp4
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MP4
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32.1 MB
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13. RestNet Architecture Explained.mp4
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MP4
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56.8 MB
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14. How to Calculate Size of Output Layers of CNN and MaxPool.mp4
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MP4
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61.3 MB
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14. Image Class Prediction.mp4
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MP4
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52.3 MB
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14. MobileNet Architecture Explained.mp4
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MP4
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121.3 MB
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14. Model Load and Store.mp4
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MP4
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22.1 MB
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14. Model Training for Better Accuracy.mp4
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MP4
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23.3 MB
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14. Neural Network Model Building.mp4
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MP4
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60.9 MB
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15. EfficientNet Architecture Explained.mp4
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MP4
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104.3 MB
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15. How to Calculate Number of Parameters in CNN and FCN.mp4
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MP4
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68.7 MB
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15. Model Summary Explanation.mp4
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MP4
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48.8 MB
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15. Train Any Model for Transfer Learning.mp4
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MP4
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63.3 MB
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16. Model Training and Layers Analysis.mp4
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MP4
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39.9 MB
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16. Model Training.mp4
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MP4
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56.3 MB
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16. Save and Load Model with Class Names.mp4
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MP4
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40.4 MB
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17. Model Evaluation.mp4
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MP4
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16.1 MB
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17. Model Training and Validation Accuracy Plot.mp4
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MP4
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26 MB
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17. Online Prediction of Flowers Classes.mp4
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MP4
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96.9 MB
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18. Building Dataset for Regularized CNN.mp4
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MP4
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17.5 MB
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18. Model Save and Load.mp4
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MP4
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23.6 MB
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19. Prediction on Real-Life Data.mp4
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MP4
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50.9 MB
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19. Regularized CNN Model Building and Training.mp4
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MP4
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42.4 MB
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2. 5 Steps of Computer Vision Model Building.mp4
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MP4
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27.7 MB
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2. Introduction to TensorFlow Datasets (TFDS).mp4
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MP4
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74.6 MB
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2. L1, L2 and Early Stopping Regularization.mp4
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MP4
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44.8 MB
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2. Load Flowers Dataset for Classification.mp4
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MP4
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68 MB
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2. Logistic Regression.mp4
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MP4
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34.4 MB
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2. Multi-Layer Perceptron.mp4
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MP4
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55.1 MB
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2. Python Introduction Part 2.mp4
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MP4
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37.8 MB
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2. Types of Machine Learning.mp4
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MP4
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19.3 MB
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2. What are Key NLP Techniques.mp4
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MP4
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39.6 MB
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2. Working Principle of CNN.mp4
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MP4
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80.2 MB
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20. Training Log Analysis.mp4
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MP4
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25.5 MB
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21. Load Model and Do the Prediction.mp4
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MP4
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83.4 MB
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22. CNN Model Visualization.mp4
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MP4
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14.3 MB
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3. Convolutional Filters.mp4
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MP4
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114 MB
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3. Download Flowers Data.mp4
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MP4
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50 MB
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3. Download Humans or Horses Dataset Part 1.mp4
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MP4
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56.2 MB
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3. Fashion MNIST Dataset Download.mp4
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MP4
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63.2 MB
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3. How Dropout and Batch Normalization Prevents Overfitting.mp4
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MP4
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42.7 MB
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3. Overview of NLP Tools.mp4
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MP4
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64.5 MB
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3. Python Introduction Part 3.mp4
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MP4
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34.7 MB
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3. Shallow vs Deep Neural Networks.mp4
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MP4
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13.8 MB
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3. Supervised Learning.mp4
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MP4
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25.5 MB
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3. Support Vector Machine - SVM.mp4
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MP4
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37.7 MB
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4. Activation Function.mp4
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MP4
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40.3 MB
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4. Common Challenges in NLP.mp4
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MP4
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19.1 MB
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4. Decision Tree.mp4
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MP4
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25.5 MB
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4. Download Humans or Horses Dataset Part 2.mp4
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MP4
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76 MB
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4. Fashion MNIST Dataset Analysis.mp4
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MP4
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87.8 MB
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4. Feature Maps.mp4
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MP4
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66.9 MB
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4. Flowers Data Visualization.mp4
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MP4
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48.7 MB
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4. Numpy Introduction Part 1.mp4
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MP4
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40.1 MB
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4. Unsupervised Learning.mp4
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MP4
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43.2 MB
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4. What is Data Augmentation [Theory].mp4
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MP4
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48.6 MB
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5. Bag of Words - The Simples Word Embedding Technique.mp4
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MP4
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27.3 MB
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5. Numpy Introduction Part 2.mp4
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MP4
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36.3 MB
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5. Padding and Strides.mp4
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MP4
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102.3 MB
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5. Preparing Data with Image Data Generator.mp4
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MP4
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51.2 MB
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5. Random Forest.mp4
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MP4
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17.5 MB
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5. Reinforcement Learning.mp4
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MP4
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16.3 MB
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5. Sample Data Load with ImageDataGenerator for Augmentation.mp4
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MP4
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71 MB
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5. Train Test Split for Data.mp4
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MP4
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25.8 MB
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5. Use of Image Data Generator.mp4
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MP4
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73.4 MB
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5. What is Back Propagation.mp4
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MP4
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79.4 MB
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6. Baseline CNN Model Building.mp4
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MP4
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46.4 MB
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6. Data Display in Subplots Matrix.mp4
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MP4
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89.2 MB
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6. L2 Regularization.mp4
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MP4
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38.3 MB
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6. Optimizers in Deep Learning.mp4
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MP4
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52.1 MB
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6. Pandas Introduction.mp4
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MP4
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49.6 MB
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6
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287.6 KB
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6. Deep Neural Network Model Building.mp4
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MP4
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36.5 MB
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6. Pooling Layers.mp4
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MP4
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86.5 MB
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6. Random Rotation Augmentation.mp4
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MP4
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55.8 MB
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6. Term Frequency - Inverse Document Frequency (TF-IDF).mp4
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MP4
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20 MB
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6. What is Deep Learning and ML.mp4
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MP4
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30.2 MB
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7. Activation Function.mp4
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MP4
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72.7 MB
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7. CNN Introduction.mp4
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MP4
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53 MB
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7. L1 Regularization.mp4
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MP4
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18.7 MB
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7. Load Spam Dataset.mp4
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MP4
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18.6 MB
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7. Matplotlib Introduction Part 1.mp4
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MP4
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64.8 MB
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7. Model Summary and Training.mp4
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MP4
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65 MB
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7. Steps to Build Neural Network.mp4
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MP4
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64.1 MB
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7
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75.5 KB
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7. How to Calculate Number of Parameters in CNN.mp4
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MP4
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63.8 MB
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7. Random Shift Augmentation.mp4
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MP4
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45.9 MB
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7. What is Neural Network.mp4
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MP4
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33.2 MB
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8. Baseline CNN Model Training.mp4
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MP4
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46.3 MB
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8. Building CNN Model.mp4
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MP4
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60.7 MB
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8. Customer Churn Dataset Loading.mp4
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MP4
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26 MB
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8. Discovering Overfitting - Early Stopping.mp4
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MP4
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77.5 MB
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8. Dropout.mp4
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MP4
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32.4 MB
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8. How Deep Learning Process Works.mp4
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MP4
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23.9 MB
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8. Matplotlib Introduction Part 2.mp4
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MP4
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70.1 MB
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8. Model Evaluation.mp4
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MP4
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31.5 MB
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8. Other Types of Data Augmentation.mp4
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MP4
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73.3 MB
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8. Text Preprocessing.mp4
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MP4
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45.8 MB
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8
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796.9 KB
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9. All Types of Augmentation at Once.mp4
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MP4
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32.7 MB
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9. Application of Deep Learning.mp4
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MP4
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28.6 MB
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9. CNN Architectures Comparison.mp4
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MP4
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55.6 MB
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9. CNN Parameter Calculation.mp4
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MP4
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44.8 MB
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9. Data Visualization Part 1.mp4
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MP4
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50.2 MB
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9
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248.5 KB
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9. Feature Engineering.mp4
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MP4
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33.7 MB
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9. Model Save and Load for Prediction.mp4
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MP4
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44.7 MB
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9. ROC-AUC Curve.mp4
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MP4
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13.5 MB
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9. Seaborn Introduction Part 1.mp4
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MP4
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30.6 MB
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9. Train Model with TFDS Data Without Saving Locally Part 1.mp4
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MP4
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41.3 MB
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TutsNode.net.txt
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TXT
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[TGx]Downloaded from torrentgalaxy.to .txt
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TXT
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112
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425.1 KB
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113
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812.3 KB
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114
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398 KB
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115
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332.2 KB
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116
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704.4 KB
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118
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28.2 KB
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119
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185.4 KB
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120
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381.2 KB
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121
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468.3 KB
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122
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470 KB
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123
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506.5 KB
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124
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55.6 KB
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125
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384.4 KB
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126
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668 KB
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127
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457.2 KB
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128
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753.1 KB
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129
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954 KB
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130
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367.5 KB
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131
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825 KB
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132
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149.6 KB
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133
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999.8 KB
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134
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731.2 KB
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135
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887 KB
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136
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265.9 KB
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137
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449.8 KB
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138
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507.3 KB
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139
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532.8 KB
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140
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747.7 KB
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141
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906.6 KB
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142
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705.5 KB
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143
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158.5 KB
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144
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554.6 KB
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