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| 1. Convolutions & Pooling Definitions.mp4 |
45.15Мб |
| 1. Detecting Good & Bad Reviews.mp4 |
75.19Мб |
| 1. Image Processing with Keras.mp4 |
57.18Мб |
| 1. Introduction.mp4 |
43.90Мб |
| 1. Jupyter Notebook Setup.mp4 |
32.92Мб |
| 1. Machine Learning Demo.mp4 |
58.33Мб |
| 1. Wrapping Up.mp4 |
8.85Мб |
| 10. Combining Neural Networks & AI.mp4 |
36.45Мб |
| 2. Adding Convolutions & Pooling.mp4 |
10.91Мб |
| 2. Creating a 2D Image Model.mp4 |
40.75Мб |
| 2. Deep Learning, Machine Learning, & Neural Networks Resources.mp4 |
24.26Мб |
| 2. Hardware & Compiler.mp4 |
25.35Мб |
| 2. Jupyter Notebook & TensorFlow.mp4 |
49.82Мб |
| 2. Natural Language Processing.mp4 |
92.96Мб |
| 3. Diagramming Convolutions & Pooling.mp4 |
107.45Мб |
| 3. Sentiment Analysis.mp4 |
66.21Мб |
| 3. Sigmoid vs. ReLU.mp4 |
23.03Мб |
| 3. Statistics & Machine Learning.mp4 |
22.53Мб |
| 3. Tensorflow Architecture.mp4 |
76.01Мб |
| 4. Long Short Term Memory.mp4 |
80.95Мб |
| 4. Neurons & Neuron Layers.mp4 |
46.75Мб |
| 4. Plotting Uniform Data.mp4 |
32.82Мб |
| 4. Softmax.mp4 |
18.72Мб |
| 4. Visualizing Convolutional Operations.mp4 |
75.14Мб |
| 5. Deep Learning.mp4 |
40.46Мб |
| 5. Plotting Data with Noise.mp4 |
56.95Мб |
| 5. Predicting, Saving & Loading Models.mp4 |
68.04Мб |
| 5. Reinforcement Learning.mp4 |
81.51Мб |
| 5. Understanding the Model.mp4 |
54.55Мб |
| 6. Defining the Solver & Loss Function.mp4 |
67.61Мб |
| 6. Neural Network Attention.mp4 |
107.17Мб |
| 6. Neurons & Model Training.mp4 |
47.20Мб |
| 6. Predicting Data with TensorFlow.mp4 |
51.47Мб |
| 7. Bias in Training Data Sets.mp4 |
31.65Мб |
| 7. Training a Model.mp4 |
109.42Мб |
| 7. Training Models.mp4 |
25.28Мб |
| 7. Visualizing Learning Rate & Loss.mp4 |
27.65Мб |
| 8. Comparing Different Models.mp4 |
77.06Мб |
| 8. Deep Learning & AI.mp4 |
35.39Мб |
| 8. Learning Rate & Gradients.mp4 |
87.22Мб |
| 9. AI Modeling Demo.mp4 |
50.41Мб |
| 9. Learning Rate vs Steps.mp4 |
64.62Мб |
| 9. Principal Component Analysis.mp4 |
61.65Мб |