365 Data Science - Deep Learning with TensorFlow 2.0 [CoursesGhar]

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365 Data Science - Deep Learning with TensorFlow 2.0 [CoursesGhar]

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种子哈希：131f089907b4d1802e8da813f93df387333530dd
文件大小： 2.38G
已经下载：342次
下载速度：极快
收录时间：2022-04-03
最近下载：2025-09-06

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文件列表

  • 13. Business case/4. Preprocessing the data.mp4 81.2 MB
  • 14. Conclusion/3. An overview of CNNs.mp4 75.4 MB
  • 6. Deep nets overview/3. Really understand deep nets.mp4 61.0 MB
  • 2. Neural networks Intro/11. One-parameter gradient descent.mp4 59.1 MB
  • 6. Deep nets overview/7. Backpropagation.mp4 55.3 MB
  • 13. Business case/1. Exploring the dataset and identifying predictors.mp4 54.6 MB
  • 2. Neural networks Intro/12. N-parameter gradient descent.mp4 52.5 MB
  • 1. Introduction/2. What does the course cover.mp4 51.6 MB
  • 14. Conclusion/1. Summary.mp4 50.9 MB
  • 4. Minimal example/4. Training the model.mp4 48.8 MB
  • 13. Business case/3. Balancing the dataset.mp4 46.8 MB
  • 13. Business case/7. Setting an early stopping mechanism.mp4 44.7 MB
  • 2. Neural networks Intro/1. Introduction to neural networks.mp4 44.6 MB
  • 2. Neural networks Intro/6. The linear model. Multiple inputs and multiple outputs.mp4 44.3 MB
  • 14. Conclusion/5. Non-NN approaches.mp4 43.9 MB
  • 5. Introduction to TensorFlow 2/1. TensorFlow outline.mp4 43.2 MB
  • 12. Deeper example/8. Learning.mp4 42.9 MB
  • 2. Neural networks Intro/3. Types of machine learning.mp4 42.8 MB
  • 11. Preprocessing/3. Standardization.mp4 42.3 MB
  • 6. Deep nets overview/4. Why do we need non-linearities.mp4 39.8 MB
  • 1. Introduction/1. Welcome to Machine Learning.mp4 39.8 MB
  • 5. Introduction to TensorFlow 2/2. TensorFlow 2 intro.mp4 39.7 MB
  • 8. Overfitting/3. Train vs validation.mp4 39.3 MB
  • 10. Optimizers/4. Learning rate schedules.mp4 38.9 MB
  • 12. Deeper example/5. Preprocess the data - shuffle and batch the data.mp4 38.4 MB
  • 3. Setting up the environment/2. Why Python and why Jupyter.mp4 36.4 MB
  • 10. Optimizers/1. SGD_Batching.mp4 36.2 MB
  • 8. Overfitting/1. Underfitting and overfitting.mp4 35.7 MB
  • 2. Neural networks Intro/10. Cross-entropy loss.mp4 35.0 MB
  • 12. Deeper example/2. How to tackle the MNIST.mp4 34.9 MB
  • 5. Introduction to TensorFlow 2/5. Model layout - inputs, outputs, targets, weights, biases, optimizer and loss.mp4 34.5 MB
  • 6. Deep nets overview/2. What is a deep net.mp4 34.2 MB
  • 8. Overfitting/2. Underfitting and overfitting. A classification example.mp4 34.1 MB
  • 11. Preprocessing/5. One-hot vs binary.mp4 33.8 MB
  • 5. Introduction to TensorFlow 2/6. Interpreting the result and extracting the weights and bias.mp4 32.9 MB
  • 3. Setting up the environment/3. Installing Anaconda.mp4 32.9 MB
  • 8. Overfitting/4. Train vs validation vs test.mp4 32.8 MB
  • 10. Optimizers/6. Adaptive learning schedules.mp4 31.3 MB
  • 6. Deep nets overview/5. Activation functions.mp4 30.6 MB
  • 10. Optimizers/7. Adaptive moment estimation.mp4 30.5 MB
  • 8. Overfitting/6. Early stopping - motivation and types.mp4 29.7 MB
  • 14. Conclusion/4. An overview of RNNs.mp4 28.7 MB
  • 12. Deeper example/6. Outline the model.mp4 28.7 MB
  • 12. Deeper example/4. Preprocess the data - create a validation dataset and scale the data.mp4 28.4 MB
  • 12. Deeper example/9. Testing the model.mp4 28.2 MB
  • 2. Neural networks Intro/2. Training the model.mp4 28.1 MB
  • 13. Business case/6. Learning and interpreting the result.mp4 27.7 MB
  • 9. Initialization/1. Initializaiton.mp4 27.4 MB
  • 2. Neural networks Intro/4. The linear model.mp4 27.3 MB
  • 8. Overfitting/5. N-fold cross validation.mp4 26.8 MB
  • 11. Preprocessing/1. Preprocessing.mp4 26.8 MB
  • 6. Deep nets overview/6. Softmax activation.mp4 26.2 MB
  • 6. Deep nets overview/8. Backpropagation - intuition.mp4 25.6 MB
  • 4. Minimal example/2. Generating the data (optional).mp4 24.9 MB
  • 2. Neural networks Intro/5. The linear model. Multiple inputs..mp4 24.8 MB
  • 2. Neural networks Intro/7. Graphical representation.mp4 23.0 MB
  • 5. Introduction to TensorFlow 2/7. Customizing your model.mp4 22.7 MB
  • 2. Neural networks Intro/9. L2-norm loss.mp4 22.4 MB
  • 4. Minimal example/3. Initializing the variables.mp4 21.4 MB
  • 3. Setting up the environment/5. Jupyter Dashboard - Part 2.mp4 21.4 MB
  • 9. Initialization/3. Xavier_s initialization.mp4 20.1 MB
  • 10. Optimizers/3. Momentum.mp4 19.9 MB
  • 11. Preprocessing/4. Dealing with categorical data.mp4 19.1 MB
  • 13. Business case/5. Load the preprocessed data.mp4 19.1 MB
  • 2. Neural networks Intro/8. The objective function.mp4 18.6 MB
  • 14. Conclusion/2. Whats more out there.mp4 18.4 MB
  • 6. Deep nets overview/1. The layer.mp4 17.2 MB
  • 12. Deeper example/3. Importing the relevant libraries and loading the data.mp4 16.6 MB
  • 3. Setting up the environment/6. Installing TensorFlow 2.0.mp4 16.5 MB
  • 10. Optimizers/2. Local minima pitfalls.mp4 15.0 MB
  • 4. Minimal example/1. Outline.mp4 14.6 MB
  • 5. Introduction to TensorFlow 2/4. Types of file formats in TensorFlow and data handling.mp4 13.9 MB
  • 12. Deeper example/7. Select the loss and the optimizer.mp4 13.3 MB
  • 9. Initialization/2. Types of simple initializations.mp4 12.9 MB
  • 11. Preprocessing/2. Basic preprocessing.mp4 11.6 MB
  • 10. Optimizers/5. Learning rate schedules. A picture.mp4 11.5 MB
  • 13. Business case/8. Testing the model.mp4 10.1 MB
  • 13. Business case/2. Outlining the business case solution.mp4 10.0 MB
  • 3. Setting up the environment/4. Jupyter Dashboard - Part 1.mp4 9.7 MB
  • 12. Deeper example/1. The dataset.mp4 8.9 MB
  • 5. Introduction to TensorFlow 2/3. A note on coding in TensorFlow.mp4 8.5 MB
  • 3. Setting up the environment/1. Setting up the environment - Do not skip, please!.mp4 6.7 MB
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