[DesireCourse.Net] Udemy - Practical Machine Learning by Example in Python

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[DesireCourse.Net] Udemy - Practical Machine Learning by Example in Python

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文件大小： 2.71G
已经下载：722次
下载速度：极快
收录时间：2021-03-14
最近下载：2024-12-31

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文件列表

  • 4. Foundations NumPy/6. Linear Regression Example.mp4 67.8 MB
  • 2. Python Quick Start/12. Classes.mp4 65.5 MB
  • 7. Foundations Pandas/2. Loading and inspecting data example.mp4 61.9 MB
  • 3. Example Logistic Regression/3. Data analysis.mp4 61.8 MB
  • 9. Example Sentiment Analysis/11. Transfer Learning Example.mp4 60.6 MB
  • 6. Example Image recognition/14. Hyperparameter tuning example.mp4 55.0 MB
  • 2. Python Quick Start/3. String formatting.mp4 54.9 MB
  • 4. Foundations NumPy/5. Introduction to Linear Regression.mp4 54.8 MB
  • 6. Example Image recognition/8. Model training.mp4 52.7 MB
  • 10. Example Fraud detection/9. Making predictions.mp4 51.8 MB
  • 3. Example Logistic Regression/8. Gradient descent.mp4 48.5 MB
  • 9. Example Sentiment Analysis/5. Data Preparation.mp4 46.5 MB
  • 8. Example Recommendations/8. Model definition.mp4 44.3 MB
  • 10. Example Fraud detection/2. Data analysis.mp4 43.8 MB
  • 3. Example Logistic Regression/12. Making predictions.mp4 42.3 MB
  • 7. Foundations Pandas/5. Sorting and transforming data example.mp4 41.8 MB
  • 9. Example Sentiment Analysis/12. Fine Tuning and Prediction.mp4 41.5 MB
  • 6. Example Image recognition/2. Data analysis.mp4 40.2 MB
  • 3. Example Logistic Regression/6. The forward function.mp4 39.7 MB
  • 8. Example Recommendations/5. Data preparation.mp4 37.9 MB
  • 1. Course Structure and Development Environment/8. Sharing Colab Notebooks.mp4 37.4 MB
  • 7. Foundations Pandas/7. Visualizing data.mp4 37.0 MB
  • 2. Python Quick Start/2. Basic Syntax.mp4 36.9 MB
  • 7. Foundations Pandas/3. Indexing and selecting data example.mp4 36.7 MB
  • 9. Example Sentiment Analysis/8. Model Training.mp4 36.5 MB
  • 2. Python Quick Start/11. Defining functions.mp4 36.4 MB
  • 7. Foundations Pandas/6. Aggregations example.mp4 35.6 MB
  • 2. Python Quick Start/13. File IO and Modules.mp4 35.4 MB
  • 4. Foundations NumPy/10. Visualizing data.mp4 35.4 MB
  • 1. Course Structure and Development Environment/2. Course Quick Tips.mp4 33.6 MB
  • 2. Python Quick Start/10. Dictionaries.mp4 33.6 MB
  • 4. Foundations NumPy/11. Images.mp4 33.3 MB
  • 6. Example Image recognition/7. Model definition.mp4 33.2 MB
  • 1. Course Structure and Development Environment/1. Course Structure and Development Environment.mp4 32.9 MB
  • 9. Example Sentiment Analysis/2. Data Analysis.mp4 32.0 MB
  • 3. Example Logistic Regression/11. Model training.mp4 31.4 MB
  • 8. Example Recommendations/12. Making predictions.mp4 31.0 MB
  • 8. Example Recommendations/9. Model training.mp4 30.8 MB
  • 8. Example Recommendations/2. Data analysis.mp4 30.5 MB
  • 9. Example Sentiment Analysis/7. Model Definition.mp4 30.4 MB
  • 5. Foundations Tensorflow/2. Model example.mp4 30.4 MB
  • 10. Example Fraud detection/4. Unsupervised learning.mp4 30.3 MB
  • 10. Example Fraud detection/11. Common questions.mp4 30.2 MB
  • 3. Example Logistic Regression/1. The problem.mp4 29.3 MB
  • 6. Example Image recognition/16. Common questions.mp4 29.2 MB
  • 9. Example Sentiment Analysis/4. Supervised Learning.mp4 29.0 MB
  • 3. Example Logistic Regression/17. Improving the model.mp4 28.4 MB
  • 1. Course Structure and Development Environment/4. Jupyter notebook Text Cells.mp4 28.4 MB
  • 6. Example Image recognition/5. Data preparation.mp4 28.3 MB
  • 8. Example Recommendations/4. Model selection.mp4 28.3 MB
  • 1. Course Structure and Development Environment/9. Artificial Intelligence, Machine Learning, and Deep Learning.mp4 26.4 MB
  • 10. Example Fraud detection/7. Model training.mp4 26.3 MB
  • 4. Foundations NumPy/2. Creating data with NumPy.mp4 26.2 MB
  • 3. Example Logistic Regression/10. Backpropagation.mp4 25.1 MB
  • 9. Example Sentiment Analysis/10. Transfer Learning with BERT.mp4 24.9 MB
  • 5. Foundations Tensorflow/5. Training example.mp4 24.7 MB
  • 2. Python Quick Start/7. Flow control.mp4 24.6 MB
  • 5. Foundations Tensorflow/12. The Three Body Problem.mp4 24.6 MB
  • 1. Course Structure and Development Environment/6. Jupyter notebook Math Markup and Magic Commands.mp4 24.5 MB
  • 6. Example Image recognition/6. CNN Model Layers.mp4 24.1 MB
  • 6. Example Image recognition/4. Model selection.mp4 23.9 MB
  • 6. Example Image recognition/13. Hyperparameter tuning.mp4 23.5 MB
  • 2. Python Quick Start/8. Lists.mp4 23.4 MB
  • 10. Example Fraud detection/1. The problem.mp4 23.4 MB
  • 3. Example Logistic Regression/5. The model.mp4 23.3 MB
  • 10. Example Fraud detection/6. Model definition.mp4 22.5 MB
  • 3. Example Logistic Regression/7. Loss and cost functions.mp4 20.8 MB
  • 2. Python Quick Start/6. Type conversion.mp4 20.6 MB
  • 8. Example Recommendations/15. Common questions.mp4 20.5 MB
  • 6. Example Image recognition/1. The problem.mp4 20.4 MB
  • 5. Foundations Tensorflow/4. Activation functions.mp4 20.3 MB
  • 5. Foundations Tensorflow/7. Loss functions.mp4 20.1 MB
  • 3. Example Logistic Regression/15. Test vs. train accuracy.mp4 19.9 MB
  • 8. Example Recommendations/13. Error analysis.mp4 19.5 MB
  • 3. Example Logistic Regression/16. Speeding up training.mp4 19.0 MB
  • 5. Foundations Tensorflow/1. About this section.mp4 19.0 MB
  • 8. Example Recommendations/7. Embedding layers.mp4 18.0 MB
  • 8. Example Recommendations/1. The problem.mp4 17.9 MB
  • 4. Foundations NumPy/9. Statistics and linear algebra.mp4 17.8 MB
  • 1. Course Structure and Development Environment/3. Introduction to Jupyter Notebook.mp4 17.7 MB
  • 10. Example Fraud detection/5. Data preparation.mp4 17.7 MB
  • 5. Foundations Tensorflow/8. Optimizers.mp4 17.2 MB
  • 2. Python Quick Start/4. Literal string interpolation.mp4 16.6 MB
  • 5. Foundations Tensorflow/11. Saving and restoring models.mp4 16.3 MB
  • 6. Example Image recognition/11. Error analysis.mp4 16.2 MB
  • 1. Course Structure and Development Environment/5. Jupyter notebook Code Cells.mp4 15.8 MB
  • 4. Foundations NumPy/3. Basic operations.mp4 15.4 MB
  • 3. Example Logistic Regression/2. Machine Learning Development Process.mp4 14.7 MB
  • 4. Foundations NumPy/8. More Complex Models.mp4 14.2 MB
  • 5. Foundations Tensorflow/3. Model layers.mp4 13.8 MB
  • 9. Example Sentiment Analysis/1. The Problem.mp4 13.8 MB
  • 6. Example Image recognition/10. Making predictions.mp4 12.9 MB
  • 11. Next steps/1. Next steps.mp4 12.2 MB
  • 4. Foundations NumPy/13. Reshaping data.mp4 11.7 MB
  • 7. Foundations Pandas/1. What is Pandas and why is it useful.mp4 9.4 MB
  • 4. Foundations NumPy/1. What is NumPy and why it is needed.mp4 8.5 MB
  • 2. Python Quick Start/1. About this section.mp4 7.8 MB
  • 2. Python Quick Start/15. Prompting for passwords.mp4 7.4 MB
  • 5. Foundations Tensorflow/10. Prediction example.mp4 7.1 MB
  • 8. Example Recommendations/11. Predictions.mp4 6.0 MB
  • 11. Next steps/2. Thank you.mp4 3.2 MB
  • 2. Python Quick Start/12. Classes.srt 16.7 kB
  • 4. Foundations NumPy/6. Linear Regression Example.srt 16.3 kB
  • 4. Foundations NumPy/5. Introduction to Linear Regression.srt 14.8 kB
  • 3. Example Logistic Regression/3. Data analysis.srt 12.8 kB
  • 10. Example Fraud detection/9. Making predictions.srt 12.3 kB
  • 3. Example Logistic Regression/12. Making predictions.srt 12.0 kB
  • 3. Example Logistic Regression/8. Gradient descent.srt 11.8 kB
  • 8. Example Recommendations/8. Model definition.srt 11.7 kB
  • 2. Python Quick Start/11. Defining functions.srt 11.5 kB
  • 6. Example Image recognition/2. Data analysis.srt 10.9 kB
  • 9. Example Sentiment Analysis/11. Transfer Learning Example.srt 10.5 kB
  • 2. Python Quick Start/13. File IO and Modules.srt 10.4 kB
  • 9. Example Sentiment Analysis/5. Data Preparation.srt 9.9 kB
  • 2. Python Quick Start/3. String formatting.srt 9.6 kB
  • 9. Example Sentiment Analysis/12. Fine Tuning and Prediction.srt 9.5 kB
  • 9. Example Sentiment Analysis/8. Model Training.srt 9.4 kB
  • 7. Foundations Pandas/2. Loading and inspecting data example.srt 9.3 kB
  • 3. Example Logistic Regression/11. Model training.srt 9.1 kB
  • 1. Course Structure and Development Environment/2. Course Quick Tips.srt 8.8 kB
  • 2. Python Quick Start/2. Basic Syntax.srt 8.7 kB
  • 8. Example Recommendations/5. Data preparation.srt 8.7 kB
  • 6. Example Image recognition/14. Hyperparameter tuning example.srt 8.7 kB
  • 1. Course Structure and Development Environment/8. Sharing Colab Notebooks.srt 8.5 kB
  • 10. Example Fraud detection/2. Data analysis.srt 8.2 kB
  • 4. Foundations NumPy/11. Images.srt 7.9 kB
  • 3. Example Logistic Regression/6. The forward function.srt 7.6 kB
  • 7. Foundations Pandas/5. Sorting and transforming data example.srt 7.4 kB
  • 2. Python Quick Start/4. Literal string interpolation.srt 7.3 kB
  • 2. Python Quick Start/7. Flow control.srt 7.2 kB
  • 6. Example Image recognition/8. Model training.srt 7.2 kB
  • 2. Python Quick Start/8. Lists.srt 7.2 kB
  • 7. Foundations Pandas/7. Visualizing data.srt 7.2 kB
  • 9. Example Sentiment Analysis/7. Model Definition.srt 7.1 kB
  • 2. Python Quick Start/10. Dictionaries.srt 7.1 kB
  • 8. Example Recommendations/12. Making predictions.srt 7.1 kB
  • 3. Example Logistic Regression/17. Improving the model.srt 7.0 kB
  • 7. Foundations Pandas/3. Indexing and selecting data example.srt 7.0 kB
  • 5. Foundations Tensorflow/2. Model example.srt 7.0 kB
  • 9. Example Sentiment Analysis/10. Transfer Learning with BERT.srt 6.8 kB
  • 8. Example Recommendations/4. Model selection.srt 6.7 kB
  • 3. Example Logistic Regression/10. Backpropagation.srt 6.6 kB
  • 8. Example Recommendations/9. Model training.srt 6.6 kB
  • 10. Example Fraud detection/7. Model training.srt 6.3 kB
  • 10. Example Fraud detection/11. Common questions.srt 6.0 kB
  • 3. Example Logistic Regression/15. Test vs. train accuracy.srt 6.0 kB
  • 4. Foundations NumPy/10. Visualizing data.srt 6.0 kB
  • 6. Example Image recognition/6. CNN Model Layers.srt 5.9 kB
  • 4. Foundations NumPy/9. Statistics and linear algebra.srt 5.9 kB
  • 10. Example Fraud detection/4. Unsupervised learning.srt 5.8 kB
  • 6. Example Image recognition/5. Data preparation.srt 5.8 kB
  • 6. Example Image recognition/11. Error analysis.srt 5.8 kB
  • 4. Foundations NumPy/2. Creating data with NumPy.srt 5.8 kB
  • 1. Course Structure and Development Environment/1. Course Structure and Development Environment.srt 5.7 kB
  • 1. Course Structure and Development Environment/9. Artificial Intelligence, Machine Learning, and Deep Learning.srt 5.7 kB
  • 3. Example Logistic Regression/5. The model.srt 5.6 kB
  • 8. Example Recommendations/13. Error analysis.srt 5.6 kB
  • 2. Python Quick Start/6. Type conversion.srt 5.6 kB
  • 9. Example Sentiment Analysis/2. Data Analysis.srt 5.5 kB
  • 10. Example Fraud detection/6. Model definition.srt 5.5 kB
  • 8. Example Recommendations/2. Data analysis.srt 5.5 kB
  • 9. Example Sentiment Analysis/4. Supervised Learning.srt 5.5 kB
  • 6. Example Image recognition/4. Model selection.srt 5.4 kB
  • 1. Course Structure and Development Environment/6. Jupyter notebook Math Markup and Magic Commands.srt 5.4 kB
  • 6. Example Image recognition/7. Model definition.srt 5.3 kB
  • 5. Foundations Tensorflow/5. Training example.srt 5.3 kB
  • 6. Example Image recognition/13. Hyperparameter tuning.srt 5.2 kB
  • 4. Foundations NumPy/3. Basic operations.srt 5.2 kB
  • 6. Example Image recognition/16. Common questions.srt 5.1 kB
  • 3. Example Logistic Regression/1. The problem.srt 5.1 kB
  • 5. Foundations Tensorflow/4. Activation functions.srt 5.1 kB
  • 3. Example Logistic Regression/7. Loss and cost functions.srt 4.9 kB
  • 5. Foundations Tensorflow/7. Loss functions.srt 4.5 kB
  • 4. Foundations NumPy/8. More Complex Models.srt 4.4 kB
  • 5. Foundations Tensorflow/11. Saving and restoring models.srt 4.3 kB
  • 8. Example Recommendations/7. Embedding layers.srt 4.2 kB
  • 7. Foundations Pandas/6. Aggregations example.srt 4.1 kB
  • 3. Example Logistic Regression/16. Speeding up training.srt 4.0 kB
  • 1. Course Structure and Development Environment/3. Introduction to Jupyter Notebook.srt 4.0 kB
  • 1. Course Structure and Development Environment/5. Jupyter notebook Code Cells.srt 4.0 kB
  • 10. Example Fraud detection/5. Data preparation.srt 3.9 kB
  • 6. Example Image recognition/1. The problem.srt 3.9 kB
  • 3. Example Logistic Regression/2. Machine Learning Development Process.srt 3.9 kB
  • 10. Example Fraud detection/1. The problem.srt 3.8 kB
  • 4. Foundations NumPy/13. Reshaping data.srt 3.8 kB
  • 5. Foundations Tensorflow/12. The Three Body Problem.srt 3.7 kB
  • 8. Example Recommendations/15. Common questions.srt 3.6 kB
  • 8. Example Recommendations/1. The problem.srt 3.4 kB
  • 1. Course Structure and Development Environment/4. Jupyter notebook Text Cells.srt 3.3 kB
  • 6. Example Image recognition/10. Making predictions.srt 3.0 kB
  • 5. Foundations Tensorflow/1. About this section.srt 3.0 kB
  • 9. Example Sentiment Analysis/1. The Problem.srt 3.0 kB
  • 5. Foundations Tensorflow/8. Optimizers.srt 2.9 kB
  • 5. Foundations Tensorflow/3. Model layers.srt 2.8 kB
  • 6. Example Image recognition/19. What you learned in this section.html 2.7 kB
  • 11. Next steps/1. Next steps.srt 2.6 kB
  • 5. Foundations Tensorflow/10. Prediction example.srt 2.4 kB
  • 2. Python Quick Start/15. Prompting for passwords.srt 2.4 kB
  • 7. Foundations Pandas/1. What is Pandas and why is it useful.srt 2.3 kB
  • 5. Foundations Tensorflow/13. What you learned in this section.html 1.7 kB
  • 3. Example Logistic Regression/18. What you learned in this section.html 1.6 kB
  • 4. Foundations NumPy/1. What is NumPy and why it is needed.srt 1.5 kB
  • 10. Example Fraud detection/13. What you learned in this section.html 1.4 kB
  • 2. Python Quick Start/1. About this section.srt 1.3 kB
  • 8. Example Recommendations/11. Predictions.srt 1.3 kB
  • 8. Example Recommendations/16. What you learned in this section.html 1.1 kB
  • 4. Foundations NumPy/14. What you learned in this section.html 823 Bytes
  • 1. Course Structure and Development Environment/10. What you learned in this section.html 674 Bytes
  • 2. Python Quick Start/16. What you learned in this section.html 584 Bytes
  • 7. Foundations Pandas/9. What you learned in this section.html 555 Bytes
  • 11. Next steps/2. Thank you.srt 538 Bytes
  • 9. Example Sentiment Analysis/14. What you learned in this section.html 425 Bytes
  • 5. Foundations Tensorflow/12.2 New Neural Network Could Solve The Three-Body Problem 100 Million Times Faster.html 174 Bytes
  • 1. Course Structure and Development Environment/8.1 Saving notebooks to Github or Drive.html 170 Bytes
  • 3. Example Logistic Regression/3.1 Github repo.html 159 Bytes
  • 7. Foundations Pandas/5.1 Sorting data.html 153 Bytes
  • 9. Example Sentiment Analysis/2.2 Github repo.html 149 Bytes
  • 1. Course Structure and Development Environment/7. Introduction to Notebooks.html 148 Bytes
  • 10. Example Fraud detection/10. Prediction and error analysis.html 148 Bytes
  • 10. Example Fraud detection/12. Improving the model.html 148 Bytes
  • 10. Example Fraud detection/3. Analyze credit card data set.html 148 Bytes
  • 10. Example Fraud detection/8. Training the model.html 148 Bytes
  • 2. Python Quick Start/14. Plot several math functions.html 148 Bytes
  • 2. Python Quick Start/5. Experiment with string formatting.html 148 Bytes
  • 2. Python Quick Start/9. Dot product.html 148 Bytes
  • 3. Example Logistic Regression/13. Training a model.html 148 Bytes
  • 3. Example Logistic Regression/14. Optional Wine Classification.html 148 Bytes
  • 3. Example Logistic Regression/4. Analyze Iris flower data set.html 148 Bytes
  • 3. Example Logistic Regression/9. Experiment with gradient descent.html 148 Bytes
  • 4. Foundations NumPy/12. Visualizing data.html 148 Bytes
  • 4. Foundations NumPy/4. Experiment with NumPy.html 148 Bytes
  • 4. Foundations NumPy/7. Experiment with Linear Regression.html 148 Bytes
  • 5. Foundations Tensorflow/6. Train a basic model.html 148 Bytes
  • 5. Foundations Tensorflow/9. Experiment with optimizers.html 148 Bytes
  • 6. Example Image recognition/12. Prediction and error analysis.html 148 Bytes
  • 6. Example Image recognition/15. Model improvement.html 148 Bytes
  • 6. Example Image recognition/17. Optional Real images.html 148 Bytes
  • 6. Example Image recognition/18. Optional Other image types.html 148 Bytes
  • 6. Example Image recognition/3. Analyze MNIST data set.html 148 Bytes
  • 6. Example Image recognition/9. Training a model.html 148 Bytes
  • 7. Foundations Pandas/4. Experiment with Pandas.html 148 Bytes
  • 7. Foundations Pandas/8. Visualizing data with Pandas.html 148 Bytes
  • 8. Example Recommendations/10. Training the model.html 148 Bytes
  • 8. Example Recommendations/14. Making recommendations and error analysis.html 148 Bytes
  • 8. Example Recommendations/3. Analyze MovieLens data set.html 148 Bytes
  • 8. Example Recommendations/6. Prepare data.html 148 Bytes
  • 9. Example Sentiment Analysis/13. Transfer Learning with BERT.html 148 Bytes
  • 9. Example Sentiment Analysis/3. Analyze Sentiment Data Set.html 148 Bytes
  • 9. Example Sentiment Analysis/6. Prepare Data.html 148 Bytes
  • 9. Example Sentiment Analysis/9. Training the Model.html 148 Bytes
  • 1. Course Structure and Development Environment/3.2 IBM Watson Studio Notebooks.html 147 Bytes
  • 2. Python Quick Start/3.2 printf style formatting.html 139 Bytes
  • 5. Foundations Tensorflow/4.2 Tensorflow activations.html 138 Bytes
  • 5. Foundations Tensorflow/2.1 Sequential models.html 137 Bytes
  • 5. Foundations Tensorflow/8.2 Tensorflow optimizers.html 137 Bytes
  • 7. Foundations Pandas/7.1 Pandas visualization user guide.html 135 Bytes
  • 5. Foundations Tensorflow/7.1 Loss functions.html 133 Bytes
  • 5. Foundations Tensorflow/10.1 Model API.html 132 Bytes
  • 5. Foundations Tensorflow/11.1 Model API.html 132 Bytes
  • 7. Foundations Pandas/3.1 User Guide Indexing and Selecting Data.html 130 Bytes
  • 9. Example Sentiment Analysis/2.1 Data set.html 129 Bytes
  • 7. Foundations Pandas/6.1 Pandas group by API.html 128 Bytes
  • 8. Example Recommendations/15.2 BellKor solution.html 126 Bytes
  • 1. Course Structure and Development Environment/4.1 Markdown cheat sheet.html 125 Bytes
  • 7. Foundations Pandas/2.2 Pandas IO.html 124 Bytes
  • 4. Foundations NumPy/9.2 Statistics functions.html 122 Bytes
  • 2. Python Quick Start/3.1 Format string syntax.html 120 Bytes
  • 8. Example Recommendations/15.1 Other solutions.html 120 Bytes
  • 10. Example Fraud detection/11.1 Building Autoencoders in Keras.html 118 Bytes
  • 4. Foundations NumPy/9.1 Linear algebra.html 118 Bytes
  • 5. Foundations Tensorflow/8.1 Stochastic gradient descent and related optimizers.html 118 Bytes
  • 9. Example Sentiment Analysis/1.2 Natural Language Processing (NLP).html 118 Bytes
  • 9. Example Sentiment Analysis/4.1 Natural Language Processing (NLP).html 118 Bytes
  • 1. Course Structure and Development Environment/3.6 AWS Sagemaker Notebook Instances.html 117 Bytes
  • 6. Example Image recognition/7.2 Sequential model guide.html 117 Bytes
  • 8. Example Recommendations/8.1 Keras functional API.html 115 Bytes
  • 8. Example Recommendations/2.1 Collaborative filtering article.html 114 Bytes
  • 4. Foundations NumPy/11.3 Image manipulation with NumPy.html 113 Bytes
  • 4. Foundations NumPy/11.1 Hughes 500.html 112 Bytes
  • 1. Course Structure and Development Environment/1.1 Github repo.html 110 Bytes
  • 5. Foundations Tensorflow/2.2 Github repo.html 110 Bytes
  • 5. Foundations Tensorflow/4.1 Activation functions.html 110 Bytes
  • 5. Foundations Tensorflow/12.1 Three Body Problem.html 109 Bytes
  • 9. Example Sentiment Analysis/1.1 Sentiment Analysis.html 109 Bytes
  • 1. Course Structure and Development Environment/6.1 LaTeX syntax.html 108 Bytes
  • 4. Foundations NumPy/11.2 Aviation.html 104 Bytes
  • 8. Example Recommendations/15.3 Netflix prize.html 104 Bytes
  • 9. Example Sentiment Analysis/5.1 GloVe Vectors.html 101 Bytes
  • 6. Example Image recognition/7.1 Keras CNN layers.html 99 Bytes
  • 1. Course Structure and Development Environment/3.5 Kaggle Notebooks.html 96 Bytes
  • 8. Example Recommendations/7.1 Keras Embedding Layers documentation.html 96 Bytes
  • 1. Course Structure and Development Environment/3.1 Google Colaboratory.html 95 Bytes
  • 10. Example Fraud detection/2.1 Github repo.html 94 Bytes
  • 4. Foundations NumPy/10.1 Matplotlib home page.html 94 Bytes
  • 6. Example Image recognition/1.1 The MNIST database of handwritten digits.html 94 Bytes
  • 6. Example Image recognition/2.1 Example Github repository.html 94 Bytes
  • 6. Example Image recognition/4.1 MNIST models and their accuracy.html 94 Bytes
  • 7. Foundations Pandas/2.1 Github repo.html 94 Bytes
  • 8. Example Recommendations/2.2 Github repo.html 94 Bytes
  • 6. Example Image recognition/10.1 Keras Model API.html 91 Bytes
  • 1. Course Structure and Development Environment/3.4 Microsoft Azure Notebooks.html 89 Bytes
  • 4. Foundations NumPy/2.2 NumPy documentation.html 88 Bytes
  • 5. Foundations Tensorflow/1.1 Tensorflow home page.html 87 Bytes
  • 7. Foundations Pandas/1.1 Pandas Home Page.html 86 Bytes
  • 1. Course Structure and Development Environment/3.3 CoCalc.html 80 Bytes
  • 4. Foundations NumPy/2.1 NumPy home page.html 79 Bytes
  • 0. Websites you may like/[DesireCourse.Net].url 51 Bytes
  • 1. Course Structure and Development Environment/[DesireCourse.Net].url 51 Bytes
  • 6. Example Image recognition/[DesireCourse.Net].url 51 Bytes
  • 9. Example Sentiment Analysis/[DesireCourse.Net].url 51 Bytes
  • [DesireCourse.Net].url 51 Bytes
  • 0. Websites you may like/[CourseClub.Me].url 48 Bytes
  • 1. Course Structure and Development Environment/[CourseClub.Me].url 48 Bytes
  • 6. Example Image recognition/[CourseClub.Me].url 48 Bytes
  • 9. Example Sentiment Analysis/[CourseClub.Me].url 48 Bytes
  • [CourseClub.Me].url 48 Bytes

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