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种子名称： [FreeCourseSite.com] Udemy - Introduction to Machine Learning & Deep Learning in Python
文件类型： 视频
文件数目： 140个文件
文件大小： 1.67 GB
收录时间： 2019-12-11 12:45
已经下载： 3次
资源热度： 112
最近下载： 2024-10-14 17:05

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[FreeCourseSite.com] Udemy - Introduction to Machine Learning & Deep Learning in Python.torrent

1. Introduction/1. Introduction.mp43.48MB

1. Introduction/2. Introduction to machine learning.mp48.05MB

10. Boosting/1. Boosting introduction - basics.mp48.39MB

10. Boosting/2. Boosting introduction - illustration.mp48.17MB

10. Boosting/3. Boosting introduction - equations.mp413.71MB

10. Boosting/4. Boosting introduction - final formula.mp413.01MB

10. Boosting/5. Boosting implementation I - iris dataset.mp412.33MB

10. Boosting/6. Boosting implementation II -tuning.mp410.35MB

10. Boosting/7. Boosting vs. bagging.mp45.21MB

11. Clustering/1. Principal component anlysis introduction.mp48.58MB

11. Clustering/10. Hierarchical clustering example.mp411.96MB

11. Clustering/2. Principal component analysis example.mp414MB

11. Clustering/3. K-means clustering introduction I.mp413.67MB

11. Clustering/4. K-means clustering introduction II.mp49.47MB

11. Clustering/5. K-means clustering example.mp49.43MB

11. Clustering/6. K-means clustering - text clustering.mp418.86MB

11. Clustering/7. DBSCAN introduction.mp411.05MB

11. Clustering/8. DBSCAN example.mp47.88MB

11. Clustering/9. Hierarchical clustering introduction.mp413.66MB

12. Neural Networks/11. Feedforward neural networks.mp418.42MB

12. Neural Networks/12. Optimization - cost function.mp425.89MB

12. Neural Networks/13. Simplified feedforward network.mp419.42MB

12. Neural Networks/14. Feedforward neural network topology.mp414.73MB

12. Neural Networks/15. The learning algorithm.mp413.26MB

12. Neural Networks/16. Error calculation.mp413.74MB

12. Neural Networks/17. Gradient calculation I - output layer.mp420.28MB

12. Neural Networks/18. Gradient calculation II - hidden layer.mp49.18MB

12. Neural Networks/19. Backpropagation.mp412.67MB

12. Neural Networks/2. Axons and neurons in the human brain.mp419.24MB

12. Neural Networks/20. Backpropagation II.mp44.68MB

12. Neural Networks/21. Applications of neural networks I - character recognition.mp48.78MB

12. Neural Networks/22. Applications of neural networks II - stock market forecast.mp49.53MB

12. Neural Networks/23. Deep learning.mp49.47MB

12. Neural Networks/25. Building networks.mp412.75MB

12. Neural Networks/26. Building networks II.mp412.02MB

12. Neural Networks/27. Handling datasets.mp46.21MB

12. Neural Networks/28. Neural network example I - XOR problem.mp417.61MB

12. Neural Networks/29. Neural network example II - iris dataset.mp435.59MB

12. Neural Networks/3. Modeling human brain.mp416.17MB

12. Neural Networks/4. Learning paradigms.mp46.51MB

12. Neural Networks/5. Artificial neurons - the model.mp416.55MB

12. Neural Networks/6. Artificial neurons - activation functions.mp414.24MB

12. Neural Networks/7. Artificial neurons - an example.mp411.37MB

12. Neural Networks/8. Neural networks - the big picture.mp410.78MB

12. Neural Networks/9. Applications of neural networks.mp45.23MB

13. Machine Learning in Finance/1. Stock market basics.mp45.63MB

13. Machine Learning in Finance/2. Fetching data from Yahoo Finance.mp47.96MB

13. Machine Learning in Finance/3. Predicting stock prices logistic regression.mp410.76MB

13. Machine Learning in Finance/4. Predicting stock prices k-nearest neighbor.mp47.1MB

13. Machine Learning in Finance/5. Predicting stock prices support vector machine.mp48.71MB

13. Machine Learning in Finance/6. Predicting stock prices - conclusion.mp43.51MB

14. Computer Vision - Face Detection/1. Computer vision introduction.mp45.76MB

14. Computer Vision - Face Detection/10. Face detection implementation IV - tuning the parameters.mp48.73MB

14. Computer Vision - Face Detection/2. Viola-Jones algorithm.mp420.94MB

14. Computer Vision - Face Detection/3. Haar-features.mp412.64MB

14. Computer Vision - Face Detection/4. Integral images.mp49.58MB

14. Computer Vision - Face Detection/5. Boosting in computer vision.mp412.32MB

14. Computer Vision - Face Detection/6. Cascading.mp46.23MB

14. Computer Vision - Face Detection/7. Face detection implementation I - installing OpenCV.mp410.56MB

14. Computer Vision - Face Detection/8. Face detection implementation II - CascadeClassifier.mp415.92MB

14. Computer Vision - Face Detection/9. Face detection implementation III - CascadeClassifier parameters.mp48.6MB

15. Deep Learning/1. Types of neural networks.mp45.49MB

16. Deep Neural Networks/1. Deep neural networks.mp47.65MB

16. Deep Neural Networks/11. Multiclass classification implementation I.mp411.1MB

16. Deep Neural Networks/12. Multiclass classification implementation II.mp410.31MB

16. Deep Neural Networks/2. Activation functions revisited.mp415.42MB

16. Deep Neural Networks/3. Loss functions.mp410.39MB

16. Deep Neural Networks/4. Gradient descent stochastic gradient descent.mp412.26MB

16. Deep Neural Networks/5. Hyperparameters.mp48.26MB

16. Deep Neural Networks/7. Deep neural network implementation I.mp415.09MB

16. Deep Neural Networks/8. Deep neural network implementation II.mp415.81MB

16. Deep Neural Networks/9. Deep neural network implementation III.mp418.4MB

17. Convolutional Neural Networks/10. Handwritten digit classification I.mp416.48MB

17. Convolutional Neural Networks/11. Handwritten digit classification II.mp415.65MB

17. Convolutional Neural Networks/12. Handwritten digit classification III.mp410.44MB

17. Convolutional Neural Networks/2. Convolutional neural networks basics.mp49.58MB

17. Convolutional Neural Networks/3. Feature selection.mp46.95MB

17. Convolutional Neural Networks/4. Convolutional neural networks - kernel.mp46.34MB

17. Convolutional Neural Networks/5. Convolutional neural networks - kernel II.mp47.79MB

17. Convolutional Neural Networks/6. Convolutional neural networks - pooling.mp49.85MB

17. Convolutional Neural Networks/7. Convolutional neural networks - flattening.mp48.41MB

17. Convolutional Neural Networks/8. Convolutional neural networks - illustration.mp46.02MB

18. Recurrent Neural Networks/10. Stock price prediction example III.mp44.98MB

18. Recurrent Neural Networks/11. Stock price prediction example IV.mp414.55MB

18. Recurrent Neural Networks/12. Stock price prediction example V.mp46.74MB

18. Recurrent Neural Networks/13. Stock price prediction example VI.mp415.2MB

18. Recurrent Neural Networks/14. Stock price prediction example VII.mp47.21MB

18. Recurrent Neural Networks/2. Why do recurrent neural networks are important.mp47.52MB

18. Recurrent Neural Networks/3. Recurrent neural networks basics.mp412.89MB

18. Recurrent Neural Networks/4. Vanishing and exploding gradients problem.mp419.63MB

18. Recurrent Neural Networks/5. Long-short term memory (LTSM) model.mp417.03MB

18. Recurrent Neural Networks/6. Gated recurrent units (GRUs).mp45.03MB

18. Recurrent Neural Networks/8. Stock price prediction example I.mp411.09MB

18. Recurrent Neural Networks/9. Stock price prediction example II.mp418.37MB

2. Installations/1. Installing Anaconda.mp44.32MB

2. Installations/2. Installing Spyder.mp42.8MB

2. Installations/3. Installing Keras and TensorFlow.mp45.95MB

3. Linear Regression/1. Linear regression introduction.mp426.43MB

3. Linear Regression/2. Linear regression theory - optimization.mp442.28MB

3. Linear Regression/3. Linear regression theory - gradient descent.mp411.1MB

3. Linear Regression/4. Linear regression implementation I.mp416.69MB

3. Linear Regression/5. Linear regression implementation II.mp48.78MB

4. Logistic Regression/1. Logistic regression introduction.mp417.63MB

4. Logistic Regression/2. Logistic regression introduction II.mp46.67MB

4. Logistic Regression/3. Logistic regression example I - sigmoid function.mp413.04MB

4. Logistic Regression/4. Logistic regression example II- credit scoring.mp421.33MB

4. Logistic Regression/5. Logistic regression example III - credit scoring.mp410.87MB

4. Logistic Regression/6. Cross validation introduction.mp411.72MB

4. Logistic Regression/7. Cross validation example.mp44.15MB

5. K-Nearest Neighbor Classifier/1. K-nearest neighbor introduction.mp49.48MB

5. K-Nearest Neighbor Classifier/2. K-nearest neighbor introduction - lazy learning.mp48.11MB

5. K-Nearest Neighbor Classifier/3. K-nearest neighbor introduction - Euclidean-distance.mp48.61MB

5. K-Nearest Neighbor Classifier/5. K-nearest neighbor implementation I.mp46.95MB

5. K-Nearest Neighbor Classifier/6. K-nearest neighbor implementation II.mp49.96MB

5. K-Nearest Neighbor Classifier/7. K-nearest neighbor implementation III.mp47.93MB

6. Naive Bayes Classifier/1. Naive Bayes classifier introduction I.mp417.44MB

6. Naive Bayes Classifier/2. Naive Bayes classifier introduction II - illustration.mp48.43MB

6. Naive Bayes Classifier/3. Naive Bayes classifier implementation.mp48MB

6. Naive Bayes Classifier/5. Text clustering - basics.mp422.12MB

6. Naive Bayes Classifier/6. Text clustering - inverse document frequency (TF-IDF).mp410.02MB

6. Naive Bayes Classifier/7. Naive Bayes example - clustering news.mp423.33MB

7. Support Vector Machine (SVM)/1. Support vector machine introduction I - linear case.mp420.76MB

7. Support Vector Machine (SVM)/2. Support vector machine introduction II - non-linear case.mp417.22MB

7. Support Vector Machine (SVM)/3. Support vector machine introduction III - kernels.mp49.9MB

7. Support Vector Machine (SVM)/4. Support vector machine example I - simple.mp410.48MB

7. Support Vector Machine (SVM)/5. Support vector machine example II - iris dataset.mp421.7MB

7. Support Vector Machine (SVM)/6. Support vector machine example III - digit recognition.mp416.43MB

8. Decision Trees/1. Decision trees introduction - basics.mp411.73MB

8. Decision Trees/2. Decision trees introduction - entropy.mp419.29MB

8. Decision Trees/3. Decision trees introduction - information gain.mp446.96MB

8. Decision Trees/4. Decision trees introduction - pros and cons.mp44.19MB

8. Decision Trees/5. Decision trees implementation.mp413.6MB

8. Decision Trees/6. Decision trees implementation II.mp46.66MB

8. Decision Trees/7. The Gini-index approach.mp418.75MB

9. Random Forest Classifier/1. Pruning introduction.mp49.83MB

9. Random Forest Classifier/2. Bagging introduction.mp411.72MB

9. Random Forest Classifier/3. Random forest classifier introduction.mp48.72MB

9. Random Forest Classifier/4. Random forests example I - iris dataset.mp411.36MB

9. Random Forest Classifier/5. Random forests example II - credit scoring.mp44.21MB

9. Random Forest Classifier/6. Random forests example III - parameter tuning.mp49.19MB