Udemy - Machine Learning and Deep Learning Bootcamp in Python (1.2025)

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Udemy - Machine Learning and Deep Learning Bootcamp in Python (1.2025)

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最近下载：2026-01-03

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文件列表

  • 37. Q Learning Implementation (Tic Tac Toe)/6. Tic tac toe with Q learning implementation VI.mp4 116.2 MB
  • 05. Linear Regression/4. Linear regression implementation I.mp4 97.0 MB
  • 32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/6. Stochastic gradient descent implementation I.mp4 85.0 MB
  • 09. Naive Bayes Classifier/6. Naive Bayes example - clustering news.mp4 83.6 MB
  • 39. Deep Q Learning Implementation (Tic Tac Toe)/3. Tic Tac Toe with deep Q learning implementation III.mp4 76.5 MB
  • 06. Logistic Regression/4. Logistic regression example II- credit scoring.mp4 75.5 MB
  • 06. Logistic Regression/3. Logistic regression example I - sigmoid function.mp4 69.1 MB
  • 32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/9. ADAGrad implementation.mp4 62.6 MB
  • 25. Convolutional Neural Networks (CNNs) Implementation/2. Handwritten digit classification II.mp4 60.9 MB
  • 08. K-Nearest Neighbor Classifier/6. K-nearest neighbor implementation II.mp4 59.2 MB
  • 09. Naive Bayes Classifier/1. What is the naive Bayes classifier.mp4 57.7 MB
  • 15. Clustering/3. K-means clustering - text clustering.mp4 57.5 MB
  • 25. Convolutional Neural Networks (CNNs) Implementation/1. Handwritten digit classification I.mp4 57.0 MB
  • 16. Machine Learning Project I - Face Recognition/4. Understanding eigenfaces.mp4 56.2 MB
  • 35. Exploration vs. Exploitation Problem/3. N-armed bandit problem implementation.mp4 55.9 MB
  • 28. Recurrent Neural Networks (RNNs) Implementation/3. Time series analysis example III.mp4 55.2 MB
  • 26. Machine Learning Project III - Identifying Objects with CNNs/5. Tuning the parameters - regularization.mp4 54.5 MB
  • 34. Markov Decision Process (MDP) Theory/3. Markov decision processes - equations.mp4 52.1 MB
  • 37. Q Learning Implementation (Tic Tac Toe)/8. Tic tac toe with Q learning implementation VIII.mp4 51.0 MB
  • 06. Logistic Regression/1. What is logistic regression.mp4 50.7 MB
  • 26. Machine Learning Project III - Identifying Objects with CNNs/3. Fitting the model.mp4 50.2 MB
  • 39. Deep Q Learning Implementation (Tic Tac Toe)/4. Tic Tac Toe with deep Q learning implementation IV.mp4 48.9 MB
  • 31. Generative Adversarial Networks (GANs) Implementation/5. GAN implementation V.mp4 47.6 MB
  • 37. Q Learning Implementation (Tic Tac Toe)/4. Tic tac toe with Q learning implementation IV.mp4 45.6 MB
  • 30. Generative Adversarial Networks (GANs) Theory/4. Training GANs.mp4 45.5 MB
  • 29. Transformers/5. Understanding positional encoding.mp4 44.6 MB
  • 22. Deep Neural Networks Implementation/2. Deep neural network implementation II.mp4 44.1 MB
  • 03. Artificial Intelligence Basics/1. Why to learn artificial intelligence and machine learning.mp4 43.0 MB
  • 03. Artificial Intelligence Basics/2. Types of artificial intelligence learning.mp4 42.7 MB
  • 32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/7. Stochastic gradient descent implementation II.mp4 41.5 MB
  • 37. Q Learning Implementation (Tic Tac Toe)/5. Tic tac toe with Q learning implementation V.mp4 41.1 MB
  • 39. Deep Q Learning Implementation (Tic Tac Toe)/2. Tic Tac Toe with deep Q learning implementation II.mp4 41.1 MB
  • 13. Boosting/6. Boosting implementation II -wine classification.mp4 40.5 MB
  • 14. Principal Component Analysis (PCA)/3. Principal component analysis example I.mp4 40.2 MB
  • 31. Generative Adversarial Networks (GANs) Implementation/2. GAN implementation II.mp4 40.0 MB
  • 10. Support Vector Machines (SVMs)/5. Support vector machine example I - simple.mp4 39.4 MB
  • 13. Boosting/4. Boosting introduction - final formula.mp4 38.6 MB
  • 31. Generative Adversarial Networks (GANs) Implementation/3. GAN implementation III.mp4 38.3 MB
  • 29. Transformers/6. The self-attention mechanism I.mp4 38.2 MB
  • 40. Proximal Policy Optimization (PPO) Theory/2. TRPO algorithm introduction I.mp4 37.5 MB
  • 03. Artificial Intelligence Basics/3. Fundamentals of statistics.mp4 37.3 MB
  • 22. Deep Neural Networks Implementation/4. Multiclass classification implementation I.mp4 37.1 MB
  • 25. Convolutional Neural Networks (CNNs) Implementation/3. Handwritten digit classification III.mp4 36.9 MB
  • 05. Linear Regression/2. Linear regression theory - optimization.mp4 36.3 MB
  • 10. Support Vector Machines (SVMs)/4. Kernel functions.mp4 35.8 MB
  • 15. Clustering/8. Hierarchical clustering - market segmentation.mp4 35.5 MB
  • 11. Decision Trees/6. Decision trees implementation I.mp4 35.3 MB
  • 05. Linear Regression/1. What is linear regression.mp4 35.2 MB
  • 12. Random Forest Classifier/6. Random forests example II - credit scoring.mp4 34.8 MB
  • 27. Recurrent Neural Networks (RNNs) Theory/6. Long-short term memory (LSTM) model theory II.mp4 33.7 MB
  • 12. Random Forest Classifier/7. Random forests example III - OCR parameter tuning.mp4 33.5 MB
  • 05. Linear Regression/3. Linear regression theory - gradient descent.mp4 33.2 MB
  • 09. Naive Bayes Classifier/4. What is text clustering.mp4 33.0 MB
  • 22. Deep Neural Networks Implementation/5. Multiclass classification implementation II.mp4 33.0 MB
  • 13. Boosting/5. Boosting implementation I - iris dataset.mp4 32.6 MB
  • 31. Generative Adversarial Networks (GANs) Implementation/4. GAN implementation IV.mp4 32.0 MB
  • 10. Support Vector Machines (SVMs)/2. Linearly separable problems.mp4 31.8 MB
  • 39. Deep Q Learning Implementation (Tic Tac Toe)/5. Tic Tac Toe with deep Q learning implementation V.mp4 31.2 MB
  • 21. Deep Neural Networks Theory/5. Loss functions revisited.mp4 31.0 MB
  • 19. Simple Feed-Forward Neural Network Implementation/3. Credit scoring with simple neural networks.mp4 30.4 MB
  • 18. Feed-Forward Neural Network Theory/8. Gradient descent with backpropagation.mp4 30.2 MB
  • 32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/5. Stochastic gradient descent introduction.mp4 29.9 MB
  • 34. Markov Decision Process (MDP) Theory/4. Markov decision processes - illustration.mp4 29.6 MB
  • 30. Generative Adversarial Networks (GANs) Theory/2. GANs fundamentals.mp4 29.6 MB
  • 32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/3. Gradient descent implementation.mp4 29.4 MB
  • 29. Transformers/2. Understanding word embeddings I.mp4 29.2 MB
  • 16. Machine Learning Project I - Face Recognition/1. The Olivetti dataset.mp4 28.6 MB
  • 11. Decision Trees/4. The Gini-index approach.mp4 28.4 MB
  • 14. Principal Component Analysis (PCA)/4. Principal component analysis example II.mp4 28.1 MB
  • 11. Decision Trees/2. Entropy and information gain.mp4 28.1 MB
  • 40. Proximal Policy Optimization (PPO) Theory/3. TRPO algorithm introduction II.mp4 27.9 MB
  • 37. Q Learning Implementation (Tic Tac Toe)/7. Tic tac toe with Q learning implementation VII.mp4 27.7 MB
  • 22. Deep Neural Networks Implementation/3. Deep neural network implementation III.mp4 27.4 MB
  • 44. Appendix #3 - Data Structures in Python/15. Sets in Python.mp4 27.3 MB
  • 29. Transformers/7. The self-attention mechanism II.mp4 26.9 MB
  • 12. Random Forest Classifier/3. Bagging introduction.mp4 26.8 MB
  • 10. Support Vector Machines (SVMs)/7. Support vector machines example III - parameter tuning.mp4 26.7 MB
  • 29. Transformers/4. Tokenization and word embeddings.mp4 26.6 MB
  • 29. Transformers/10. The neural network layer.mp4 26.5 MB
  • 30. Generative Adversarial Networks (GANs) Theory/3. GANs learning procedure.mp4 26.5 MB
  • 38. Deep Q Learning Theory/3. Remember and replay.mp4 26.4 MB
  • 28. Recurrent Neural Networks (RNNs) Implementation/5. Time series analysis example V.mp4 26.4 MB
  • 27. Recurrent Neural Networks (RNNs) Theory/5. Long-short term memory (LSTM) model theory I.mp4 26.1 MB
  • 29. Transformers/9. Multi-head architecture.mp4 25.8 MB
  • 07. Cross Validation/1. What is cross validation.mp4 25.6 MB
  • 34. Markov Decision Process (MDP) Theory/7. What is value iteration.mp4 25.4 MB
  • 44. Appendix #3 - Data Structures in Python/16. Sorting.mp4 24.9 MB
  • 19. Simple Feed-Forward Neural Network Implementation/1. Simple neural network implementation - XOR problem.mp4 24.9 MB
  • 34. Markov Decision Process (MDP) Theory/1. What are Markov decision processes.mp4 24.9 MB
  • 29. Transformers/3. Understanding word embeddings II.mp4 24.7 MB
  • 37. Q Learning Implementation (Tic Tac Toe)/3. Tic tac toe with Q learning implementation III.mp4 24.7 MB
  • 24. Convolutional Neural Networks (CNNs) Theory/8. Colored images and tensors.mp4 24.5 MB
  • 44. Appendix #3 - Data Structures in Python/13. Hashing and O(1) running time complexity.mp4 24.2 MB
  • 10. Support Vector Machines (SVMs)/3. Non-linearly separable problems.mp4 24.1 MB
  • 02. Environment Setup/2. Installing PyCharm.mp4 23.9 MB
  • 06. Logistic Regression/2. Logistic regression and maximum likelihood estimation.mp4 23.8 MB
  • 43. Appendix #2 - Functions/3. Positional arguments and keyword arguments.mp4 23.3 MB
  • 10. Support Vector Machines (SVMs)/8. Support vector machine example IV - digit recognition.mp4 23.0 MB
  • 46. Appendix #5 - NumPy/7. Stacking and merging arrays.mp4 23.0 MB
  • 16. Machine Learning Project I - Face Recognition/2. Understanding the dataset.mp4 22.6 MB
  • 16. Machine Learning Project I - Face Recognition/3. Finding optimal number of principal components (eigenvectors).mp4 22.5 MB
  • 36. Q Learning Theory/3. Q learning illustration.mp4 22.5 MB
  • 47. COURSE MATERIALS (DOWNLOADS)/1. PythonMachineLearning.zip 22.2 MB
  • 06. Logistic Regression/5. Logistic regression example III - credit scoring.mp4 22.0 MB
  • 08. K-Nearest Neighbor Classifier/3. Distance metrics - Euclidean-distance.mp4 22.0 MB
  • 23. Machine Learning Project II - Smile Detector/3. Reading the images and constructing the dataset II.mp4 21.8 MB
  • 44. Appendix #3 - Data Structures in Python/11. What are linked list data structures.mp4 21.8 MB
  • 11. Decision Trees/1. What are decision trees.mp4 21.7 MB
  • 15. Clustering/5. DBSCAN example.mp4 21.6 MB
  • 12. Random Forest Classifier/5. Random forests example I - iris dataset.mp4 21.3 MB
  • 27. Recurrent Neural Networks (RNNs) Theory/2. Recurrent neural networks basics.mp4 21.2 MB
  • 15. Clustering/7. Hierarchical clustering example.mp4 21.2 MB
  • 10. Support Vector Machines (SVMs)/1. What are Support Vector Machines (SVMs).mp4 21.1 MB
  • 14. Principal Component Analysis (PCA)/1. Principal component analysis (PCA) introduction.mp4 21.1 MB
  • 26. Machine Learning Project III - Identifying Objects with CNNs/4. What is batch normalization.mp4 20.9 MB
  • 23. Machine Learning Project II - Smile Detector/2. Reading the images and constructing the dataset I.mp4 20.6 MB
  • 35. Exploration vs. Exploitation Problem/2. N-armed bandit problem introduction.mp4 20.5 MB
  • 44. Appendix #3 - Data Structures in Python/14. Dictionaries in Python.mp4 20.4 MB
  • 15. Clustering/2. K-means clustering example.mp4 20.0 MB
  • 18. Feed-Forward Neural Network Theory/4. Neural networks - the big picture.mp4 20.0 MB
  • 16. Machine Learning Project I - Face Recognition/5. Constructing the machine learning models.mp4 19.9 MB
  • 18. Feed-Forward Neural Network Theory/9. Backpropagation explained.mp4 19.8 MB
  • 37. Q Learning Implementation (Tic Tac Toe)/1. Tic tac toe with Q learning implementation I.mp4 19.8 MB
  • 26. Machine Learning Project III - Identifying Objects with CNNs/1. What is the CIFAR-10 dataset.mp4 19.7 MB
  • 44. Appendix #3 - Data Structures in Python/6. Lists in Python - advanced operations.mp4 19.5 MB
  • 46. Appendix #5 - NumPy/3. Dimension of arrays.mp4 19.3 MB
  • 44. Appendix #3 - Data Structures in Python/1. How to measure the running time of algorithms.mp4 19.2 MB
  • 10. Support Vector Machines (SVMs)/6. Support vector machine example II - iris dataset.mp4 19.0 MB
  • 40. Proximal Policy Optimization (PPO) Theory/7. Proximal policy optimization (PPO) algorithm IV.mp4 18.8 MB
  • 45. Appendix #4 - Object Oriented Programming (OOP)/3. Using the constructor.mp4 18.7 MB
  • 11. Decision Trees/3. Example of how to construct decision trees.mp4 18.5 MB
  • 32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/12. ADAM optimizer implementation.mp4 18.5 MB
  • 22. Deep Neural Networks Implementation/1. Deep neural network implementation I.mp4 18.3 MB
  • 43. Appendix #2 - Functions/9. What is recursion.mp4 18.2 MB
  • 21. Deep Neural Networks Theory/3. Activation functions and weight initializations.mp4 18.1 MB
  • 45. Appendix #4 - Object Oriented Programming (OOP)/13. Comparing objects - overriding functions.mp4 17.9 MB
  • 24. Convolutional Neural Networks (CNNs) Theory/5. Convolutional neural networks - flattening and the neural network layer.mp4 17.9 MB
  • 46. Appendix #5 - NumPy/6. Reshape.mp4 17.8 MB
  • 37. Q Learning Implementation (Tic Tac Toe)/2. Tic tac toe with Q learning implementation II.mp4 17.7 MB
  • 24. Convolutional Neural Networks (CNNs) Theory/2. Feature selection with kernels.mp4 17.6 MB
  • 46. Appendix #5 - NumPy/2. Creating and updating arrays.mp4 17.6 MB
  • 46. Appendix #5 - NumPy/4. Indexes and slicing.mp4 17.5 MB
  • 15. Clustering/1. K-means clustering introduction.mp4 17.4 MB
  • 15. Clustering/6. Hierarchical clustering introduction.mp4 17.4 MB
  • 28. Recurrent Neural Networks (RNNs) Implementation/2. Time series analysis example II.mp4 17.3 MB
  • 08. K-Nearest Neighbor Classifier/5. K-nearest neighbor implementation I.mp4 17.1 MB
  • 11. Decision Trees/8. Decision tree implementation III - identifying cancer.mp4 17.0 MB
  • 45. Appendix #4 - Object Oriented Programming (OOP)/9. What is polymorphism.mp4 17.0 MB
  • 38. Deep Q Learning Theory/1. What is deep Q learning.mp4 16.8 MB
  • 42. Appendix #1 - Python Basics/9. How to use multiple conditions.mp4 16.7 MB
  • 08. K-Nearest Neighbor Classifier/2. Concept of lazy learning.mp4 16.7 MB
  • 13. Boosting/1. What is boosting.mp4 16.5 MB
  • 28. Recurrent Neural Networks (RNNs) Implementation/6. Time series analysis example VI.mp4 16.5 MB
  • 36. Q Learning Theory/2. Q learning introduction - the algorithm.mp4 16.2 MB
  • 34. Markov Decision Process (MDP) Theory/5. Bellman-equation.mp4 16.2 MB
  • 07. Cross Validation/2. Cross validation example.mp4 16.1 MB
  • 45. Appendix #4 - Object Oriented Programming (OOP)/5. Private variables and name mangling.mp4 16.0 MB
  • 12. Random Forest Classifier/1. What is the bias-variance tradeoff.mp4 16.0 MB
  • 18. Feed-Forward Neural Network Theory/7. Optimization with gradient descent.mp4 16.0 MB
  • 10. Support Vector Machines (SVMs)/9. Support vector machine example V - digit recognition.mp4 15.6 MB
  • 21. Deep Neural Networks Theory/7. Normalization, batches and epochs.mp4 15.5 MB
  • 08. K-Nearest Neighbor Classifier/4. Bias and variance trade-off.mp4 15.4 MB
  • 09. Naive Bayes Classifier/5. Text clustering - inverse document frequency (TF-IDF).mp4 15.4 MB
  • 45. Appendix #4 - Object Oriented Programming (OOP)/4. Class variables and instance variables.mp4 15.4 MB
  • 21. Deep Neural Networks Theory/6. Gradient descent and stochastic gradient descent.mp4 15.4 MB
  • 42. Appendix #1 - Python Basics/4. Strings.mp4 15.3 MB
  • 32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/10. What is RMSProp.mp4 15.2 MB
  • 34. Markov Decision Process (MDP) Theory/2. Markov decision processes basics.mp4 14.8 MB
  • 11. Decision Trees/7. Decision trees implementation II - parameter tuning.mp4 14.8 MB
  • 40. Proximal Policy Optimization (PPO) Theory/5. Proximal policy optimization (PPO) algorithm II.mp4 14.6 MB
  • 23. Machine Learning Project II - Smile Detector/5. Evaluating and testing the model.mp4 14.6 MB
  • 30. Generative Adversarial Networks (GANs) Theory/1. What is a generative adversarial network (GAN).mp4 14.6 MB
  • 21. Deep Neural Networks Theory/4. Softmax activation function.mp4 14.5 MB
  • 45. Appendix #4 - Object Oriented Programming (OOP)/10. Polymorphism and abstraction example.mp4 14.4 MB
  • 08. K-Nearest Neighbor Classifier/1. What is the k-nearest neighbor classifier.mp4 14.4 MB
  • 13. Boosting/3. Boosting introduction - equations.mp4 14.1 MB
  • 39. Deep Q Learning Implementation (Tic Tac Toe)/1. Tic Tac Toe with deep Q learning implementation I.mp4 14.0 MB
  • 32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/2. What is gradient descent.mp4 14.0 MB
  • 31. Generative Adversarial Networks (GANs) Implementation/1. GAN implementation I.mp4 13.8 MB
  • 08. K-Nearest Neighbor Classifier/7. K-nearest neighbor implementation III.mp4 13.6 MB
  • 38. Deep Q Learning Theory/4. Why use an additional target neural network.mp4 13.4 MB
  • 42. Appendix #1 - Python Basics/5. String slicing.mp4 13.3 MB
  • 14. Principal Component Analysis (PCA)/2. Mathematical formulation of principal components.mp4 13.3 MB
  • 19. Simple Feed-Forward Neural Network Implementation/2. Linearly and non-linearly separable problems.mp4 13.2 MB
  • 05. Linear Regression/5. Linear regression implementation II.mp4 13.2 MB
  • 24. Convolutional Neural Networks (CNNs) Theory/1. What are convolutional neural networks.mp4 13.1 MB
  • 32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/8. What is ADAGrad.mp4 13.0 MB
  • 44. Appendix #3 - Data Structures in Python/4. What are array data structures II.mp4 12.9 MB
  • 44. Appendix #3 - Data Structures in Python/3. What are array data structures I.mp4 12.9 MB
  • 18. Feed-Forward Neural Network Theory/3. Why to use activation functions.mp4 12.7 MB
  • 35. Exploration vs. Exploitation Problem/4. Applications AB testing in marketing.mp4 12.7 MB
  • 24. Convolutional Neural Networks (CNNs) Theory/9. Evolution of CNN architectures.mp4 12.5 MB
  • 36. Q Learning Theory/1. What is Q learning.mp4 12.4 MB
  • 18. Feed-Forward Neural Network Theory/6. How to measure the error of the network.mp4 12.3 MB
  • 31. Generative Adversarial Networks (GANs) Implementation/6. GAN implementation VI.mp4 12.3 MB
  • 27. Recurrent Neural Networks (RNNs) Theory/1. Why do recurrent neural networks are important.mp4 12.2 MB
  • 44. Appendix #3 - Data Structures in Python/12. Doubly linked list implementation in Python.mp4 12.0 MB
  • 21. Deep Neural Networks Theory/2. Vanishing and exploding gradients problem.mp4 12.0 MB
  • 44. Appendix #3 - Data Structures in Python/7. Lists in Python - list comprehension.mp4 11.9 MB
  • 15. Clustering/4. DBSCAN introduction.mp4 11.9 MB
  • 16. Machine Learning Project I - Face Recognition/6. Using cross-validation.mp4 11.9 MB
  • 18. Feed-Forward Neural Network Theory/1. What are feed-forward neural networks.mp4 11.9 MB
  • 40. Proximal Policy Optimization (PPO) Theory/4. Proximal policy optimization (PPO) algorithm I.mp4 11.9 MB
  • 13. Boosting/2. Boosting introduction - illustration.mp4 11.7 MB
  • 09. Naive Bayes Classifier/3. Naive Bayes classifier implementation.mp4 11.6 MB
  • 18. Feed-Forward Neural Network Theory/2. Artificial neural networks - the model.mp4 11.6 MB
  • 24. Convolutional Neural Networks (CNNs) Theory/7. How do you update the kernel weights exactly.mp4 11.6 MB
  • 45. Appendix #4 - Object Oriented Programming (OOP)/11. Modules.mp4 11.6 MB
  • 27. Recurrent Neural Networks (RNNs) Theory/3. The backpropagation through time (BPTT) algorithm.mp4 11.5 MB
  • 24. Convolutional Neural Networks (CNNs) Theory/3. Convolutional operation example.mp4 11.3 MB
  • 06. Logistic Regression/6. Why is logistic regression linear.mp4 11.3 MB
  • 42. Appendix #1 - Python Basics/7. Operators.mp4 11.2 MB
  • 12. Random Forest Classifier/4. Random forest classifier introduction.mp4 11.1 MB
  • 21. Deep Neural Networks Theory/8. Regularization.mp4 11.1 MB
  • 24. Convolutional Neural Networks (CNNs) Theory/6. Convolutional neural networks - illustration.mp4 11.1 MB
  • 44. Appendix #3 - Data Structures in Python/5. Lists in Python.mp4 11.0 MB
  • 32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/4. Gradient descent with momentum.mp4 10.8 MB
  • 40. Proximal Policy Optimization (PPO) Theory/1. What are the problems with deep Q learning.mp4 10.5 MB
  • 46. Appendix #5 - NumPy/5. Types.mp4 10.4 MB
  • 42. Appendix #1 - Python Basics/15. Break and continue.mp4 10.4 MB
  • 02. Environment Setup/3. Installing TensorFlow and Keras.mp4 10.2 MB
  • 43. Appendix #2 - Functions/2. Defining functions.mp4 10.1 MB
  • 42. Appendix #1 - Python Basics/11. Loops - for loop.mp4 10.0 MB
  • 27. Recurrent Neural Networks (RNNs) Theory/4. Vanishing and exploding gradients problem.mp4 10.0 MB
  • 09. Naive Bayes Classifier/2. Naive Bayes classifier illustration.mp4 9.7 MB
  • 43. Appendix #2 - Functions/6. Yield operator.mp4 9.6 MB
  • 45. Appendix #4 - Object Oriented Programming (OOP)/7. The super keyword.mp4 9.6 MB
  • 21. Deep Neural Networks Theory/1. What are deep neural networks.mp4 9.1 MB
  • 44. Appendix #3 - Data Structures in Python/10. Mutability and immutability.mp4 9.1 MB
  • 23. Machine Learning Project II - Smile Detector/4. Building the deep neural network model.mp4 9.0 MB
  • 42. Appendix #1 - Python Basics/8. Conditional statements.mp4 9.0 MB
  • 28. Recurrent Neural Networks (RNNs) Implementation/4. Time series analysis example IV.mp4 8.9 MB
  • 24. Convolutional Neural Networks (CNNs) Theory/4. Convolutional neural networks - pooling.mp4 8.7 MB
  • 42. Appendix #1 - Python Basics/6. Type casting.mp4 8.6 MB
  • 46. Appendix #5 - NumPy/1. What is the key advantage of NumPy.mp4 8.6 MB
  • 45. Appendix #4 - Object Oriented Programming (OOP)/6. What is inheritance in OOP.mp4 8.5 MB
  • 43. Appendix #2 - Functions/1. What are functions.mp4 8.5 MB
  • 42. Appendix #1 - Python Basics/10. Logical operators.mp4 8.4 MB
  • 01. Introduction/1. Introduction.mp4 8.3 MB
  • 43. Appendix #2 - Functions/10. Local vs global variables.mp4 8.2 MB
  • 11. Decision Trees/5. Decision trees introduction - pros and cons.mp4 8.1 MB
  • 42. Appendix #1 - Python Basics/2. What are the basic data types.mp4 8.1 MB
  • 42. Appendix #1 - Python Basics/14. Enumerate.mp4 8.1 MB
  • 18. Feed-Forward Neural Network Theory/5. Using bias nodes in the neural network.mp4 8.1 MB
  • 45. Appendix #4 - Object Oriented Programming (OOP)/12. The __str__ function.mp4 8.0 MB
  • 46. Appendix #5 - NumPy/8. Filter.mp4 8.0 MB
  • 35. Exploration vs. Exploitation Problem/1. Exploration vs exploitation problem.mp4 8.0 MB
  • 43. Appendix #2 - Functions/8. What are the most relevant built-in functions.mp4 8.0 MB
  • 42. Appendix #1 - Python Basics/12. Loops - while loop.mp4 7.9 MB
  • 44. Appendix #3 - Data Structures in Python/9. What are tuples.mp4 7.9 MB
  • 27. Recurrent Neural Networks (RNNs) Theory/9. Gated recurrent units (GRUs).mp4 7.9 MB
  • 29. Transformers/8. What is masking.mp4 7.8 MB
  • 29. Transformers/11. Understanding the training of transformers.mp4 7.8 MB
  • 42. Appendix #1 - Python Basics/1. First steps in Python.mp4 7.7 MB
  • 27. Recurrent Neural Networks (RNNs) Theory/7. Long-short term memory (LSTM) forward pass example.mp4 7.7 MB
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  • 34. Markov Decision Process (MDP) Theory/8. What is policy iteration.mp4 7.3 MB
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  • 13. Boosting/7. Boosting vs. bagging.mp4 7.2 MB
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  • 33. ### REINFORCEMENT LEARNING ###/2. Applications of reinforcement learning.mp4 6.9 MB
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  • 02. Environment Setup/1. Installing Python.mp4 5.0 MB
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  • 13. Boosting/4. Boosting introduction - final formula.vtt 11.4 kB
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  • 10. Support Vector Machines (SVMs)/8. Support vector machine example IV - digit recognition.vtt 11.3 kB
  • 40. Proximal Policy Optimization (PPO) Theory/7. Proximal policy optimization (PPO) algorithm IV.vtt 11.2 kB
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  • 03. Artificial Intelligence Basics/2. Types of artificial intelligence learning.vtt 10.6 kB
  • 29. Transformers/4. Tokenization and word embeddings.vtt 10.6 kB
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  • 36. Q Learning Theory/2. Q learning introduction - the algorithm.vtt 9.8 kB
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  • 32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/2. What is gradient descent.vtt 9.4 kB
  • 13. Boosting/3. Boosting introduction - equations.vtt 9.4 kB
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  • 39. Deep Q Learning Implementation (Tic Tac Toe)/5. DeepQLearningTicTacToe.py 9.2 kB
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  • 32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/8. What is ADAGrad.vtt 9.1 kB
  • 16. Machine Learning Project I - Face Recognition/1. The Olivetti dataset.vtt 9.0 kB
  • 37. Q Learning Implementation (Tic Tac Toe)/2. Tic tac toe with Q learning implementation II.vtt 9.0 kB
  • 34. Markov Decision Process (MDP) Theory/2. Markov decision processes basics.vtt 9.0 kB
  • 40. Proximal Policy Optimization (PPO) Theory/5. Proximal policy optimization (PPO) algorithm II.vtt 9.0 kB
  • 03. Artificial Intelligence Basics/3. Fundamentals of statistics.vtt 9.0 kB
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  • 16. Machine Learning Project I - Face Recognition/4. Understanding eigenfaces.vtt 8.9 kB
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  • 37. Q Learning Implementation (Tic Tac Toe)/3. Tic tac toe with Q learning implementation III.vtt 8.5 kB
  • 24. Convolutional Neural Networks (CNNs) Theory/5. Convolutional neural networks - flattening and the neural network layer.vtt 8.5 kB
  • 31. Generative Adversarial Networks (GANs) Implementation/4. GAN implementation IV.vtt 8.5 kB
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  • 37. Q Learning Implementation (Tic Tac Toe)/4. Tic tac toe with Q learning implementation IV.vtt 8.3 kB
  • 11. Decision Trees/3. Example of how to construct decision trees.vtt 8.3 kB
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  • 03. Artificial Intelligence Basics/1. Why to learn artificial intelligence and machine learning.vtt 8.2 kB
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  • 38. Deep Q Learning Theory/4. Why use an additional target neural network.vtt 8.1 kB
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  • 26. Machine Learning Project III - Identifying Objects with CNNs/1. What is the CIFAR-10 dataset.vtt 8.0 kB
  • 08. K-Nearest Neighbor Classifier/5. K-nearest neighbor implementation I.vtt 8.0 kB
  • 25. Convolutional Neural Networks (CNNs) Implementation/3. Handwritten digit classification III.vtt 8.0 kB
  • 10. Support Vector Machines (SVMs)/1. What are Support Vector Machines (SVMs).vtt 7.9 kB
  • 22. Deep Neural Networks Implementation/1. Deep neural network implementation I.vtt 7.9 kB
  • 37. Q Learning Implementation (Tic Tac Toe)/8. Tic tac toe with Q learning implementation VIII.vtt 7.9 kB
  • 39. Deep Q Learning Implementation (Tic Tac Toe)/2. Tic Tac Toe with deep Q learning implementation II.vtt 7.9 kB
  • 38. Deep Q Learning Theory/3. Remember and replay.vtt 7.9 kB
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  • 29. Transformers/10. The neural network layer.vtt 7.8 kB
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  • 11. Decision Trees/4. The Gini-index approach.vtt 7.7 kB
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  • 36. Q Learning Theory/1. What is Q learning.vtt 7.6 kB
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  • 18. Feed-Forward Neural Network Theory/6. How to measure the error of the network.vtt 7.5 kB
  • 32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/7. Stochastic gradient descent implementation II.vtt 7.5 kB
  • 44. Appendix #3 - Data Structures in Python/5. Lists in Python.vtt 7.5 kB
  • 42. Appendix #1 - Python Basics/15. Break and continue.vtt 7.5 kB
  • 13. Boosting/2. Boosting introduction - illustration.vtt 7.4 kB
  • 24. Convolutional Neural Networks (CNNs) Theory/2. Feature selection with kernels.vtt 7.4 kB
  • 10. Support Vector Machines (SVMs)/6. Support vector machine example II - iris dataset.vtt 7.4 kB
  • 43. Appendix #2 - Functions/2. Defining functions.vtt 7.3 kB
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  • 18. Feed-Forward Neural Network Theory/2. Artificial neural networks - the model.vtt 7.3 kB
  • 22. Deep Neural Networks Implementation/3. Deep neural network implementation III.vtt 7.3 kB
  • 34. Markov Decision Process (MDP) Theory/5. Bellman-equation.vtt 7.2 kB
  • 37. Q Learning Implementation (Tic Tac Toe)/7. Tic tac toe with Q learning implementation VII.vtt 7.2 kB
  • 07. Cross Validation/1. What is cross validation.vtt 7.2 kB
  • 45. Appendix #4 - Object Oriented Programming (OOP)/10. Polymorphism and abstraction example.vtt 7.2 kB
  • 12. Random Forest Classifier/3. Bagging introduction.vtt 7.1 kB
  • 40. Proximal Policy Optimization (PPO) Theory/4. Proximal policy optimization (PPO) algorithm I.vtt 7.1 kB
  • 06. Logistic Regression/2. Logistic regression and maximum likelihood estimation.vtt 7.1 kB
  • 27. Recurrent Neural Networks (RNNs) Theory/3. The backpropagation through time (BPTT) algorithm.vtt 7.0 kB
  • 11. Decision Trees/1. What are decision trees.vtt 7.0 kB
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  • 23. Machine Learning Project II - Smile Detector/2. Reading the images and constructing the dataset I.vtt 6.9 kB
  • 16. Machine Learning Project I - Face Recognition/2. Understanding the dataset.vtt 6.8 kB
  • 27. Recurrent Neural Networks (RNNs) Theory/7. Long-short term memory (LSTM) forward pass example.vtt 6.8 kB
  • 31. Generative Adversarial Networks (GANs) Implementation/3. GAN implementation III.vtt 6.8 kB
  • 16. Machine Learning Project I - Face Recognition/3. Finding optimal number of principal components (eigenvectors).vtt 6.8 kB
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  • 24. Convolutional Neural Networks (CNNs) Theory/9. Evolution of CNN architectures.vtt 6.5 kB
  • 39. Deep Q Learning Implementation (Tic Tac Toe)/4. Tic Tac Toe with deep Q learning implementation IV.vtt 6.5 kB
  • 42. Appendix #1 - Python Basics/2. What are the basic data types.vtt 6.5 kB
  • 22. Deep Neural Networks Implementation/5. Multiclass classification implementation II.vtt 6.5 kB
  • 12. Random Forest Classifier/4. Random forest classifier introduction.vtt 6.4 kB
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  • 11. Decision Trees/8. Decision tree implementation III - identifying cancer.vtt 6.4 kB
  • 28. Recurrent Neural Networks (RNNs) Implementation/2. Time series analysis example II.vtt 6.4 kB
  • 09. Naive Bayes Classifier/2. Naive Bayes classifier illustration.vtt 6.4 kB
  • 18. Feed-Forward Neural Network Theory/1. What are feed-forward neural networks.vtt 6.4 kB
  • 10. Support Vector Machines (SVMs)/9. Support vector machine example V - digit recognition.vtt 6.3 kB
  • 34. Markov Decision Process (MDP) Theory/1. What are Markov decision processes.vtt 6.3 kB
  • 21. Deep Neural Networks Theory/2. Vanishing and exploding gradients problem.vtt 6.3 kB
  • 21. Deep Neural Networks Theory/3. Activation functions and weight initializations.vtt 6.3 kB
  • 28. Recurrent Neural Networks (RNNs) Implementation/5. Time series analysis example V.vtt 6.2 kB
  • 06. Logistic Regression/5. Logistic regression example III - credit scoring.vtt 6.2 kB
  • 11. Decision Trees/7. Decision trees implementation II - parameter tuning.vtt 6.2 kB
  • 44. Appendix #3 - Data Structures in Python/10. Mutability and immutability.vtt 6.2 kB
  • 43. Appendix #2 - Functions/1. What are functions.vtt 6.1 kB
  • 07. Cross Validation/2. Cross validation example.vtt 6.1 kB
  • 45. Appendix #4 - Object Oriented Programming (OOP)/5. Private variables and name mangling.vtt 6.1 kB
  • 13. Boosting/1. What is boosting.vtt 6.1 kB
  • 31. Generative Adversarial Networks (GANs) Implementation/1. GAN implementation I.vtt 6.1 kB
  • 06. Logistic Regression/6. Why is logistic regression linear.vtt 6.1 kB
  • 32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/11. ADAM optimizer introduction.vtt 6.0 kB
  • 09. Naive Bayes Classifier/5. Text clustering - inverse document frequency (TF-IDF).vtt 6.0 kB
  • 35. Exploration vs. Exploitation Problem/4. Applications AB testing in marketing.vtt 6.0 kB
  • 29. Transformers/11. Understanding the training of transformers.vtt 5.9 kB
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  • 30. Generative Adversarial Networks (GANs) Theory/1. What is a generative adversarial network (GAN).vtt 5.7 kB
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  • 45. Appendix #4 - Object Oriented Programming (OOP)/4. Class variables and instance variables.vtt 5.7 kB
  • 43. Appendix #2 - Functions/10. Local vs global variables.vtt 5.7 kB
  • 42. Appendix #1 - Python Basics/8. Conditional statements.vtt 5.6 kB
  • 42. Appendix #1 - Python Basics/6. Type casting.vtt 5.6 kB
  • 32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/4. Gradient descent with momentum.vtt 5.6 kB
  • 39. Deep Q Learning Implementation (Tic Tac Toe)/5. Tic Tac Toe with deep Q learning implementation V.vtt 5.5 kB
  • 09. Naive Bayes Classifier/3. Naive Bayes classifier implementation.vtt 5.5 kB
  • 26. Machine Learning Project III - Identifying Objects with CNNs/4. What is batch normalization.vtt 5.5 kB
  • 37. Q Learning Implementation (Tic Tac Toe)/5. Tic tac toe with Q learning implementation V.vtt 5.5 kB
  • 01. Introduction/1. Introduction.vtt 5.4 kB
  • 44. Appendix #3 - Data Structures in Python/9. What are tuples.vtt 5.4 kB
  • 02. Environment Setup/2. Installing PyCharm.vtt 5.4 kB
  • 29. Transformers/8. What is masking.vtt 5.4 kB
  • 08. K-Nearest Neighbor Classifier/4. Bias and variance trade-off.vtt 5.4 kB
  • 21. Deep Neural Networks Theory/6. Gradient descent and stochastic gradient descent.vtt 5.3 kB
  • 34. Markov Decision Process (MDP) Theory/8. What is policy iteration.vtt 5.3 kB
  • 42. Appendix #1 - Python Basics/14. Enumerate.vtt 5.2 kB
  • 40. Proximal Policy Optimization (PPO) Theory/1. What are the problems with deep Q learning.vtt 5.2 kB
  • 21. Deep Neural Networks Theory/8. Regularization.vtt 5.2 kB
  • 27. Recurrent Neural Networks (RNNs) Theory/1. Why do recurrent neural networks are important.vtt 5.2 kB
  • 23. Machine Learning Project II - Smile Detector/3. Reading the images and constructing the dataset II.vtt 5.2 kB
  • 31. Generative Adversarial Networks (GANs) Implementation/6. GAN.py 5.2 kB
  • 28. Recurrent Neural Networks (RNNs) Implementation/6. Time series analysis example VI.vtt 5.1 kB
  • 05. Linear Regression/5. Linear regression implementation II.vtt 5.1 kB
  • 32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/10. What is RMSProp.vtt 5.1 kB
  • 16. Machine Learning Project I - Face Recognition/5. Constructing the machine learning models.vtt 5.1 kB
  • 24. Convolutional Neural Networks (CNNs) Theory/4. Convolutional neural networks - pooling.vtt 5.1 kB
  • 21. Deep Neural Networks Theory/1. What are deep neural networks.vtt 5.0 kB
  • 27. Recurrent Neural Networks (RNNs) Theory/9. Gated recurrent units (GRUs).vtt 5.0 kB
  • 44. Appendix #3 - Data Structures in Python/2. Data structures introduction.vtt 5.0 kB
  • 28. Recurrent Neural Networks (RNNs) Implementation/1. Time series analysis example I.vtt 5.0 kB
  • 35. Exploration vs. Exploitation Problem/1. Exploration vs exploitation problem.vtt 4.9 kB
  • 45. Appendix #4 - Object Oriented Programming (OOP)/6. What is inheritance in OOP.vtt 4.9 kB
  • 46. Appendix #5 - NumPy/8. Filter.vtt 4.9 kB
  • 43. Appendix #2 - Functions/11. The __main__ function.vtt 4.8 kB
  • 31. Generative Adversarial Networks (GANs) Implementation/6. GAN implementation VI.vtt 4.8 kB
  • 42. Appendix #1 - Python Basics/10. Logical operators.vtt 4.7 kB
  • 37. Q Learning Implementation (Tic Tac Toe)/1. Tic tac toe with Q learning implementation I.vtt 4.7 kB
  • 08. K-Nearest Neighbor Classifier/7. K-nearest neighbor implementation III.vtt 4.7 kB
  • 11. Decision Trees/5. Decision trees introduction - pros and cons.vtt 4.6 kB
  • 38. Deep Q Learning Theory/2. Deep Q learning and ε-greedy strategy.vtt 4.6 kB
  • 39. Deep Q Learning Implementation (Tic Tac Toe)/1. Tic Tac Toe with deep Q learning implementation I.vtt 4.6 kB
  • 08. K-Nearest Neighbor Classifier/2. Concept of lazy learning.vtt 4.5 kB
  • 18. Feed-Forward Neural Network Theory/5. Using bias nodes in the neural network.vtt 4.5 kB
  • 27. Recurrent Neural Networks (RNNs) Theory/4. Vanishing and exploding gradients problem.vtt 4.4 kB
  • 13. Boosting/7. Boosting vs. bagging.vtt 4.4 kB
  • 23. Machine Learning Project II - Smile Detector/5. Evaluating and testing the model.vtt 4.2 kB
  • 27. Recurrent Neural Networks (RNNs) Theory/8. Long-short term memory (LSTM) backpropagation example.vtt 4.2 kB
  • 45. Appendix #4 - Object Oriented Programming (OOP)/12. The __str__ function.vtt 4.2 kB
  • 40. Proximal Policy Optimization (PPO) Theory/6. Proximal policy optimization (PPO) algorithm III.vtt 4.1 kB
  • 43. Appendix #2 - Functions/5. Returning multiple values.vtt 4.0 kB
  • 10. Support Vector Machines (SVMs)/10. Advantages and disadvantages.vtt 4.0 kB
  • 45. Appendix #4 - Object Oriented Programming (OOP)/2. Class and objects basics.vtt 3.8 kB
  • 29. Transformers/1. Transformers chapter overview.vtt 3.7 kB
  • 33. ### REINFORCEMENT LEARNING ###/2. Applications of reinforcement learning.vtt 3.7 kB
  • 42. Appendix #1 - Python Basics/13. What are nested loops.vtt 3.7 kB
  • 23. Machine Learning Project II - Smile Detector/4. Building the deep neural network model.vtt 3.6 kB
  • 28. Recurrent Neural Networks (RNNs) Implementation/4. Time series analysis example IV.vtt 3.6 kB
  • 02. Environment Setup/1. Installing Python.vtt 3.4 kB
  • 45. Appendix #4 - Object Oriented Programming (OOP)/1. What is object oriented programming (OOP).vtt 3.3 kB
  • 45. Appendix #4 - Object Oriented Programming (OOP)/8. Function (method) override.vtt 3.3 kB
  • 42. Appendix #1 - Python Basics/16. Calculating Fibonacci-numbers.vtt 3.3 kB
  • 34. Markov Decision Process (MDP) Theory/6. How to solve MDP problems.vtt 3.3 kB
  • 41. ### PYTHON PROGRAMMING CRASH COURSE ###/1. Python crash course introduction.vtt 3.2 kB
  • 24. Convolutional Neural Networks (CNNs) Theory/6. Convolutional neural networks - illustration.vtt 3.2 kB
  • 20. Deep Learning/1. Types of neural networks.vtt 3.1 kB
  • 43. Appendix #2 - Functions/4. Returning values.vtt 3.1 kB
  • 29. Transformers/12. What is ChatGPT.vtt 3.0 kB
  • 16. Machine Learning Project I - Face Recognition/6. Using cross-validation.vtt 3.0 kB
  • 02. Environment Setup/3. Installing TensorFlow and Keras.vtt 3.0 kB
  • 43. Appendix #2 - Functions/7. Local and global variables.vtt 2.8 kB
  • 12. Random Forest Classifier/2. Pruning introduction.vtt 2.8 kB
  • 42. Appendix #1 - Python Basics/3. Booleans.vtt 2.7 kB
  • 23. Machine Learning Project II - Smile Detector/1. Understanding the classification problem.vtt 2.4 kB
  • 19. Simple Feed-Forward Neural Network Implementation/2. Linearly and non-linearly separable problems.vtt 2.2 kB
  • 32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/7. StochasticGradientDescentRegression.py 2.2 kB
  • 26. Machine Learning Project III - Identifying Objects with CNNs/2. Preprocessing the data.vtt 2.2 kB
  • 32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/6. StochasticGradientDescent.py 2.0 kB
  • 32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/9. GradientDescentAdaGrad.py 1.6 kB
  • 46. Appendix #5 - NumPy/9. Running time comparison arrays and lists.html 1.3 kB
  • 32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/3. GradientDescent.py 1.3 kB
  • 32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/12. ADAM.py 1.1 kB
  • 33. ### REINFORCEMENT LEARNING ###/1. What is reinforcement learning.html 899 Bytes
  • 44. Appendix #3 - Data Structures in Python/8. (!!!) Python lists and arrays.html 631 Bytes
  • 15. Clustering/9. Mathematical formulation of clustering.html 629 Bytes
  • 32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/1. Numerical optimization algorithms in machine learning.html 599 Bytes
  • 04. ### MACHINE LEARNING ###/1. Machine learning section.html 471 Bytes
  • 10. Support Vector Machines (SVMs)/11. Mathematical formulation of Support Vector Machines (SVMs).html 419 Bytes
  • 17. ### NEURAL NETWORKS AND DEEP LEARNING ###/1. Neural networks and deep learning section.html 374 Bytes
  • 11. Decision Trees/9. Mathematical formulation of decision trees.html 356 Bytes
  • 29. Transformers/13. Mathematical formulation of transformers.html 323 Bytes
  • 13. Boosting/8. Mathematical formulation of boosting.html 290 Bytes
  • 21. Deep Neural Networks Theory/9. Mathematical formulation of deep neural networks.html 290 Bytes
  • 24. Convolutional Neural Networks (CNNs) Theory/11. Mathematical formulation of convolution neural networks.html 290 Bytes
  • 34. Markov Decision Process (MDP) Theory/9. Mathematical formulation of reinforcement learning.html 284 Bytes
  • 14. Principal Component Analysis (PCA)/5. Mathematical formulation of principle component analysis (PCA).html 282 Bytes
  • 08. K-Nearest Neighbor Classifier/8. Mathematical formulation of k-nearest neighbor classifier.html 276 Bytes
  • 05. Linear Regression/6. Mathematical formulation of linear regression.html 275 Bytes
  • 32. ### NUMERICAL OPTIMIZATION (OPTIMIZERS) ###/13. Mathematical formulation of optimization algorithms in machine learning.html 275 Bytes
  • 38. Deep Q Learning Theory/5. Mathematical formulation of deep Q learning.html 272 Bytes
  • 06. Logistic Regression/7. Mathematical formulation of logistic regression.html 263 Bytes
  • 12. Random Forest Classifier/8. Mathematical formulation of random forest classifiers.html 263 Bytes
  • 36. Q Learning Theory/4. Mathematical formulation of Q learning.html 262 Bytes
  • 18. Feed-Forward Neural Network Theory/10. Mathematical formulation of feed-forward neural networks.html 261 Bytes
  • 27. Recurrent Neural Networks (RNNs) Theory/10. Mathematical formulation of recurrent neural networks.html 258 Bytes
  • 09. Naive Bayes Classifier/7. Mathematical formulation of naive Bayes classifier.html 246 Bytes
  • 30. Generative Adversarial Networks (GANs) Theory/5. Mathematical formulation of GANs.html 234 Bytes
  • 24. Convolutional Neural Networks (CNNs) Theory/10. CNN visualization tool.html 126 Bytes
  • 47. COURSE MATERIALS (DOWNLOADS)/1. Course materials (source code and slides).html 66 Bytes

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