[GigaCourse.Com] Udemy - Machine Learning Natural Language Processing in Python (V2)

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[GigaCourse.Com] Udemy - Machine Learning Natural Language Processing in Python (V2)

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收录时间：2023-12-17
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文件列表

  • 18. Recurrent Neural Networks/9. Parts-of-Speech (POS) Tagging in Tensorflow.mp4 152.2 MB
  • 3. Vector Models and Text Preprocessing/14. TF-IDF (Code).mp4 131.0 MB
  • 16. Feedforward Artificial Neural Networks/13. CBOW in Tensorflow (Advanced).mp4 123.3 MB
  • 21. Effective Learning Strategies for Machine Learning FAQ/4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 113.4 MB
  • 9. Spam Detection/6. Spam Detection in Python.mp4 112.8 MB
  • 7. Cipher Decryption (Advanced)/4. Genetic Algorithms.mp4 110.3 MB
  • 3. Vector Models and Text Preprocessing/10. Count Vectorizer (Code).mp4 106.9 MB
  • 6. Article Spinner (Intermediate)/4. Article Spinner in Python (pt 1).mp4 100.6 MB
  • 16. Feedforward Artificial Neural Networks/4. Activation Functions.mp4 93.7 MB
  • 17. Convolutional Neural Networks/6. CNN Architecture.mp4 93.6 MB
  • 11. Text Summarization/8. TextRank in Python (Advanced).mp4 86.3 MB
  • 18. Recurrent Neural Networks/6. GRU and LSTM (pt 1).mp4 86.3 MB
  • 13. Latent Semantic Analysis (Latent Semantic Indexing)/2. SVD (Singular Value Decomposition) Intuition.mp4 85.8 MB
  • 15. The Neuron/4. Text Classification in Tensorflow.mp4 85.6 MB
  • 17. Convolutional Neural Networks/2. What is Convolution.mp4 83.7 MB
  • 3. Vector Models and Text Preprocessing/16. How to Build TF-IDF From Scratch.mp4 83.7 MB
  • 21. Effective Learning Strategies for Machine Learning FAQ/3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 83.5 MB
  • 11. Text Summarization/4. Text Summarization in Python.mp4 81.9 MB
  • 6. Article Spinner (Intermediate)/5. Article Spinner in Python (pt 2).mp4 79.0 MB
  • 17. Convolutional Neural Networks/5. Convolution on Color Images.mp4 78.9 MB
  • 3. Vector Models and Text Preprocessing/9. Stemming and Lemmatization Demo.mp4 78.5 MB
  • 3. Vector Models and Text Preprocessing/6. Tokenization.mp4 77.1 MB
  • 1. Introduction/1. Introduction and Outline.mp4 76.5 MB
  • 12. Topic Modeling/6. Topic Modeling with Latent Dirichlet Allocation (LDA) in Python.mp4 75.9 MB
  • 5. Markov Models (Intermediate)/8. Building a Text Classifier (Code pt 2).mp4 75.7 MB
  • 20. Extra Help With Python Coding for Beginners FAQ/1. How to Code by Yourself (part 1).mp4 75.3 MB
  • 20. Extra Help With Python Coding for Beginners FAQ/3. Proof that using Jupyter Notebook is the same as not using it.mp4 72.8 MB
  • 15. The Neuron/2. Fitting a Line.mp4 71.9 MB
  • 3. Vector Models and Text Preprocessing/18. Neural Word Embeddings Demo.mp4 70.1 MB
  • 7. Cipher Decryption (Advanced)/3. Language Models (Review).mp4 68.7 MB
  • 2. Getting Set Up/2. How to use Github & Extra Coding Tips (Optional).mp4 67.0 MB
  • 10. Sentiment Analysis/2. Logistic Regression Intuition (pt 1).mp4 66.7 MB
  • 10. Sentiment Analysis/6. Sentiment Analysis in Python (pt 1).mp4 66.2 MB
  • 5. Markov Models (Intermediate)/11. Language Model (Code pt 1).mp4 65.9 MB
  • 9. Spam Detection/4. Aside Class Imbalance, ROC, AUC, and F1 Score (pt 1).mp4 63.1 MB
  • 12. Topic Modeling/5. Latent Dirichlet Allocation (LDA) - Intuition (Advanced).mp4 63.1 MB
  • 3. Vector Models and Text Preprocessing/12. TF-IDF (Theory).mp4 61.4 MB
  • 3. Vector Models and Text Preprocessing/8. Stemming and Lemmatization.mp4 60.7 MB
  • 5. Markov Models (Intermediate)/7. Building a Text Classifier (Code pt 1).mp4 60.5 MB
  • 13. Latent Semantic Analysis (Latent Semantic Indexing)/4. Latent Semantic Analysis Latent Semantic Indexing in Python.mp4 60.4 MB
  • 3. Vector Models and Text Preprocessing/5. Count Vectorizer (Theory).mp4 60.2 MB
  • 18. Recurrent Neural Networks/5. RNNs Paying Attention to Shapes.mp4 59.9 MB
  • 16. Feedforward Artificial Neural Networks/3. The Geometrical Picture.mp4 59.3 MB
  • 3. Vector Models and Text Preprocessing/21. How To Do NLP In Other Languages.mp4 58.7 MB
  • 12. Topic Modeling/2. Latent Dirichlet Allocation (LDA) - Essentials.mp4 57.9 MB
  • 9. Spam Detection/5. Aside Class Imbalance, ROC, AUC, and F1 Score (pt 2).mp4 56.6 MB
  • 19. Setting Up Your Environment FAQ/2. Anaconda Environment Setup.mp4 55.2 MB
  • 12. Topic Modeling/7. Non-Negative Matrix Factorization (NMF) Intuition.mp4 55.0 MB
  • 5. Markov Models (Intermediate)/12. Language Model (Code pt 2).mp4 55.0 MB
  • 10. Sentiment Analysis/7. Sentiment Analysis in Python (pt 2).mp4 54.5 MB
  • 15. The Neuron/6. How does a model learn.mp4 54.1 MB
  • 9. Spam Detection/2. Naive Bayes Intuition.mp4 53.8 MB
  • 19. Setting Up Your Environment FAQ/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 53.4 MB
  • 18. Recurrent Neural Networks/7. GRU and LSTM (pt 2).mp4 52.8 MB
  • 16. Feedforward Artificial Neural Networks/8. Text Preprocessing Code Preparation.mp4 52.4 MB
  • 11. Text Summarization/6. TextRank - How It Really Works (Advanced).mp4 51.7 MB
  • 20. Extra Help With Python Coding for Beginners FAQ/2. How to Code by Yourself (part 2).mp4 51.6 MB
  • 3. Vector Models and Text Preprocessing/3. What is a Vector.mp4 51.3 MB
  • 3. Vector Models and Text Preprocessing/15. Word-to-Index Mapping.mp4 49.9 MB
  • 16. Feedforward Artificial Neural Networks/2. Forward Propagation.mp4 49.0 MB
  • 11. Text Summarization/5. TextRank Intuition.mp4 48.2 MB
  • 18. Recurrent Neural Networks/8. RNN for Text Classification in Tensorflow.mp4 48.2 MB
  • 5. Markov Models (Intermediate)/3. The Markov Model.mp4 48.1 MB
  • 3. Vector Models and Text Preprocessing/17. Neural Word Embeddings.mp4 47.8 MB
  • 15. The Neuron/5. The Neuron.mp4 47.4 MB
  • 11. Text Summarization/9. Text Summarization in Python - The Easy Way (Beginner).mp4 47.4 MB
  • 3. Vector Models and Text Preprocessing/11. Vector Similarity.mp4 47.3 MB
  • 5. Markov Models (Intermediate)/9. Language Model (Theory).mp4 47.1 MB
  • 16. Feedforward Artificial Neural Networks/5. Multiclass Classification.mp4 46.6 MB
  • 2. Getting Set Up/1. Get Your Hands Dirty, Practical Coding Experience, Data Links.mp4 45.7 MB
  • 10. Sentiment Analysis/1. Sentiment Analysis - Problem Description.mp4 44.8 MB
  • 16. Feedforward Artificial Neural Networks/10. Embeddings.mp4 44.3 MB
  • 18. Recurrent Neural Networks/4. RNN Code Preparation.mp4 44.2 MB
  • 17. Convolutional Neural Networks/8. Convolutional Neural Network for NLP in Tensorflow.mp4 44.1 MB
  • 6. Article Spinner (Intermediate)/1. Article Spinning - Problem Description.mp4 44.0 MB
  • 2. Getting Set Up/4. How to Succeed in This Course.mp4 43.2 MB
  • 18. Recurrent Neural Networks/3. Simple RNN Elman Unit (pt 2).mp4 43.2 MB
  • 7. Cipher Decryption (Advanced)/10. Code pt 5.mp4 43.0 MB
  • 18. Recurrent Neural Networks/2. Simple RNN Elman Unit (pt 1).mp4 42.8 MB
  • 17. Convolutional Neural Networks/7. CNNs for Text.mp4 42.5 MB
  • 22. Appendix FAQ Finale/2. BONUS.mp4 41.8 MB
  • 10. Sentiment Analysis/4. Logistic Regression Training and Interpretation (pt 3).mp4 41.6 MB
  • 7. Cipher Decryption (Advanced)/11. Code pt 6.mp4 41.3 MB
  • 7. Cipher Decryption (Advanced)/7. Code pt 2.mp4 41.0 MB
  • 21. Effective Learning Strategies for Machine Learning FAQ/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 40.9 MB
  • 16. Feedforward Artificial Neural Networks/1. ANN - Section Introduction.mp4 40.5 MB
  • 16. Feedforward Artificial Neural Networks/15. Aside How to Choose Hyperparameters (Optional).mp4 39.9 MB
  • 16. Feedforward Artificial Neural Networks/7. Text Classification ANN in Tensorflow.mp4 37.9 MB
  • 12. Topic Modeling/8. Topic Modeling with Non-Negative Matrix Factorization (NMF) in Python.mp4 37.8 MB
  • 13. Latent Semantic Analysis (Latent Semantic Indexing)/3. LSA LSI Applying SVD to NLP.mp4 36.1 MB
  • 5. Markov Models (Intermediate)/4. Probability Smoothing and Log-Probabilities.mp4 35.7 MB
  • 15. The Neuron/3. Classification Code Preparation.mp4 34.5 MB
  • 5. Markov Models (Intermediate)/2. The Markov Property.mp4 33.8 MB
  • 18. Recurrent Neural Networks/10. Named Entity Recognition (NER) in Tensorflow.mp4 33.1 MB
  • 9. Spam Detection/1. Spam Detection - Problem Description.mp4 32.9 MB
  • 16. Feedforward Artificial Neural Networks/9. Text Preprocessing in Tensorflow.mp4 32.4 MB
  • 17. Convolutional Neural Networks/4. What is Convolution (Weight Sharing).mp4 31.3 MB
  • 8. Machine Learning Models (Introduction)/1. Machine Learning Models (Introduction).mp4 31.0 MB
  • 7. Cipher Decryption (Advanced)/8. Code pt 3.mp4 31.0 MB
  • 5. Markov Models (Intermediate)/6. Building a Text Classifier (Exercise Prompt).mp4 30.8 MB
  • 13. Latent Semantic Analysis (Latent Semantic Indexing)/5. LSA LSI Exercises.mp4 30.5 MB
  • 5. Markov Models (Intermediate)/5. Building a Text Classifier (Theory).mp4 30.3 MB
  • 5. Markov Models (Intermediate)/10. Language Model (Exercise Prompt).mp4 30.2 MB
  • 3. Vector Models and Text Preprocessing/2. Basic Definitions for NLP.mp4 29.7 MB
  • 6. Article Spinner (Intermediate)/6. Case Study Article Spinning Gone Wrong.mp4 29.6 MB
  • 3. Vector Models and Text Preprocessing/22. Suggestion Box.mp4 28.5 MB
  • 4. Probabilistic Models (Introduction)/1. Probabilistic Models (Introduction).mp4 28.2 MB
  • 1. Introduction/2. Are You Beginner, Intermediate, or Advanced All are OK!.mp4 28.0 MB
  • 7. Cipher Decryption (Advanced)/1. Section Introduction.mp4 27.6 MB
  • 11. Text Summarization/2. Text Summarization Using Vectors.mp4 27.0 MB
  • 11. Text Summarization/1. Text Summarization Section Introduction.mp4 27.0 MB
  • 7. Cipher Decryption (Advanced)/9. Code pt 4.mp4 26.9 MB
  • 17. Convolutional Neural Networks/1. CNN - Section Introduction.mp4 26.9 MB
  • 6. Article Spinner (Intermediate)/3. Article Spinner Exercise Prompt.mp4 25.8 MB
  • 17. Convolutional Neural Networks/3. What is Convolution (Pattern Matching).mp4 25.8 MB
  • 14. Deep Learning (Introduction)/1. Deep Learning Introduction (Intermediate-Advanced).mp4 25.7 MB
  • 7. Cipher Decryption (Advanced)/13. Section Conclusion.mp4 25.4 MB
  • 10. Sentiment Analysis/3. Multiclass Logistic Regression (pt 2).mp4 24.8 MB
  • 3. Vector Models and Text Preprocessing/7. Stopwords.mp4 24.6 MB
  • 19. Setting Up Your Environment FAQ/1. Pre-Installation Check.mp4 23.9 MB
  • 2. Getting Set Up/5. Temporary 403 Errors.mp4 23.1 MB
  • 13. Latent Semantic Analysis (Latent Semantic Indexing)/1. LSA LSI Section Introduction.mp4 22.0 MB
  • 18. Recurrent Neural Networks/1. RNN - Section Introduction.mp4 21.9 MB
  • 3. Vector Models and Text Preprocessing/19. Vector Models & Text Preprocessing Summary.mp4 21.9 MB
  • 7. Cipher Decryption (Advanced)/5. Code Preparation.mp4 21.6 MB
  • 16. Feedforward Artificial Neural Networks/6. ANN Code Preparation.mp4 21.1 MB
  • 11. Text Summarization/10. Text Summarization Section Summary.mp4 21.1 MB
  • 21. Effective Learning Strategies for Machine Learning FAQ/1. How to Succeed in this Course (Long Version).mp4 18.7 MB
  • 2. Getting Set Up/3. Where to get the code, notebooks, and data.mp4 18.6 MB
  • 3. Vector Models and Text Preprocessing/1. Vector Models & Text Preprocessing Intro.mp4 18.4 MB
  • 7. Cipher Decryption (Advanced)/2. Ciphers.mp4 18.1 MB
  • 12. Topic Modeling/1. Topic Modeling Section Introduction.mp4 17.9 MB
  • 10. Sentiment Analysis/5. Sentiment Analysis - Exercise Prompt.mp4 17.4 MB
  • 22. Appendix FAQ Finale/1. What is the Appendix.mp4 17.2 MB
  • 7. Cipher Decryption (Advanced)/6. Code pt 1.mp4 16.8 MB
  • 6. Article Spinner (Intermediate)/2. Article Spinning - N-Gram Approach.mp4 16.7 MB
  • 16. Feedforward Artificial Neural Networks/11. CBOW (Advanced).mp4 16.5 MB
  • 5. Markov Models (Intermediate)/13. Markov Models Section Summary.mp4 16.3 MB
  • 7. Cipher Decryption (Advanced)/12. Cipher Decryption - Additional Discussion.mp4 15.4 MB
  • 18. Recurrent Neural Networks/11. Exercise Return to CNNs (Advanced).mp4 15.3 MB
  • 12. Topic Modeling/3. LDA - Code Preparation.mp4 15.2 MB
  • 3. Vector Models and Text Preprocessing/4. Bag of Words.mp4 14.5 MB
  • 3. Vector Models and Text Preprocessing/13. (Interactive) Recommender Exercise Prompt.mp4 14.0 MB
  • 5. Markov Models (Intermediate)/1. Markov Models Section Introduction.mp4 13.7 MB
  • 15. The Neuron/1. The Neuron - Section Introduction.mp4 11.5 MB
  • 15. The Neuron/7. The Neuron - Section Summary.mp4 10.8 MB
  • 12. Topic Modeling/9. Topic Modeling Section Summary.mp4 10.3 MB
  • 18. Recurrent Neural Networks/12. RNN - Section Summary.mp4 9.5 MB
  • 12. Topic Modeling/4. LDA - Maybe Useful Picture (Optional).mp4 9.4 MB
  • 9. Spam Detection/3. Spam Detection - Exercise Prompt.mp4 9.2 MB
  • 17. Convolutional Neural Networks/9. CNN - Section Summary.mp4 8.6 MB
  • 11. Text Summarization/3. Text Summarization Exercise Prompt.mp4 8.5 MB
  • 16. Feedforward Artificial Neural Networks/14. ANN - Section Summary.mp4 8.0 MB
  • 11. Text Summarization/7. TextRank Exercise Prompt (Advanced).mp4 7.8 MB
  • 3. Vector Models and Text Preprocessing/20. Text Summarization Preview.mp4 6.6 MB
  • 16. Feedforward Artificial Neural Networks/12. CBOW Exercise Prompt.mp4 5.3 MB
  • 21. Effective Learning Strategies for Machine Learning FAQ/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt 33.2 kB
  • 7. Cipher Decryption (Advanced)/4. Genetic Algorithms.srt 29.9 kB
  • 17. Convolutional Neural Networks/6. CNN Architecture.srt 29.4 kB
  • 3. Vector Models and Text Preprocessing/14. TF-IDF (Code).srt 25.4 kB
  • 21. Effective Learning Strategies for Machine Learning FAQ/4. Machine Learning and AI Prerequisite Roadmap (pt 2).srt 24.2 kB
  • 18. Recurrent Neural Networks/6. GRU and LSTM (pt 1).srt 23.8 kB
  • 16. Feedforward Artificial Neural Networks/4. Activation Functions.srt 23.5 kB
  • 20. Extra Help With Python Coding for Beginners FAQ/1. How to Code by Yourself (part 1).srt 23.2 kB
  • 18. Recurrent Neural Networks/9. Parts-of-Speech (POS) Tagging in Tensorflow.srt 23.2 kB
  • 10. Sentiment Analysis/2. Logistic Regression Intuition (pt 1).srt 23.1 kB
  • 17. Convolutional Neural Networks/5. Convolution on Color Images.srt 21.4 kB
  • 6. Article Spinner (Intermediate)/4. Article Spinner in Python (pt 1).srt 21.2 kB
  • 17. Convolutional Neural Networks/2. What is Convolution.srt 21.2 kB
  • 7. Cipher Decryption (Advanced)/3. Language Models (Review).srt 21.0 kB
  • 16. Feedforward Artificial Neural Networks/13. CBOW in Tensorflow (Advanced).srt 20.8 kB
  • 12. Topic Modeling/5. Latent Dirichlet Allocation (LDA) - Intuition (Advanced).srt 20.7 kB
  • 19. Setting Up Your Environment FAQ/2. Anaconda Environment Setup.srt 20.6 kB
  • 3. Vector Models and Text Preprocessing/6. Tokenization.srt 20.3 kB
  • 9. Spam Detection/6. Spam Detection in Python.srt 19.7 kB
  • 3. Vector Models and Text Preprocessing/5. Count Vectorizer (Theory).srt 19.6 kB
  • 3. Vector Models and Text Preprocessing/10. Count Vectorizer (Code).srt 19.6 kB
  • 3. Vector Models and Text Preprocessing/16. How to Build TF-IDF From Scratch.srt 19.0 kB
  • 15. The Neuron/2. Fitting a Line.srt 18.8 kB
  • 3. Vector Models and Text Preprocessing/12. TF-IDF (Theory).srt 18.7 kB
  • 11. Text Summarization/8. TextRank in Python (Advanced).srt 17.5 kB
  • 9. Spam Detection/4. Aside Class Imbalance, ROC, AUC, and F1 Score (pt 1).srt 17.2 kB
  • 21. Effective Learning Strategies for Machine Learning FAQ/3. Machine Learning and AI Prerequisite Roadmap (pt 1).srt 17.0 kB
  • 5. Markov Models (Intermediate)/3. The Markov Model.srt 16.9 kB
  • 3. Vector Models and Text Preprocessing/8. Stemming and Lemmatization.srt 16.2 kB
  • 2. Getting Set Up/2. How to use Github & Extra Coding Tips (Optional).srt 16.1 kB
  • 1. Introduction/1. Introduction and Outline.srt 15.8 kB
  • 13. Latent Semantic Analysis (Latent Semantic Indexing)/2. SVD (Singular Value Decomposition) Intuition.srt 15.8 kB
  • 9. Spam Detection/2. Naive Bayes Intuition.srt 15.6 kB
  • 12. Topic Modeling/2. Latent Dirichlet Allocation (LDA) - Essentials.srt 15.5 kB
  • 3. Vector Models and Text Preprocessing/11. Vector Similarity.srt 15.5 kB
  • 18. Recurrent Neural Networks/7. GRU and LSTM (pt 2).srt 15.5 kB
  • 11. Text Summarization/4. Text Summarization in Python.srt 15.4 kB
  • 16. Feedforward Artificial Neural Networks/8. Text Preprocessing Code Preparation.srt 15.2 kB
  • 3. Vector Models and Text Preprocessing/3. What is a Vector.srt 15.2 kB
  • 3. Vector Models and Text Preprocessing/15. Word-to-Index Mapping.srt 15.2 kB
  • 19. Setting Up Your Environment FAQ/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt 15.0 kB
  • 3. Vector Models and Text Preprocessing/21. How To Do NLP In Other Languages.srt 14.9 kB
  • 21. Effective Learning Strategies for Machine Learning FAQ/1. How to Succeed in this Course (Long Version).srt 14.9 kB
  • 9. Spam Detection/5. Aside Class Imbalance, ROC, AUC, and F1 Score (pt 2).srt 14.8 kB
  • 15. The Neuron/6. How does a model learn.srt 14.7 kB
  • 20. Extra Help With Python Coding for Beginners FAQ/3. Proof that using Jupyter Notebook is the same as not using it.srt 14.7 kB
  • 3. Vector Models and Text Preprocessing/9. Stemming and Lemmatization Demo.srt 14.2 kB
  • 5. Markov Models (Intermediate)/8. Building a Text Classifier (Code pt 2).srt 14.1 kB
  • 12. Topic Modeling/7. Non-Negative Matrix Factorization (NMF) Intuition.srt 14.1 kB
  • 11. Text Summarization/6. TextRank - How It Really Works (Advanced).srt 13.8 kB
  • 3. Vector Models and Text Preprocessing/17. Neural Word Embeddings.srt 13.8 kB
  • 20. Extra Help With Python Coding for Beginners FAQ/2. How to Code by Yourself (part 2).srt 13.6 kB
  • 5. Markov Models (Intermediate)/9. Language Model (Theory).srt 13.6 kB
  • 12. Topic Modeling/6. Topic Modeling with Latent Dirichlet Allocation (LDA) in Python.srt 13.6 kB
  • 5. Markov Models (Intermediate)/11. Language Model (Code pt 1).srt 13.5 kB
  • 2. Getting Set Up/4. How to Succeed in This Course.srt 13.3 kB
  • 18. Recurrent Neural Networks/3. Simple RNN Elman Unit (pt 2).srt 13.2 kB
  • 3. Vector Models and Text Preprocessing/18. Neural Word Embeddings Demo.srt 13.0 kB
  • 15. The Neuron/5. The Neuron.srt 13.0 kB
  • 16. Feedforward Artificial Neural Networks/10. Embeddings.srt 12.9 kB
  • 16. Feedforward Artificial Neural Networks/2. Forward Propagation.srt 12.9 kB
  • 18. Recurrent Neural Networks/4. RNN Code Preparation.srt 12.9 kB
  • 6. Article Spinner (Intermediate)/5. Article Spinner in Python (pt 2).srt 12.7 kB
  • 2. Getting Set Up/1. Get Your Hands Dirty, Practical Coding Experience, Data Links.srt 12.3 kB
  • 5. Markov Models (Intermediate)/7. Building a Text Classifier (Code pt 1).srt 12.1 kB
  • 16. Feedforward Artificial Neural Networks/3. The Geometrical Picture.srt 12.1 kB
  • 15. The Neuron/4. Text Classification in Tensorflow.srt 12.0 kB
  • 10. Sentiment Analysis/6. Sentiment Analysis in Python (pt 1).srt 11.9 kB
  • 18. Recurrent Neural Networks/2. Simple RNN Elman Unit (pt 1).srt 11.8 kB
  • 16. Feedforward Artificial Neural Networks/5. Multiclass Classification.srt 11.6 kB
  • 5. Markov Models (Intermediate)/12. Language Model (Code pt 2).srt 11.6 kB
  • 11. Text Summarization/5. TextRank Intuition.srt 11.2 kB
  • 10. Sentiment Analysis/4. Logistic Regression Training and Interpretation (pt 3).srt 11.1 kB
  • 6. Article Spinner (Intermediate)/1. Article Spinning - Problem Description.srt 11.0 kB
  • 5. Markov Models (Intermediate)/4. Probability Smoothing and Log-Probabilities.srt 10.5 kB
  • 13. Latent Semantic Analysis (Latent Semantic Indexing)/3. LSA LSI Applying SVD to NLP.srt 10.5 kB
  • 18. Recurrent Neural Networks/5. RNNs Paying Attention to Shapes.srt 10.4 kB
  • 13. Latent Semantic Analysis (Latent Semantic Indexing)/4. Latent Semantic Analysis Latent Semantic Indexing in Python.srt 10.3 kB
  • 10. Sentiment Analysis/1. Sentiment Analysis - Problem Description.srt 10.1 kB
  • 17. Convolutional Neural Networks/7. CNNs for Text.srt 10.0 kB
  • 10. Sentiment Analysis/7. Sentiment Analysis in Python (pt 2).srt 10.0 kB
  • 15. The Neuron/3. Classification Code Preparation.srt 9.7 kB
  • 5. Markov Models (Intermediate)/2. The Markov Property.srt 9.7 kB
  • 5. Markov Models (Intermediate)/5. Building a Text Classifier (Theory).srt 9.7 kB
  • 16. Feedforward Artificial Neural Networks/1. ANN - Section Introduction.srt 9.5 kB
  • 7. Cipher Decryption (Advanced)/7. Code pt 2.srt 9.5 kB
  • 5. Markov Models (Intermediate)/10. Language Model (Exercise Prompt).srt 9.2 kB
  • 7. Cipher Decryption (Advanced)/10. Code pt 5.srt 9.0 kB
  • 5. Markov Models (Intermediate)/6. Building a Text Classifier (Exercise Prompt).srt 9.0 kB
  • 9. Spam Detection/1. Spam Detection - Problem Description.srt 8.9 kB
  • 16. Feedforward Artificial Neural Networks/15. Aside How to Choose Hyperparameters (Optional).srt 8.8 kB
  • 10. Sentiment Analysis/3. Multiclass Logistic Regression (pt 2).srt 8.7 kB
  • 7. Cipher Decryption (Advanced)/13. Section Conclusion.srt 8.5 kB
  • 17. Convolutional Neural Networks/4. What is Convolution (Weight Sharing).srt 8.3 kB
  • 22. Appendix FAQ Finale/2. BONUS.srt 8.1 kB
  • 8. Machine Learning Models (Introduction)/1. Machine Learning Models (Introduction).srt 8.0 kB
  • 11. Text Summarization/9. Text Summarization in Python - The Easy Way (Beginner).srt 8.0 kB
  • 6. Article Spinner (Intermediate)/3. Article Spinner Exercise Prompt.srt 7.8 kB
  • 6. Article Spinner (Intermediate)/6. Case Study Article Spinning Gone Wrong.srt 7.7 kB
  • 11. Text Summarization/1. Text Summarization Section Introduction.srt 7.7 kB
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