MuerBT磁力搜索 BT种子搜索利器 免费下载BT种子,超5000万条种子数据

[FreeCourseSite.com] Udemy - Machine Learning Natural Language Processing in Python (V2)

磁力链接/BT种子名称

[FreeCourseSite.com] Udemy - Machine Learning Natural Language Processing in Python (V2)

磁力链接/BT种子简介

种子哈希:b08f8f6452aa500e439368ca45df746b7e2962a8
文件大小: 6.67G
已经下载:1550次
下载速度:极快
收录时间:2024-04-14
最近下载:2025-07-18

移花宫入口

移花宫.com邀月.com怜星.com花无缺.comyhgbt.icuyhgbt.top

磁力链接下载

magnet:?xt=urn:btih:B08F8F6452AA500E439368CA45DF746B7E2962A8
推荐使用PIKPAK网盘下载资源,10TB超大空间,不限制资源,无限次数离线下载,视频在线观看

下载BT种子文件

磁力链接 迅雷下载 PIKPAK在线播放 世界之窗 91视频 含羞草 欲漫涩 逼哩逼哩 成人快手 51品茶 抖阴破解版 极乐禁地 91短视频 TikTok成人版 PornHub 草榴社区 哆哔涩漫 呦乐园 萝莉岛

最近搜索

妈妈勾引 ero 瑜伽合集 高潮 颤抖 有女初长成 2012 cd rachael 2025年6月最新,换妻界顶流大神, 强奸伦 电影 s级尤物反差婊 推特骚妻 hibiki otsuki 家庭乱伦 abw-293 裸舞 啪 抱插 mkmp-110 rachael 抖音 熟女 女友反差 白妖妖 ssis 萝莉学妹 おきた 元歌恋爱日记 露脸 流出 抽搐 极品浪货 lena paul blacked

文件列表

  • 18. Recurrent Neural Networks/9. Parts-of-Speech (POS) Tagging in Tensorflow.mp4 152.1 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 82.0 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 60.3 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 51.5 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 48.1 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 47.5 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 42.9 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 42.4 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 40.8 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 18.0 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
  • 13. Latent Semantic Analysis (Latent Semantic Indexing)/5. LSA LSI Exercises.srt 7.5 kB
  • 11. Text Summarization/2. Text Summarization Using Vectors.srt 7.5 kB
  • 7. Cipher Decryption (Advanced)/11. Code pt 6.srt 7.4 kB
  • 1. Introduction/2. Are You Beginner, Intermediate, or Advanced All are OK!.srt 7.4 kB
  • 17. Convolutional Neural Networks/3. What is Convolution (Pattern Matching).srt 7.1 kB
  • 14. Deep Learning (Introduction)/1. Deep Learning Introduction (Intermediate-Advanced).srt 6.9 kB
  • 7. Cipher Decryption (Advanced)/5. Code Preparation.srt 6.9 kB
  • 7. Cipher Decryption (Advanced)/1. Section Introduction.srt 6.8 kB
  • 3. Vector Models and Text Preprocessing/2. Basic Definitions for NLP.srt 6.8 kB
  • 19. Setting Up Your Environment FAQ/1. Pre-Installation Check.srt 6.8 kB
  • 18. Recurrent Neural Networks/1. RNN - Section Introduction.srt 6.5 kB
  • 3. Vector Models and Text Preprocessing/7. Stopwords.srt 6.5 kB
  • 16. Feedforward Artificial Neural Networks/9. Text Preprocessing in Tensorflow.srt 6.4 kB
  • 4. Probabilistic Models (Introduction)/1. Probabilistic Models (Introduction).srt 6.4 kB
  • 7. Cipher Decryption (Advanced)/8. Code pt 3.srt 6.3 kB
  • 18. Recurrent Neural Networks/10. Named Entity Recognition (NER) in Tensorflow.srt 6.3 kB
  • 16. Feedforward Artificial Neural Networks/6. ANN Code Preparation.srt 6.1 kB
  • 17. Convolutional Neural Networks/1. CNN - Section Introduction.srt 6.1 kB
  • 18. Recurrent Neural Networks/8. RNN for Text Classification in Tensorflow.srt 5.7 kB
  • 16. Feedforward Artificial Neural Networks/11. CBOW (Advanced).srt 5.4 kB
  • 6. Article Spinner (Intermediate)/2. Article Spinning - N-Gram Approach.srt 5.3 kB
  • 13. Latent Semantic Analysis (Latent Semantic Indexing)/1. LSA LSI Section Introduction.srt 5.3 kB
  • 10. Sentiment Analysis/5. Sentiment Analysis - Exercise Prompt.srt 5.2 kB
  • 16. Feedforward Artificial Neural Networks/7. Text Classification ANN in Tensorflow.srt 5.2 kB
  • 17. Convolutional Neural Networks/8. Convolutional Neural Network for NLP in Tensorflow.srt 5.1 kB
  • 3. Vector Models and Text Preprocessing/1. Vector Models & Text Preprocessing Intro.srt 5.1 kB
  • 12. Topic Modeling/3. LDA - Code Preparation.srt 5.0 kB
  • 12. Topic Modeling/8. Topic Modeling with Non-Negative Matrix Factorization (NMF) in Python.srt 5.0 kB
  • 7. Cipher Decryption (Advanced)/9. Code pt 4.srt 5.0 kB
  • 7. Cipher Decryption (Advanced)/2. Ciphers.srt 4.9 kB
  • 3. Vector Models and Text Preprocessing/19. Vector Models & Text Preprocessing Summary.srt 4.9 kB
  • 3. Vector Models and Text Preprocessing/22. Suggestion Box.srt 4.9 kB
  • 11. Text Summarization/10. Text Summarization Section Summary.srt 4.5 kB
  • 2. Getting Set Up/3. Where to get the code, notebooks, and data.srt 4.4 kB
  • 7. Cipher Decryption (Advanced)/6. Code pt 1.srt 4.2 kB
  • 18. Recurrent Neural Networks/11. Exercise Return to CNNs (Advanced).srt 4.2 kB
  • 12. Topic Modeling/1. Topic Modeling Section Introduction.srt 4.2 kB
  • 7. Cipher Decryption (Advanced)/12. Cipher Decryption - Additional Discussion.srt 4.2 kB
  • 5. Markov Models (Intermediate)/13. Markov Models Section Summary.srt 4.1 kB
  • 22. Appendix FAQ Finale/1. What is the Appendix.srt 3.9 kB
  • 2. Getting Set Up/5. Temporary 403 Errors.srt 3.8 kB
  • 5. Markov Models (Intermediate)/1. Markov Models Section Introduction.srt 3.5 kB
  • 3. Vector Models and Text Preprocessing/13. (Interactive) Recommender Exercise Prompt.srt 3.3 kB
  • 3. Vector Models and Text Preprocessing/4. Bag of Words.srt 3.2 kB
  • 15. The Neuron/1. The Neuron - Section Introduction.srt 3.0 kB
  • 9. Spam Detection/3. Spam Detection - Exercise Prompt.srt 2.7 kB
  • 12. Topic Modeling/4. LDA - Maybe Useful Picture (Optional).srt 2.6 kB
  • 18. Recurrent Neural Networks/12. RNN - Section Summary.srt 2.4 kB
  • 11. Text Summarization/3. Text Summarization Exercise Prompt.srt 2.4 kB
  • 15. The Neuron/7. The Neuron - Section Summary.srt 2.2 kB
  • 12. Topic Modeling/9. Topic Modeling Section Summary.srt 2.0 kB
  • 16. Feedforward Artificial Neural Networks/14. ANN - Section Summary.srt 2.0 kB
  • 11. Text Summarization/7. TextRank Exercise Prompt (Advanced).srt 1.8 kB
  • 3. Vector Models and Text Preprocessing/20. Text Summarization Preview.srt 1.7 kB
  • 17. Convolutional Neural Networks/9. CNN - Section Summary.srt 1.7 kB
  • 16. Feedforward Artificial Neural Networks/12. CBOW Exercise Prompt.srt 970 Bytes
  • 2. Getting Set Up/1.1 Data Links.html 157 Bytes
  • 2. Getting Set Up/3.2 Data Links.html 157 Bytes
  • 2. Getting Set Up/1.2 Github Link.html 139 Bytes
  • 2. Getting Set Up/3.3 Github Link.html 139 Bytes
  • 0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
  • 2. Getting Set Up/3.1 Code Link.html 125 Bytes
  • 0. Websites you may like/[CourseClub.Me].url 122 Bytes
  • 0. Websites you may like/[GigaCourse.Com].url 49 Bytes

随机展示

相关说明

本站不存储任何资源内容,只收集BT种子元数据(例如文件名和文件大小)和磁力链接(BT种子标识符),并提供查询服务,是一个完全合法的搜索引擎系统。 网站不提供种子下载服务,用户可以通过第三方链接或磁力链接获取到相关的种子资源。本站也不对BT种子真实性及合法性负责,请用户注意甄别!