[FreeTutorials.Us] natural-language-processing-with-deep-learning-in-python
    
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                    - 02 Word Embeddings and Word2Vec/014 Word2Vec in Code with Numpy part 1.mp4  53.7 MB
 
                
                    - 05 Recursive Neural Networks Tree Neural Networks/038 Recursive Neural Network in Theano.mp4  52.5 MB
 
                
                    - 06 Appendix/042 How to install Numpy Scipy Matplotlib Pandas IPython Theano and TensorFlow.mp4  43.9 MB
 
                
                    - 03 Word Embeddings using GLoVe/021 GLoVe in Code - Numpy Gradient Descent.mp4  41.9 MB
 
                
                    - 04 Using Neural Networks to Solve NLP Problems/027 Parts-of-Speech Tagging Baseline.mp4  39.9 MB
 
                
                    - 04 Using Neural Networks to Solve NLP Problems/028 Parts-of-Speech Tagging Recurrent Neural Network.mp4  34.8 MB
 
                
                    - 02 Word Embeddings and Word2Vec/018 Word2Vec in Code with Theano.mp4  31.0 MB
 
                
                    - 02 Word Embeddings and Word2Vec/008 TF-IDF and t-SNE experiment.mp4  26.7 MB
 
                
                    - 06 Appendix/043 How to Code by Yourself part 1.mp4  24.5 MB
 
                
                    - 03 Word Embeddings using GLoVe/019 Recommender systems and matrix factorization tutorial.mp4  18.9 MB
 
                
                    - 02 Word Embeddings and Word2Vec/005 What is a word embedding.mp4  18.0 MB
 
                
                    - 04 Using Neural Networks to Solve NLP Problems/031 Named Entity Recognition Baseline.mp4  14.9 MB
 
                
                    - 06 Appendix/044 How to Code by Yourself part 2.mp4  14.8 MB
 
                
                    - 04 Using Neural Networks to Solve NLP Problems/029 Parts-of-Speech Tagging Hidden Markov Model HMM.mp4  14.4 MB
 
                
                    - 02 Word Embeddings and Word2Vec/012 Negative Sampling.mp4  13.5 MB
 
                
                    - 05 Recursive Neural Networks Tree Neural Networks/040 Recursive Neural Network in TensorFlow with Recursion.mp4  12.7 MB
 
                
                    - 03 Word Embeddings using GLoVe/022 GLoVe in Code - Theano Gradient Descent.mp4  12.6 MB
 
                
                    - 02 Word Embeddings and Word2Vec/004 Alternative to Wikipedia Data Brown Corpus.mp4  12.5 MB
 
                
                    - 03 Word Embeddings using GLoVe/023 GLoVe in Code - Alternating Least Squares.mp4  11.6 MB
 
                
                    - 05 Recursive Neural Networks Tree Neural Networks/034 Data Description for Recursive Neural Networks.mp4  11.5 MB
 
                
                    - 05 Recursive Neural Networks Tree Neural Networks/037 Trees to Sequences.mp4  11.2 MB
 
                
                    - 05 Recursive Neural Networks Tree Neural Networks/039 Recursive Neural Tensor Networks.mp4  11.1 MB
 
                
                    - 01 Outline Review and Logistical Things/003 How to Succeed in this Course.mp4  9.5 MB
 
                
                    - 05 Recursive Neural Networks Tree Neural Networks/035 What are Recursive Neural Networks  Tree Neural Networks TNNs.mp4  9.5 MB
 
                
                    - 02 Word Embeddings and Word2Vec/017 How to update only part of a Theano shared variable.mp4  9.2 MB
 
                
                    - 03 Word Embeddings using GLoVe/024 Visualizing country analogies with t-SNE.mp4  9.2 MB
 
                
                    - 02 Word Embeddings and Word2Vec/009 Word2Vec introduction.mp4  8.8 MB
 
                
                    - 04 Using Neural Networks to Solve NLP Problems/026 Parts-of-Speech POS Tagging.mp4  8.5 MB
 
                
                    - 05 Recursive Neural Networks Tree Neural Networks/036 Building a TNN with Recursion.mp4  8.1 MB
 
                
                    - 03 Word Embeddings using GLoVe/020 GLoVe - Global Vectors for Word Representation.mp4  7.1 MB
 
                
                    - 02 Word Embeddings and Word2Vec/007 Word analogies using word embeddings.mp4  6.6 MB
 
                
                    - 02 Word Embeddings and Word2Vec/011 Skip-Gram.mp4  6.3 MB
 
                
                    - 02 Word Embeddings and Word2Vec/015 Word2Vec in Code with Numpy part 2.mp4  6.0 MB
 
                
                    - 02 Word Embeddings and Word2Vec/016 Converting a sequence of word indexes to a sequence of word vectors.mp4  5.5 MB
 
                
                    - 04 Using Neural Networks to Solve NLP Problems/032 Named Entity Recognition RNN.mp4  5.4 MB
 
                
                    - 04 Using Neural Networks to Solve NLP Problems/030 Named Entity Recognition NER.mp4  4.9 MB
 
                
                    - 01 Outline Review and Logistical Things/001 Introduction Outline and Review.mp4  4.5 MB
 
                
                    - 02 Word Embeddings and Word2Vec/010 CBOW.mp4  4.2 MB
 
                
                    - 02 Word Embeddings and Word2Vec/006 Using pre-trained word embeddings.mp4  4.1 MB
 
                
                    - 03 Word Embeddings using GLoVe/025 Hyperparameter Challenge.mp4  4.0 MB
 
                
                    - 06 Appendix/045 BONUS Where to get Udemy coupons and FREE deep learning material.mp4  4.0 MB
 
                
                    - 04 Using Neural Networks to Solve NLP Problems/033 Hyperparameter Challenge II.mp4  3.8 MB
 
                
                    - 06 Appendix/041 How to install wp2txt or WikiExtractor.py.mp4  3.8 MB
 
                
                    - 01 Outline Review and Logistical Things/002 Where to get the code  data for this course.mp4  3.1 MB
 
                
                    - 02 Word Embeddings and Word2Vec/013 Why do I have 2 word embedding matrices and what do I do with them.mp4  2.6 MB
 
                
                    - Freetutorials.Us.url  119 bytes
 
                
                    - [FreeTutorials.Us].txt  75 bytes
 
                
            
        
     
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