Natural Language Processing in Action
Focused View
17:24:59
65 View
00001 Part 1. Wordy machines.mp4
01:36
00002 Natural language vs. programming language.mp4
03:58
00003 The magic.mp4
05:58
00004 The math.mp4
05:45
00005 Practical applications.mp4
05:17
00006 Language through a computer s eyes.mp4
09:20
00007 A simple chatbot.mp4
09:18
00008 Another way.mp4
10:50
00009 A brief overflight of hyperspace.mp4
06:42
00010 Word order and grammar.mp4
04:28
00011 A chatbot natural language pipeline.mp4
06:25
00012 Processing in depth.mp4
06:53
00013 Natural language IQ.mp4
07:30
00014 Challenges a preview of stemming.mp4
09:29
00015 Building your vocabulary with a tokenizer Part 1.mp4
12:11
00016 Building your vocabulary with a tokenizer Part 2.mp4
10:11
00017 Dot product.mp4
04:13
00018 A token improvement.mp4
11:26
00019 Extending your vocabulary with n-grams Part 1.mp4
08:20
00020 Extending your vocabulary with n-grams Part 2.mp4
07:15
00021 Normalizing your vocabulary Part 1.mp4
09:11
00022 Normalizing your vocabulary Part 2.mp4
07:56
00023 Normalizing your vocabulary Part 3.mp4
08:53
00024 Sentiment.mp4
06:21
00025 VADER A rule-based sentiment analyzer.mp4
06:37
00026 Math with words TF-IDF vectors.mp4
04:07
00027 Bag of words.mp4
08:05
00028 Vectorizing.mp4
03:53
00029 Vector spaces.mp4
11:49
00030 Zipf s Law.mp4
05:09
00031 Topic modeling.mp4
08:31
00032 Relevance ranking.mp4
07:42
00033 Okapi BM25.mp4
04:13
00034 From word counts to topic scores.mp4
04:52
00035 TF-IDF vectors and lemmatization.mp4
09:01
00036 Thought experiment.mp4
10:44
00037 An algorithm for scoring topics.mp4
05:51
00038 An LDA classifier.mp4
09:30
00039 Latent semantic analysis.mp4
07:54
00040 Your thought experiment made real.mp4
06:55
00041 Singular value decomposition.mp4
06:42
00042 U left singular vectors.mp4
06:05
00043 SVD matrix orientation.mp4
06:26
00044 Principal component analysis.mp4
07:36
00045 Stop horsing around and get back to NLP.mp4
09:22
00046 Using truncated SVD for SMS message semantic analysis.mp4
10:10
00047 Latent Dirichlet allocation LDiA.mp4
10:28
00048 LDiA topic model for SMS messages.mp4
12:26
00049 Distance and similarity.mp4
07:25
00050 Steering with feedback.mp4
06:05
00051 Topic vector power.mp4
04:46
00052 Semantic search.mp4
08:37
00053 Part 2. Deeper learning neural networks.mp4
02:37
00054 Neural networks the ingredient list.mp4
08:54
00055 Detour through bias Part 1.mp4
09:54
00056 Detour through bias Part 2.mp4
08:47
00057 Detour through bias Part 3.mp4
11:00
00058 Let s go skiing the error surface.mp4
07:56
00059 Keras - Neural networks in Python.mp4
08:53
00060 Semantic queries and analogies.mp4
07:13
00061 Word vectors.mp4
10:04
00062 Vector-oriented reasoning.mp4
07:12
00063 How to compute Word2vec representations Part 1.mp4
09:31
00064 How to compute Word2vec representations Part 2.mp4
08:37
00065 How to use the gensim.word2vec module.mp4
05:08
00066 How to generate your own word vector representations.mp4
07:15
00067 fastText.mp4
05:42
00068 Visualizing word relationships.mp4
10:29
00069 Unnatural words.mp4
05:53
00070 Learning meaning.mp4
07:52
00071 Toolkit.mp4
03:02
00072 Convolutional neural nets.mp4
06:41
00073 Padding.mp4
05:43
00074 Narrow windows indeed.mp4
03:22
00075 Implementation in Keras - prepping the data.mp4
08:14
00076 Convolutional neural network architecture.mp4
09:34
00077 The cherry on the sundae.mp4
09:04
00078 Using the model in a pipeline.mp4
06:51
00079 Loopy recurrent neural networks RNNs.mp4
03:59
00080 Remembering with recurrent networks.mp4
08:03
00081 Backpropagation through time.mp4
09:30
00082 Recap.mp4
10:06
00083 Putting things together.mp4
05:29
00084 Hyperparameters.mp4
05:30
00085 Predicting.mp4
08:16
00086 LSTM Part 1.mp4
09:47
00087 LSTM Part 2.mp4
08:37
00088 Backpropagation through time.mp4
09:31
00089 Back to the dirty data.mp4
09:09
00090 My turn to chat.mp4
03:54
00091 My turn to speak more clearly.mp4
10:56
00092 Learned how to say but not yet what.mp4
05:10
00093 Encoder-decoder architecture.mp4
04:57
00094 Decoding thought.mp4
05:19
00095 Look familiar.mp4
08:06
00096 Assembling a sequence-to-sequence pipeline.mp4
05:06
00097 Sequence encoder.mp4
05:51
00098 Training the sequence-to-sequence network.mp4
03:52
00099 Building a chatbot using sequence-to-sequence networks.mp4
07:36
00100 Enhancements.mp4
05:12
00101 In the real world.mp4
05:31
00102 Part 3. Getting real real-world NLP challenges.mp4
00:55
00103 Named entities and relations.mp4
03:00
00104 A knowledge base.mp4
09:10
00105 Regular patterns.mp4
08:53
00106 Information worth extracting.mp4
02:46
00107 Extracting dates.mp4
09:11
00108 Extracting relationships relations.mp4
09:36
00109 Relation normalization and extraction.mp4
08:12
00110 Why won t split . work.mp4
08:35
00111 Language skill.mp4
06:40
00112 Modern approaches Part 1.mp4
09:35
00113 Modern approaches Part 2.mp4
09:16
00114 Pattern-matching approach.mp4
05:13
00115 A pattern-matching chatbot with AIML Part 1.mp4
04:01
00116 A pattern-matching chatbot with AIML Part 2.mp4
09:46
00117 Grounding.mp4
06:31
00118 Retrieval search.mp4
08:18
00119 Example retrieval-based chatbot.mp4
10:21
00120 Generative models.mp4
08:02
00121 Four-wheel drive.mp4
03:19
00122 Design process.mp4
05:38
00123 Trickery.mp4
09:04
00124 Too much of a good thing data.mp4
03:14
00125 Optimizing NLP algorithms.mp4
06:45
00126 Advanced indexing.mp4
07:06
00127 Advanced indexing with Annoy.mp4
05:17
00128 Why use approximate indexes at all.mp4
05:50
00129 Constant RAM algorithms.mp4
05:34
00130 Parallelizing your NLP computations.mp4
08:28
00131 Reducing the memory footprint during model training.mp4
07:27
00132 Anaconda3.mp4
07:05
00133 Mac.mp4
07:19
00134 Working with strings.mp4
05:59
00135 Regular expressions.mp4
09:18
00136 Vectors.mp4
08:06
00137 Distances Part 2.mp4
04:49
00138 Data selection and avoiding bias.mp4
06:50
00139 Knowing is half the battle.mp4
06:36
00140 Holding your model back.mp4
04:22
00141 Imbalanced training sets.mp4
06:11
00142 Performance metrics.mp4
09:08
00143 High-dimensional vectors are different.mp4
04:55
00144 High-dimensional thinking.mp4
08:42
00145 High-dimensional indexing.mp4
04:34
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View courses Manning PublicationsManning Publications is an American publisher specializing in content relating to computers. Manning mainly publishes textbooks but also release videos and projects for professionals within the computing world.
- language english
- Training sessions 145
- duration 17:24:59
- Release Date 2023/11/06
