00:00 Learn about sequence models for speech recognition, music generation, DNA sequence analysis, and more. 06:02 Described notation for sequence data training set 18:40 Recurrent neural networks use parameters to make predictions based on previous inputs. 23:45 Recurrent Neural Networks (RNNs) can be simplified by compressing parameter matrices into one. 35:00 RNN architectures can be modified to handle varying input and output lengths. 40:36 Different types of RNN architectures 51:30 Training a language model using an RNN 56:57 Generate novel sequences of words or characters using RNN language models 1:07:39 Vanishing gradients are a weakness of basic RNNs, but can be addressed with GRUs. 1:13:04 The GRU unit has a memory cell and an activation value, and uses a gate to decide when to update the memory cell. 1:23:55 GRU is a type of RNN that enables capturing long-range dependencies 1:29:13 LSTM has three gates instead of two 1:40:33 Bi-directional RNN allows predictions anywhere in the sequence 1:46:16 Deep RNNs are computationally expensive to train 1:57:12 Word embeddings are high dimensional feature vectors that allow algorithms to quickly figure out similarities between words. 2:02:33 Transfer learning using word embeddings 2:13:12 Analogical reasoning using word embeddings can be carried out by finding the word that maximizes similarity. 2:19:35 Word embeddings can learn analogy relationships and use cosine similarity to measure similarity. 2:30:19 Building a neural network to predict the next word in a sequence 2:35:45 Learning word embeddings using different contexts 2:46:30 Using hierarchical softmax can speed up the softmax classification 2:51:44 Negative sampling is a modified learning problem that allows for more efficient learning of word embeddings. 3:02:38 The GloVe algorithm learns word vectors based on co-occurrence counts. 3:08:16 GloVe algorithm simplifies word embedding learning 3:18:56 Sentiment classification using RNNs 3:24:27 Reducing bias in word embeddings 3:35:44 Neural networks can be trained to translate languages and caption images 3:41:31 Conditional language model for machine translation 3:52:29 Using a neural network to evaluate the probability of the second word given the input sentence and the first word 3:58:07 Beam search algorithm with 3 copies of the network can efficiently evaluate all possible outputs 4:09:20 Beam search is a heuristic search algorithm used in production systems. 4:14:46 Error analysis process for improving machine translation 4:25:58 Modified precision measure can be used to evaluate machine translation output. 4:31:52 The Blue Score is a useful single evaluation metric for machine translation and text generation systems. 4:43:32 Attention model allows neural network to focus on specific parts of input sentence. 4:49:01 Generating translations using attention weights 5:00:31 Speech recognition using end-to-end deep learning 5:06:11 CTC cost function allows for collapsing repeated characters and inserting blank characters in speech recognition models. 5:17:31 Self-attention and multi-headed attention are key ideas in transformer networks. 5:23:24 Self-attention mechanism computes richer, more useful word representations. 5:35:11 Multi-head attention mechanism allows asking multiple questions for every word. 5:41:01 The Transformer architecture uses encoder and decoder blocks to perform sequence-to-sequence translation tasks. 5:52:42 Deep learning is a superpower
Andrew is the only man on earth can explain toughest concepts like a story by having same shirt,mic and the same way of teaching , He is legend . People like him should be celebrated more than fking movie and others.
The best thing about Andrew NG sir's lectures is that he explains the intuition behind something in the most clear, reasonable and ordered way, arms you with the understanding to expand your thinking yourself. His lectures have become prereuisite to any AI/ML concept for me🙂. Thank you so much sir..🤗
0:00: 🔑 Importance of Sequence Models in Speech Recognition and Music Generation 24:17: 🧠 Explanation of forward propagation in neural networks simplified for better understanding. 47:59: 📝 Importance of End of Sentence Token in Natural Language Processing 1:10:55: 🧠 Effective solution for vanishing gradient problem in neural networks using GRU. 1:34:52: 🧠 Explanation of the Long Short-Term Memory (LSTM) unit in neural networks. 1:58:24: 📚 Learning word embeddings using high-dimensional feature vectors improves representation of words for better generalization in algorithms. 2:22:19: 🔑 Word embeddings can learn relationships between words based on large text corpus, aiding in analogy reasoning and similarity measurement. 2:45:27: ⚙ Neural network model using embedding vectors and softmax unit for word prediction faces computational speed issues. 3:09:02: 🔑 Weighting factor function f of x i j assigns meaningful computation to frequent and infrequent words in co-occurrence analysis. 3:32:24: 📝 Algorithm for gender bias neutralization using a linear classifier on definitional words and hand-picked pairs. 3:56:21: ⚙ Beam search narrows down possibilities by evaluating word probabilities, selecting the top three choices. 4:19:58: ⚙ Error analysis process for sequence models involves attributing errors to beam search or RNN model to optimize performance. 4:44:22: ⚙ Attention mechanism in RNN units determines context importance for word generation. 5:07:59: ⚙ Utilizing blank characters and repetition allows neural networks to represent short outputs effectively. 5:32:25: 💡 Illustration of how query and key vectors are used to represent words in a sequence through self-attention computation. Recap by Tammy AI
multi-headed attention (at 05:33:57) .. Andrew explained that we have to. multiply W^Q with q ...But in self attention , q = W^Q * x ... which one of these two is correct ?
![Seungmin "그렇게, 천천히, 우리(As we are)" | [Stray Kids : SKZ-PLAYER]](http://i.ytimg.com/vi/kAzmhLHePqU/mqdefault.jpg)
00:00 Learn about sequence models for speech recognition, music generation, DNA sequence analysis, and more.
06:02 Described notation for sequence data training set
18:40 Recurrent neural networks use parameters to make predictions based on previous inputs.
23:45 Recurrent Neural Networks (RNNs) can be simplified by compressing parameter matrices into one.
35:00 RNN architectures can be modified to handle varying input and output lengths.
40:36 Different types of RNN architectures
51:30 Training a language model using an RNN
56:57 Generate novel sequences of words or characters using RNN language models
1:07:39 Vanishing gradients are a weakness of basic RNNs, but can be addressed with GRUs.
1:13:04 The GRU unit has a memory cell and an activation value, and uses a gate to decide when to update the memory cell.
1:23:55 GRU is a type of RNN that enables capturing long-range dependencies
1:29:13 LSTM has three gates instead of two
1:40:33 Bi-directional RNN allows predictions anywhere in the sequence
1:46:16 Deep RNNs are computationally expensive to train
1:57:12 Word embeddings are high dimensional feature vectors that allow algorithms to quickly figure out similarities between words.
2:02:33 Transfer learning using word embeddings
2:13:12 Analogical reasoning using word embeddings can be carried out by finding the word that maximizes similarity.
2:19:35 Word embeddings can learn analogy relationships and use cosine similarity to measure similarity.
2:30:19 Building a neural network to predict the next word in a sequence
2:35:45 Learning word embeddings using different contexts
2:46:30 Using hierarchical softmax can speed up the softmax classification
2:51:44 Negative sampling is a modified learning problem that allows for more efficient learning of word embeddings.
3:02:38 The GloVe algorithm learns word vectors based on co-occurrence counts.
3:08:16 GloVe algorithm simplifies word embedding learning
3:18:56 Sentiment classification using RNNs
3:24:27 Reducing bias in word embeddings
3:35:44 Neural networks can be trained to translate languages and caption images
3:41:31 Conditional language model for machine translation
3:52:29 Using a neural network to evaluate the probability of the second word given the input sentence and the first word
3:58:07 Beam search algorithm with 3 copies of the network can efficiently evaluate all possible outputs
4:09:20 Beam search is a heuristic search algorithm used in production systems.
4:14:46 Error analysis process for improving machine translation
4:25:58 Modified precision measure can be used to evaluate machine translation output.
4:31:52 The Blue Score is a useful single evaluation metric for machine translation and text generation systems.
4:43:32 Attention model allows neural network to focus on specific parts of input sentence.
4:49:01 Generating translations using attention weights
5:00:31 Speech recognition using end-to-end deep learning
5:06:11 CTC cost function allows for collapsing repeated characters and inserting blank characters in speech recognition models.
5:17:31 Self-attention and multi-headed attention are key ideas in transformer networks.
5:23:24 Self-attention mechanism computes richer, more useful word representations.
5:35:11 Multi-head attention mechanism allows asking multiple questions for every word.
5:41:01 The Transformer architecture uses encoder and decoder blocks to perform sequence-to-sequence translation tasks.
5:52:42 Deep learning is a superpower
Nice work thanks 🙏
king
🙏🙏
Thanks Akhi
Andrew is the only man on earth can explain toughest concepts like a story by having same shirt,mic and the same way of teaching , He is legend . People like him should be celebrated more than fking movie and others.
Completely agree .. I tried going through so many videos but always fall back to his.. I am just such a fan of his.
The best thing about Andrew NG sir's lectures is that he explains the intuition behind something in the most clear, reasonable and ordered way, arms you with the understanding to expand your thinking yourself. His lectures have become prereuisite to any AI/ML concept for me🙂.
Thank you so much sir..🤗
Love Andrew's lectures
@25:02 for a calculation, Waa is multiplied by a not with a.
0:00: 🔑 Importance of Sequence Models in Speech Recognition and Music Generation
24:17: 🧠 Explanation of forward propagation in neural networks simplified for better understanding.
47:59: 📝 Importance of End of Sentence Token in Natural Language Processing
1:10:55: 🧠 Effective solution for vanishing gradient problem in neural networks using GRU.
1:34:52: 🧠 Explanation of the Long Short-Term Memory (LSTM) unit in neural networks.
1:58:24: 📚 Learning word embeddings using high-dimensional feature vectors improves representation of words for better generalization in algorithms.
2:22:19: 🔑 Word embeddings can learn relationships between words based on large text corpus, aiding in analogy reasoning and similarity measurement.
2:45:27: ⚙ Neural network model using embedding vectors and softmax unit for word prediction faces computational speed issues.
3:09:02: 🔑 Weighting factor function f of x i j assigns meaningful computation to frequent and infrequent words in co-occurrence analysis.
3:32:24: 📝 Algorithm for gender bias neutralization using a linear classifier on definitional words and hand-picked pairs.
3:56:21: ⚙ Beam search narrows down possibilities by evaluating word probabilities, selecting the top three choices.
4:19:58: ⚙ Error analysis process for sequence models involves attributing errors to beam search or RNN model to optimize performance.
4:44:22: ⚙ Attention mechanism in RNN units determines context importance for word generation.
5:07:59: ⚙ Utilizing blank characters and repetition allows neural networks to represent short outputs effectively.
5:32:25: 💡 Illustration of how query and key vectors are used to represent words in a sequence through self-attention computation.
Recap by Tammy AI
You are always the best, sir. Big Thanks!
You are the 🐐 mr NG
the only one in the hall internet that knew how to explain the transformer model in the rite way
The best explanation of NLP in one video...
thank you andrew making this wonderfull course
i feel like andrew deep learning is only thing require to become better than good in deep learning
Thanks a lot bro. I was unable to complete my sequence model course on coursera and it expired. Thank god you uploaded
finally gonna pass my nlp exam due to this absolute legend
You are amazing
Awesome
Thx for the reup!
It's so good lecture
If you are not familairt with that kind of concept, dont worry about it!!!
Such a wanderful series Dr Ng. Thank you from a AI university teacher.
1:26:03 (GRU Relevance gate)
thanks
multi-headed attention (at 05:33:57) .. Andrew explained that we have to. multiply W^Q with q ...But in self attention , q = W^Q * x ... which one of these two is correct ?
@@samedbey3548 ...thank you... after getting q , there is one more transformation W1^Q*q ??
Rama Rama Mahabahu
@@smokinghighnotesitna marunga
1:36:46 (LSTM MCQ)
The first French I’m learning is in this video
Can use seq2seq model for spell correction sir?
Yes
@3:05:41 correction in subscript Xij; where i = t and j = c
🐐
2:39:46
before starting the video , should i need to learn CNN?
no
Who else is here after their mind got blown by stable diffusion?
Thompson William Anderson Steven White Susan