منصة انسياب

اللغه الطبيعيه

-يحتوي الحلقات : 53 الدخول من الكمبيوتر و الموبايل جروب للتواصل المباشر

محتويات المقرر

#1.1 What is a Natural Language #1.2 Connected Areas #1.3 NLP is Hard #1.4 How to Make Computers Process Natural Languages #1.5 Typical Steps for NLP Application #1.6 Parts of Speech

#2.1 Need for Linguistics in NLP #2.2 Word-Related NLP Tasks #2.3 NLP Task: POS Tagging #2.4 Types Beyond Words

#3.1 Syntax #3.2 Syntactic Ambiguity #3.3 NLP Task Syntactic Parsing

لا يوجد حلقات

#5.1 Machine Learning Techniques #5.2 Training and Test Sets #5.3 Evaluation Measures #5.4 Evaluation for Multiple Classes

#6.1 Confusion Matrix #6.2 Confusion Matrix2

#7.1 What is Probability? #7.2 Multiplicative and Chain Rules #7.3 Conditional Independence #7.4 Joint Probability Distribution

#8.1 What is a Language Model (LM)? #8.2 N-Gram Language Models #8.3 Training and Testing a Language Model #8.4 Perplexity #8.5 Backoff

#9.1 The Up and Down History of Neural Networks (NNs) #9.2 Artificial Neuron A Mathematical Model #9.3 Perceptron as a Linear Separator

#10.1 A Better Activation Function #10.2 Error Minimization

#11.1 Adapting Neural Networks for Classification #11.2 Adapting Neural Networks for Classification-part2

#12.1 One-Hot Encoding #12.2 Word Embeddings #12.3 Word Embeddings from Neural Networks #12.4 Evaluation of Word Embeddings

#13.1 What is Deep Learning #13.2 Vanishing Gradients #13.3 ReLU Activation Function

#14.1 Attention and Recurrence #14.2 Multi-Head Attention #14.3 Add and Norm Component #14.4 Positional Encoding #14.5 Attention in Decoder #14.6 Linear and Softmax Layers

#15.1 BERT #15.2 Masked Language Modelling #15.3 Next Sentence Prediction #15.4 Fine-Tuning BERT #15.5 NLP Tasks