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Jupyter Notebook Module Not Found Error.
Jupyter Notebook Module Not Found Error. Table Of Contents: How To Solve Module Not Found Error In Jupyter Notebook? Solution: Step-1: Install Missing Dependencies. Use the below command to install missing dependencies. !pip install numpy Step-3: Check Under Which Folder Your Library Has Installed. First, you need to know under which folder your installed library is present. Run the below command to check for the library location. !pip install textblob Step-3: Check The Module Path You need to check what the paths Jupyter Notebook is looking for to find the packages and libraries. import sys print(sys.path) If your installed library
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NLP Text Preprocessing.
NLP Text Preprocessing Table Of Contents: Introduction. Lowercasing. Remove HTML Tags. Remove URLs. Remove Punctuation. Chat Word Treatment. Spelling Correction. Removing Stop Words. Handling Emojis. Tokenization. Stemming. Lemmatization. (1) What Is NLP Text Preprocessing? NLP (Natural Language Processing) text preprocessing is the initial step in the NLP pipeline, where the raw text data is transformed and prepared for further analysis and processing. The goal of text preprocessing is to clean, normalize, and transform the text data into a format that can be effectively utilized by NLP models and algorithms. (2) Lower Casing. Lowercasing is an important step in NLP text
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NLP Pipeline !
NLP Pipeline Table Of Contents: What Is NLP Pipeline? Steps Under NLP Pipeline. Data Acquisition. Text Preprocessing. Feature Engineering. Model Building. Model Evaluation. Model Deployment. (1) What Is NLP Pipeline? An NLP (Natural Language Processing) pipeline is a series of interconnected steps or modules used to process and analyze natural language data. The pipeline typically consists of several stages, each performing a specific task in the overall process of understanding and extracting useful information from unstructured text. The NLP pipeline is a set of steps to build an end-to-end NLP software. (2) Steps Under NLP Pipeline. Data Acquisition. Data Acquisition
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Introduction To NLP!
Introduction To NLP Table Of Contents: What Is Natural Language? Features OF Natural Language. Natural Language Vs. Programming Language. What Is Natural Language Processing? Fundamental Task NLP Perform: Common NLP Tasks: Techniques and Approaches: Applications Of NLP. Models Used In NLP. Specific Models In NLP. (1) What Is Natural Language? Natural Language refers to the language that humans use to communicate with each other, including spoken and written forms. It encompasses the complex and nuanced ways in which humans express themselves, including grammar, syntax, semantics, pragmatics, and phonology. Natural Language is characterized by its variability, ambiguity, and context dependency, making
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Gated Recurrent Unit (GRUs).
Gated Recurrent Units(GRUs) Table Of Contents: Disadvantages Of LSTM Networks. What Is GRU? Why We Need GRU Neural Network? (1) Disadvantages Of LSTM Networks. Computational Complexity: LSTM networks are more computationally complex compared to simpler architectures like feedforward neural networks or basic RNNs. This complexity arises due to the presence of multiple gates, memory cells, and additional parameters. As a result, training and inference can be more computationally expensive and time-consuming, especially for large-scale models and datasets. Memory Requirement: LSTM networks require more memory to store the additional parameters and memory cells. This can pose challenges when working with limited
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Maths Behind LSTM Networks.
Architecture Of LSTM Networks Table Of Contents: Architectural Diagram Of LSTM Network. (1) Architectural Diagram Of LSTM Network. (2) Inputs For LSTM Network. An LSTM network takes three inputs as its parameter. Previous Time Stamp Cell State Value. Previous Time Stamp Hidden State Value. Current Time Stamp Input State. (2) Outputs For LSTM Network. An LSTM network has two outputs. Current Cell State Value. Current Hidden State Value. (3) Core Idea Of LSTM Network. An LSTM unit is like a box whose main purpose is to store some important words and give them as an output. Which words to store
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Long Short Term Memory Network.
Long Short Term Memory Network Table Of Contents: Problem With ANN. Problem With RNN Core Ideas Behind LSTM Working Principle Of LSTM Difference Between RNN & LSTM Architecture Of LSTM Network. (1) Problem With ANN Problem-1: Sequence Is Lost The problem with ANN architecture is that its design is unable to process sequential information. When we pass sequential information to the ANN network we have to pass everything at a time. When we pass every word at a time It’s sequence will be lost in the process. We will not be able to know which word comes after what. Problem-2:
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Vanishing & Exploding Gradient Problem In RNN.
Vanishing & Exploding Gradient Table Of Contents: Vanishing & Exploding Gradient. What Is Vanishing Gradient? What Is Exploding Gradient? Solution To Vanishing & Exploding Gradient. (1) Vanishing & Exploding Gradient The vanishing and exploding gradient problems are common challenges in training recurrent neural networks (RNNs). These problems occur when the gradients used to update the network’s parameters during training become either too small (vanishing gradients) or too large (exploding gradients). This can hinder the convergence of the training process and affect the performance of the RNN model. (2) Vanishing Gradient During backpropagation, the calculation of (partial) derivatives/gradients in the weight update formula follows
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Recurrent Neural Network.
Recurrent Neural Network Table Of Contents: What Are Recurrent Neural Networks? The Architecture of a Traditional RNN. Types Of Recurrent Neural Networks. How Does Recurrent Neural Networks Work? Common Activation Functions. Advantages And Disadvantages of RNN. Recurrent Neural Network Vs Feedforward Neural Network. Backpropagation Through Time (BPTT). Two Issues Of Standard RNNs. RNN Applications. Sequence Generation. Sequence Classification. (1) What Is Recurrent Neural Network? Recurrent Neural Network(RNN) is a type of Neural Network where the output from the previous step is fed as input to the current step. In traditional neural networks, all the inputs and outputs are independent of each other.
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Why ANN Can’t Be Used In Sequential Data?
Why ANN Can’t Be Used In Sequential Data? Table Of Contents: Input Text Having Varying Size. Zero Padding – Unnecessary Computation. Prediction Problem On Different Input Length. Not Considering Sequential Information. Reason-1:Input Text Having Varying Size. In real life, input sentences will have different word counts. Suppose you make an ANN having the below structure. It has 3 input nodes. Our first sentence contains 6 words, hence the weight metrics will be 6 * 3 structure. The second sentence contains 3 words hence the weight metrics will be 3 * 3 structure. The third sentence contains 4 words hence the weight
