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AI_Python_Support_Ticket_Classification

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python
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DeepLearning or NLP
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Machine Learning
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I'm struggling with the concept of recursion in the context of parsing context-free grammars. I've read the literature but I'm still not getting it. Can you help?
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I'm struggling with the proof of the Pythagorean theorem in Euclidean space. Can you guide me through the steps?
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I'm struggling with the proof of the Pythagorean theorem in my advanced Euclidean geometry course. I've read through the textbook and notes, but I'm still not grasping the logical steps. Could you help me understand it better?
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I'm experiencing a '404 Not Found' error when trying to access a specific API endpoint. This only happens on our production server, not in our development environment. I've checked the API documentation and the endpoint seems correct, but I can't figure out why it's not working. Any suggestions?
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I'm struggling to understand the concept of decorators in Python. I've read the official documentation, but it's still not clicking. Can you provide a simple, real-world example?
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I'm struggling with the mathematical foundations of deep learning. Specifically, I don't understand how backpropagation works with continuous and discrete random variables in the context of Bayesian neural networks.
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I'm having trouble understanding the concept of backpropagation in neural networks and how it's used to optimize the weights. Could you provide a clear explanation or some resources? I've tried looking up tutorials, but they're either too basic or too complex for my current understanding.
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I'm having trouble implementing the Adam optimizer in my deep neural network for sentiment analysis. The loss function is not decreasing as expected and I'm not sure if my learning rate is optimal.
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I'm struggling to understand the mathematics behind the backpropagation algorithm in deep learning. Can you guide me through the calculus involved?
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I'm struggling to understand the concept of backpropagation in neural networks. Could you provide a step-by-step explanation or a resource that breaks it down?
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I need help with my Python code
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I'm struggling to understand the garbage collection process in Python's CPython implementation. I've read the docs but it's still not clicking. Can someone explain it like I'm five?
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I'm struggling to comprehend the nuances of backpropagation in neural networks and how it relates to the vanishing gradient problem. I've read the relevant chapters in Rumelhart and McClelland's 'Parallel Distributed Processing', but I'm still having trouble grasping the concept. Can you provide a more intuitive explanation or perhaps a real-world analogy?
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I'm struggling with the backpropagation algorithm in my deep learning project. I've tried adjusting the learning rate and optimizing the network architecture, but I'm still not seeing any improvement in my model's performance. Could you provide some insights or suggest alternative approaches?
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I'm struggling with the vanishing gradient problem in my deep neural network. I've tried various techniques like ReLU and LSTM, but the gradients still vanish during backpropagation. Any suggestions?
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I'm struggling to understand the concept of backpropagation in neural networks. The math involved is quite complex and I'm having trouble grasping it. I've read several tutorials but they seem to gloss over the details. Can you provide a more in-depth explanation?
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I'm struggling to understand the intricacies of the Bellman-Ford algorithm for solving single-source shortest path problems in directed graphs. I've tried implementing it in Python, but I'm not getting the correct results. Can you help?
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I'm trying to implement a Variational Autoencoder for generating new samples in a high-dimensional space. However, I'm stuck on how to choose the right encoder and decoder architectures. Any suggestions?
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I'm struggling with implementing a Long Short-Term Memory (LSTM) network for sentiment analysis in Python using Keras. I've tried adjusting the number of epochs and layers, but the accuracy is not improving.
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I'm struggling to implement the backpropagation algorithm for my deep learning model. The gradients are not updating as expected and I'm not sure if my initial weights are causing the issue.
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I'm struggling with the exploding gradient problem during backpropagation in my deep neural network. I've tried various techniques like gradient clipping and reducing learning rate, but the issue persists. Any insights on how to tackle this would be greatly appreciated.
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I'm having trouble understanding the concept of derivative in my math homework, it's not making sense.
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I'm struggling with the concept of limits and how they relate to derivatives in calculus. Can you provide a clear explanation or a resource that breaks it down?
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I've been trying to implement a generative adversarial network (GAN) for image synthesis, but I'm encountering issues with mode collapse. I've tried adjusting the learning rate and the architecture, but nothing seems to help. Any suggestions?
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I'm having trouble with the regularization parameter in my SVM model. I've tried adjusting C, but it's not improving my cross-validation score. Any suggestions?
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I'm experiencing difficulties with the sentiment analysis model. It's not accurately predicting the emotional tone of text, particularly for sarcastic or ironical statements. Could you guide me on how to improve its performance?
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I'm having trouble understanding the concept of word embeddings in the context of deep learning. I've read the papers by Mikolov et al. and LeCun, but I'm still struggling to grasp how they're used in neural networks for tasks like sentiment analysis.
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My AI model is struggling with overfitting. I've tried regularization techniques, but the performance on the validation set is still deteriorating. Any suggestions?
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I'm struggling with the concept of gradient descent in neural networks. The math behind it is giving me a hard time, especially when it comes to updating the weights and biases. I've tried looking up resources online, but they seem to gloss over the mathematical details. Could you guide me through the process step by step?
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I'm having trouble understanding the concept of gradient descent in the context of backpropagation for neural networks. I've been trying to grasp it through online tutorials and textbooks, but I'm still struggling with the mathematical intuition behind it. Could you guide me through it?
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I'm having trouble understanding the concept of 'recurrent neural networks' for my NLP project. Could you point me to some resources or explain it in a different way?
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I'm struggling with the concept of limits in calculus. I've read the textbook but I'm still not grasping it. Can you help?
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I'm struggling with the proof by induction step in my abstract algebra course. I've tried applying it to the sum of the first n natural numbers, but I'm getting stuck at the inductive step.
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I'm having trouble understanding the concept of vanishing gradients in deep neural networks and how it affects my model's performance on the MNIST dataset. I've tried various optimizers but the problem persists. Could you guide me on how to address this issue?
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I need help with my machine learning model
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I'm struggling with the concept of derivatives in calculus. Can you help me understand the chain rule better?
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I'm having trouble understanding the concept of derivatives in calculus. I've read the textbook and watched some videos, but it's still not clicking. Can you help?
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I'm struggling to understand the intricacies of the Long Short-Term Memory (LSTM) architecture in the context of sequence-to-sequence models for machine translation. I've read through the relevant papers by Graves and Hochreiter, but I'm still having difficulty grasping how the forget gate works in practice. Could you provide some intuitive explanation or suggest any resources that could help?
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I'm struggling with the implementation of a genetic algorithm to optimize a neural network's architecture for image classification tasks. The fitness function isn't improving, and I'm not sure if my crossover and mutation rates are optimal.
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I'm having trouble understanding the concept of 'attention mechanisms' in the Transformer model as described in the 'Attention is All You Need' paper by Vaswani et al. Could you provide a more intuitive explanation or suggest some resources for further learning?
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I'm struggling with the concept of backpropagation in neural networks. Can you guide me through the process step by step?
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I'm struggling to understand the implications of the vanishing gradient problem in deep learning. I've read about it, but I can't seem to grasp how it affects the training process when using different activation functions. Could you provide some intuition or point me to a resource that explains it in a more accessible way?
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I'm struggling to understand the implications of the vanishing gradient problem in deep learning, specifically in the context of recurrent neural networks for sequence labeling tasks. The literature seems to suggest that it's a significant issue, but I'm not sure how it affects the interpretability of my models. Could you provide some insights?
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I'm having trouble understanding the difference between supervised and unsupervised learning in the context of deep learning for natural language processing. Can you provide a clear explanation with examples?
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What is the capital of France?
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Write a text classification task
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I'm struggling to implement the Adam optimizer in TensorFlow for my Generative Adversarial Network (GAN) model. I've tried adjusting the learning rate and batch size, but the model isn't converging. I've also tried different initializers for the weights, but nothing seems to help. Any suggestions?
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I'm struggling with the vanishing gradient problem in my deep neural network. I've tried various optimizers, but the gradients seem to be disappearing. Any suggestions?
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I'm struggling with the concept of gradient descent in neural networks and its application in deep learning models. Can you provide a detailed explanation or any resources that could help?
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The Eiffel Tower is a symbol of love
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I'm struggling to understand the mathematical foundations behind gradient descent in neural networks. I've read Rumelhart's original paper, but I'm still having trouble grasping the intuition behind it. Can you provide some insights?
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I'm struggling to understand the difference between word embeddings and sentence embeddings in the context of BERT. Can you provide a clear explanation with examples?
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I'm struggling to understand the concept of attention mechanisms in transformers and how they differ from traditional sequence-to-sequence models. Can you provide a detailed explanation with examples?
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In the context of abstract algebra, prove that the center of a finite group is a normal subgroup.
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I'm struggling with the concept of a non-Euclidean manifold in the context of string theory. I've read Penrose's 'The Road to Reality' and Witten's lectures, but I'm still having trouble grasping the mathematical intuition behind it.
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I'm struggling to understand the mathematical formalism behind the chain rule in backpropagation for deep learning. Could you provide a detailed explanation with examples?
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I'm struggling with the vanishing gradient problem in my recurrent neural network. I've tried various optimizers and architectures, but the gradients seem to disappear after a few layers. Any suggestions?
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I'm struggling with the concept of measure theory in the context of stochastic processes. I've read chapter 7 of 'Probability with Martingales' by David Williams, but I'm still having trouble understanding how to apply it to my research on Brownian motion.
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I'm struggling to understand the concept of metric spaces in the context of functional analysis. I've read the definition and examples, but I'm having trouble seeing how this applies to the study of operators on Banach spaces. Can you provide a clear explanation or a specific example that illustrates this?
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I need help with understanding Python syntax
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I'm struggling to understand the concept of backpropagation in neural networks. Could you provide a step-by-step explanation, perhaps using an example from natural language processing?
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The customer's text specified by the classification task.
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I've been trying to implement a Generative Adversarial Network (GAN) for image synthesis, but I'm struggling with mode collapse. I've tried various techniques like using different architectures, changing the loss function, and even tried adding noise to the generator's input, but nothing seems to work. I'm at my wits' end here. Any suggestions?
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I'm struggling with the Cauchy-Schwarz Inequality in a multivariate context. I've tried applying it in n-dimensional space, but my results are inconsistent.
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I'm struggling with understanding the integration of a trigonometric function in polar coordinates. The problem involves a complex limit involving the unit circle, and I'm not sure how to proceed.
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Python
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What is the classification task?
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I'm struggling to understand the concept of backpropagation in the context of recurrent neural networks. The vanishing gradient problem seems to be exacerbated in LSTMs, but I'm not sure how to mitigate it. I've read through the relevant chapters in 'Deep Learning' by Goodfellow, Bengio, and Courville, but I'm still having trouble grasping it.
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I'm having trouble understanding the concept of backpropagation in the context of neural networks. Can you guide me through it with a real-world example?
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I'm struggling to understand the mathematical foundations of backpropagation in deep neural networks. I've read Rumelhart et al.'s original paper but I'm still having trouble grasping the geometry behind it. Can you help?
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I'm struggling to understand the mathematical foundations of backpropagation. I've read Rumelhart et al.'s original paper, but I'm still having trouble grasping the geometry behind it. Could you recommend any resources that explain it in a more intuitive way?
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What is the meaning of life, the universe?
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I'm struggling to understand the mathematical foundations of backpropagation in neural networks. Could you guide me through the chain rule derivation?
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I'm struggling to understand the concept of backpropagation in neural networks. Could you guide me through the math behind it?
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I'm struggling with the implementation of the Adam optimizer in TensorFlow for my deep learning model. I've tried adjusting the learning rate and beta values, but it's not converging as expected. I've checked the documentation and forums, but I can't seem to find a solution that works for my specific use case. Could you provide some guidance on how to troubleshoot this issue?
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I'm struggling to understand the implications of the Turing completeness of a hypothetical programming language with a unique stack-based instruction set, and how it compares to the Church-Turing thesis in the context of artificial neural networks.
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In the context of quantum computing, how would you implement a Grover's algorithm to search an unsorted database of 1000 qubits, given that the oracle function is not provided?
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In the realm of deep learning, how does the backpropagation algorithm adaptively modify the weights and biases in a neural network to minimize the error in the output layer, given that the activation function in the hidden layers is a sigmoid function?
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I'm struggling to understand the implications of the vanishing gradient problem in deep learning and how it affects the training of recurrent neural networks for sequence prediction tasks. Could you provide some insights and potential solutions?
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I'm struggling with the curse of dimensionality while implementing a neural network for high-dimensional data. My accuracy is low, and I suspect it's due to overfitting. I've tried regularization techniques, but they seem to be ineffective. Any suggestions?
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I'm struggling to understand the mathematical foundations of backpropagation. The chain rule seems intuitive, but I'm having trouble applying it to deep neural networks.
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I'm struggling to implement backpropagation in my neural network using Python. I've tried several approaches but can't seem to get it right. I've read through the relevant chapters in Rumelhart, Hinton, and Williams' 'Learning representations by back-propagating errors', but I'm still having trouble understanding how to apply it correctly.
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I'm struggling to implement a Generative Adversarial Network (GAN) for image synthesis using TensorFlow. I've tried various architectures like DCGAN and StyleGAN, but the generated images lack diversity and have blurry artifacts. I suspect it might be due to the loss function or the optimization process, but I'm not sure how to proceed.
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I'm struggling to understand the mathematical foundations of backpropagation in deep learning. Can you guide me through the chain rule application?
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I'm struggling with the exploding gradient problem during backpropagation in my deep learning model. I've tried gradient clipping, but it's not helping much. Any suggestions?
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I'm struggling to understand the implications of the Riemann Hypothesis on the distribution of prime numbers in the context of a p-adic field. My current approach using Dirichlet series isn't yielding the expected results.
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I'm struggling with the concept of homeomorphisms in algebraic topology. I've read several papers and books, but I'm still not getting it. Can you help?
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I'm having trouble with the regularization parameter in my SVM model. I've tried various values, but the performance seems to be plateauing. Is there a way to optimize this without overfitting?
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I've been trying to implement a transformer-based model for sentiment analysis, but I'm having trouble with the attention mechanism. It seems like the model isn't learning the dependencies between words correctly. I've tried adjusting the dropout rate and the number of attention heads, but nothing seems to work. Do you have any suggestions?
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I'm having trouble understanding the concept of 'attention mechanisms' in the context of Transformer models. I've read the original Vaswani et al. paper and watched several tutorials, but I'm still struggling to grasp how they work under the hood.
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I'm struggling with the concept of backpropagation in neural networks. The chain rule is giving me a headache. I've read the textbook chapters multiple times, but it's still not clicking. I've also tried looking at online tutorials, but they all seem to gloss over the details. I'm at my wits' end here.
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I'm struggling with the recursive formulation of the Fibonacci sequence in Python. I've tried using dynamic programming, but I'm not seeing the expected results. Also, could you explain how this relates to the concept of inductive bias in AI?
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What is the capital of France?
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I'm struggling with the optimization of the Viterbi algorithm for HMMs in my NLP project. I've tried different dynamic programming approaches, but the computational complexity is still too high. Any suggestions?
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I'm struggling with the concept of limits in calculus. I've read the textbook and watched videos, but I still don't understand how to apply it to solve problems. Can you guide me through it?
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I'm struggling with the proof by contradiction in Euclid's Elements. I've tried to negate the conclusion, but I'm stuck on how to derive a contradiction from the premises.
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I'm struggling to implement the Adam optimizer in TensorFlow for my deep learning model. I've tried adjusting the learning rate and beta values, but the model's performance isn't improving. Any suggestions?
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I'm having trouble implementing the backpropagation algorithm for my neural network. I've tried adjusting the learning rate and activation functions, but the loss is still oscillating. I've also looked into using different optimization algorithms like Adam or RMSprop, but I'm not sure if I'm implementing them correctly. Any help would be greatly appreciated.
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I have been assigned a text classification task
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I'm having trouble with the backpropagation algorithm in my neural network. I've tried adjusting the learning rate, but the loss function seems to be stuck in a local minimum. I've also tried using different activation functions, but nothing seems to work. I'm not sure if it's a problem with my implementation or if there's something more fundamental that I'm missing.
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