{ "source_file": "0930", "source_platform": "Medium", "author": "Angela Shi & Kezhan Shi", "title_listed": "The Machine Learning Advent Calendar Day 23: 1D CNN for Text in Excel", "keywords": "", "domain": "机器学习", "content_type": "深度技术解析", "is_paid_flag": "否", "url": "https://towardsdatascience.com/the-machine-learning-advent-calendar-day-23-cnn-in-excel/", "publish_date": "2025-12-24", "corpus_stage": "", "notes": "", "id": "e91f1fee1dd6", "collected_at": "2026-10-02T01:36:22", "title": "The Machine Learning “Advent Calendar” Day 23: 1D CNN for Text in Excel | Towards Data Science", "body_markdown": "![Revisit consent button](../images/body-df29beacd7496220cc94.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. 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It also helps in adverstising on the website.\n\n\n * Cookie\nedgebucket\n * Duration\nsession\n * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n * Cookie\ndid\n * Duration\n1 year\n * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\n# The Machine Learning “Advent Calendar” Day 23: 1D CNN for Text in Excel\nA 1D Convolutional Neural Network for Text Classification, Built Step by Step in Excel\n[Angela and Kezhan Shi](https://towardsdatascience.com/author/angela.shi/)\nDecember 23, 20259 min read\n![](../images/000.jpg)\n1D CNN in excel\nConvolutional Neural Networks were first introduced for images, and for images they are often easy to understand.\nA filter slides over pixels and detects edges, shapes, or textures. You can [read this article I wrote earlier](https://towardsdatascience.com/understanding-convolutional-neural-networks-cnns-through-excel/) to understand how CNNs work for images with Excel.\nFor text, the idea is the same.\n * Instead of pixels, we slide filters over **words**.\n * Instead of visual patterns, we detect **linguistic patterns**.\n\n\nAnd many important patterns in text are very local. One simple and intuitive use case is to detect negation. Let's take these very simple examples:\n * “good” is positive\n * “bad” is negative\n * “not good” is negative\n * “not bad” is often positive\n\n\n[In my previous article](https://towardsdatascience.com/the-machine-learning-advent-calendar-day-22-embeddings-in-excel/), we saw how to represent words as numbers using embeddings.\nWe also saw a key limitation: when we used a global average, **word order was completely ignored**.\nFrom the model’s point of view, “not good” and “good not” looked exactly the same.\nSo the next challenge is clear: **we want the model to take word order into account**.\nA 1D Convolutional Neural Network is a natural tool for this, because it scans a sentence with small sliding windows and reacts when it recognizes familiar local patterns.\nLearn this step by step with the interactive [Machine Learning](https://roadmap.sh/machine-learning) roadmap.\n## 1. Understanding a 1D CNN for Text: Architecture and Depth\n### 1.1. Building a 1D CNN for text in Excel\nIn this article, we build a 1D CNN architecture in Excel with the following components:\n * **Embedding dictionary** \nWe use a 2-dimensional embedding. Because one dimension is not enough for this task. \nOne dimension encodes **sentiment** , and the second dimension encodes **negation**.\n * **Conv1D layer** \nThis is the core component of a CNN architecture. \nIt consists of filters that **slide across the sentence** with a window length of 2 words. We choose 2 words to be simple.\n * **ReLU and global max pooling** \nThese steps keep only the strongest matches detected by the filters. \nWe will also discuss the fact that ReLU is optional.\n * **Logistic regression** \nThis is the final classification layer, which combines the detected patterns into a probability.\n\n\n![](../images/001.png)\n1D CNN in Excel - all images by author\nThis pipeline corresponds to a standard CNN text classifier. \nThe only difference here is that we explicitly write and visualize the forward pass in Excel.\n### 1.2. What “deep learning” means in this architecture\nBefore going further, let us take a step back. \nYes, I know, I do this often, but having a global view of models really helps to understand them.\nThe definition of _deep learning_ is often blurred. \nFor many people, deep learning simply means “many layers”.\nHere, I will take a slightly different point of view.\nWhat really characterizes deep learning is not the number of layers, but the **depth of the transformation applied to the input data**.\nWith this definition:\n * Even a model with a single convolution layer can be considered deep learning,\n * because the input is transformed into a more structured and abstract representation.\n\n\nOn the other hand, taking raw input data, applying one-hot encoding, and stacking many fully connected layers does not necessarily make a model deep in a meaningful sense. \nIn theory, if we don’t have any transformation, one layer is enough.\nIn CNNs, the presence of multiple layers has a very concrete motivation.\nConsider a sentence like:\nThis movie is not very good\nWith a single convolution layer and a small window, we can detect simple local patterns such as: “very + good”\nBut we cannot yet detect higher-level patterns such as: “not + (very good)”\nThis is why CNNs are often stacked:\n * the first layer detects simple local patterns,\n * the second layer combines them into more complex ones.\n\n\nIn this article, we deliberately focus on **one convolution layer**. \nThis makes every step visible and easy to understand in Excel, while keeping the logic identical to deeper CNN architectures.\n## 2. Turning words into embeddings\nLet us start with some simple words. We will try to detect negation, so we will use these terms, with other words (that we will not model)\n * “good”\n * “bad”\n * “not good”\n * “not bad”\n\n\nWe keep the representation intentionally small so that every step is visible.\nWe will only use a dictionary of three words : good, bad and not.\nAll other words will have 0 as embeddings.\n### 2.1 Why one dimension is not enough\nIn a previous article on sentiment detection, we used a single dimension. \nThat worked for “good” versus “bad”.\nBut now we want to handle **negation**.\nOne dimension can only represent one concept well. \nSo we need **two dimensions** :\n * senti: sentiment polarity\n * neg: negation marker\n\n\n### 2.2 The embedding dictionary\nEach word becomes a 2D vector:\n * good → (senti = +1, neg = 0)\n * bad → (senti = -1, neg = 0)\n * not → (senti = 0, neg = +1)\n * any other word → (0, 0)\n\n\n![](../images/body-628b15afc0dda5eaf889.png)\nThis is not how real embeddings look. Real embeddings are learned, high-dimensional, and not directly interpretable.\nBut for understanding how Conv1D works, this toy embedding is perfect.\nIn Excel, this is just a lookup table. \nIn a real neural network, this embedding matrix would be trainable.\n![](../images/body-2a2e8e4966228910a038.png)\n## 3. Conv1D filters as sliding pattern detectors\nNow we arrive at the core idea of a 1D CNN.\nA Conv1D filter is nothing mysterious. It is just a **small set of weights plus a bias** that slides over the sentence.\nBecause:\n * each word embedding has 2 values (senti, neg)\n * our window contains 2 words\n\n\neach filter has:\n * 4 weights (2 dimensions × 2 positions)\n * 1 bias\n\n\nThat is all.\nYou can think of a filter as repeatedly asking the same question at every position:\n“Do these two neighboring words match a pattern I care about?”\n### 3.1 Sliding windows: how Conv1D sees a sentence\nConsider this sentence:\nit is not bad at all\nWe choose a window size of 2 words.\nThat means the model looks at every adjacent pair:\n * (it, is)\n * (is, not)\n * (not, bad)\n * (bad, at)\n * (at, all)\n\n\nImportant point: \n**The filters slide everywhere** , even when both words are neutral (all zeros).\n![](../images/body-1868b4a175952ad1db3c.png)\n### 3.2 Four intuitive filters\nTo make the behavior easy to understand, we use four filters.\n![](../images/body-6357fb9c28f6ddcf401f.png)\n### Filter 1 – “I see GOOD”\nThis filter looks only at the sentiment of the **current word**.\nPlain-text equation for one window:\nz = senti(current_word)\nIf the word is “good”, z = 1 \nIf the word is “bad”, z = -1 \nIf the word is neutral, z = 0\nAfter ReLU, negative values become 0. But it is optional.\nUnderstanding the mechanism sometimes comes with [**MODEL_NOT_MAGIC**](https://ko-fi.com/angelashi/link/MODEL_NOT_MAGIC).\n### Filter 2 – “I see BAD”\nThis one is symmetric.\nz = -senti(current_word)\nSo:\n * “bad” → z = 1\n * “good” → z = -1 → ReLU → 0\n\n\n### Filter 3 – “I see NOT GOOD”\nThis filter looks at two things at the same time:\n * neg(previous_word)\n * senti(current_word)\n\n\nEquation:\nz = neg(previous_word) + senti(current_word) - 1\nWhy the “-1”? \nIt acts like a threshold so that both conditions must be true.\nResults:\n * “not good” → 1 + 1 - 1 = 1 → activated\n * “is good” → 0 + 1 - 1 = 0 → not activated\n * “not bad” → 1 - 1 - 1 = -1 → ReLU → 0\n\n\n### Filter 4 – “I see NOT BAD”\nSame idea, slightly different sign:\nz = neg(previous_word) + (-senti(current_word)) - 1\nResults:\n * “not bad” → 1 + 1 - 1 = 1\n * “not good” → 1 - 1 - 1 = -1 → 0\n\n\nThis is a very important intuition:\nA CNN filter can behave like a **local logical rule** , learned from data.\n### 3.3 Final result of sliding windows\nHere is the final results of these 4 filters.\n![](../images/body-07a2d813bd9cc457739e.png)\n## 4. ReLU and max pooling: from local to global\nReLU is a gate, max pooling is a memory.\n### 4.1 ReLU\nAfter computing z for every window, we apply ReLU:\nReLU(z) = max(0, z)\nMeaning:\n * negative evidence is ignored\n * positive evidence is kept\n\n\nEach filter becomes a **presence detector**.\nBy the way, it is an activation function in the Neural network. So a Neural network is not that difficult after all.\n![](../images/body-07a2d813bd9cc457739e.png)\n### 4.2 Global Max pooling\nThen comes **global max pooling**.\nFor each filter, we keep only: max activation over all windows\nInterpretation: “I do not care where the pattern appears, only whether it appears strongly somewhere.”\nAt this point, the whole sentence is summarized by 4 numbers:\n * strongest “good” signal\n * strongest “bad” signal\n * strongest “not good” signal\n * strongest “not bad” signal\n\n\n![](../images/body-798c9590effdadeb02ad.png)\n### 4.3 What happens if we remove ReLU?\nWithout ReLU:\n * negative values stay negative\n * max pooling may select negative values\n\n\nThis mixes two ideas:\n * absence of a pattern\n * opposite of a pattern\n\n\nThe filter stops being a clean detector and becomes a signed score.\nThe model could still work mathematically, but interpretation becomes harder.\n## 5. The final layer is logistic regression\nNow we combine these signals.\nWe compute a score using a linear combination:\nscore = 2 × F_good - 2 × F_bad - 3 × F_not_good - 3 × F_not_bad - bias\n![](../images/body-fbbc54383222b641825a.png)\nThen we convert the score into a probability:\nprobability = 1 / (1 + exp(-score))\nThat is exactly logistic regression.\nSo yes:\n * the CNN extracts features: this step can be considered as feature engineering, right?\n * logistic regression makes the final decisions, it is a classic machine learning model we know well\n\n\n![](../images/body-e9b462db5ca9b260727a.png)\n## 6. Full examples with sliding filters\n### Example 1\n\"it is bad, so it is not good at all\"\nThe sentence contains:\n * “bad”\n * “not good”\n\n\nAfter max pooling:\n * F_good = 1 (because “good” exists)\n * F_bad = 1\n * F_not_good = 1\n * F_not_bad = 0\n\n\nFinal score becomes strongly negative. \nPrediction: negative sentiment.\n![](../images/body-0928dd0cb869dd93e527.png)\n### Example 2\n\"it is good. yes, not bad.\"\nThe sentence contains:\n * “good”\n * “not bad”\n\n\nAfter max pooling:\n * F_good = 1\n * F_bad = 1 (because the word “bad” appears)\n * F_not_good = 0\n * F_not_bad = 1\n\n\nThe final linear layer learns that “not bad” should outweigh “bad”.\nPrediction: positive sentiment.\nThis also shows something important: max pooling keeps all strong signals. \nThe final layer decides how to combine them.\n![](../images/body-bf8ae376900587d2cb6f.png)\n### Exemple 3 with A limitation that explains why CNNs get deeper\nTry this sentence:\n\"it is not very bad\"\nWith a window of size 2, the model sees:\n * (not, very)\n * (very, bad)\n\n\nIt never sees (not, bad), so the “not bad” filter never fires.\nIt explains why real models use:\n * larger windows\n * multiple convolution layers\n * or other architectures for longer dependencies\n\n\n![](../images/body-e9b462db5ca9b260727a.png)\n## Conclusion\nThe strength of Excel is visibility.\nYou can see:\n * the embedding dictionary\n * all filter weights and biases\n * every sliding window\n * every ReLU activation\n * the max pooling result\n * the logistic regression parameters\n\n\nTraining is simply the process of adjusting these numbers.\nOnce you see that, CNNs stop being mysterious.\nThey become what they really are: **structured, trainable pattern detectors that slide over data.**\n···\nAll the Excel files are available through this [Kofi link](https://ko-fi.com/s/4ddca6dff1). 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You can [read this article I wrote earlier](https://towardsdatascience.com/understanding-convolutional-neural-networks-cnns-through-excel/) to understand how CNNs work for images with Excel.\nFor text, the idea is the same.\n * Instead of pixels, we slide filters over **words**.\n * Instead of visual patterns, we detect **linguistic patterns**.\n\n\nAnd many important patterns in text are very local. One simple and intuitive use case is to detect negation. Let's take these very simple examples:\n * “good” is positive\n * “bad” is negative\n * “not good” is negative\n * “not bad” is often positive\n\n\n[In my previous article](https://towardsdatascience.com/the-machine-learning-advent-calendar-day-22-embeddings-in-excel/), we saw how to represent words as numbers using embeddings.\nWe also saw a key limitation: when we used a global average, **word order was completely ignored**.\nFrom the model’s point of view, “not good” and “good not” looked exactly the same.\nSo the next challenge is clear: **we want the model to take word order into account**.\nA 1D Convolutional Neural Network is a natural tool for this, because it scans a sentence with small sliding windows and reacts when it recognizes familiar local patterns.\nLearn this step by step with the interactive [Machine Learning](https://roadmap.sh/machine-learning) roadmap.\n## 1. Understanding a 1D CNN for Text: Architecture and Depth\n### 1.1. Building a 1D CNN for text in Excel\nIn this article, we build a 1D CNN architecture in Excel with the following components:\n * **Embedding dictionary** \nWe use a 2-dimensional embedding. Because one dimension is not enough for this task. \nOne dimension encodes **sentiment** , and the second dimension encodes **negation**.\n * **Conv1D layer** \nThis is the core component of a CNN architecture. \nIt consists of filters that **slide across the sentence** with a window length of 2 words. We choose 2 words to be simple.\n * **ReLU and global max pooling** \nThese steps keep only the strongest matches detected by the filters. \nWe will also discuss the fact that ReLU is optional.\n * **Logistic regression** \nThis is the final classification layer, which combines the detected patterns into a probability.\n\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/12/image-419.png)\n1D CNN in Excel - all images by author\nThis pipeline corresponds to a standard CNN text classifier. \nThe only difference here is that we explicitly write and visualize the forward pass in Excel.\n### 1.2. What “deep learning” means in this architecture\nBefore going further, let us take a step back. \nYes, I know, I do this often, but having a global view of models really helps to understand them.\nThe definition of _deep learning_ is often blurred. \nFor many people, deep learning simply means “many layers”.\nHere, I will take a slightly different point of view.\nWhat really characterizes deep learning is not the number of layers, but the **depth of the transformation applied to the input data**.\nWith this definition:\n * Even a model with a single convolution layer can be considered deep learning,\n * because the input is transformed into a more structured and abstract representation.\n\n\nOn the other hand, taking raw input data, applying one-hot encoding, and stacking many fully connected layers does not necessarily make a model deep in a meaningful sense. \nIn theory, if we don’t have any transformation, one layer is enough.\nIn CNNs, the presence of multiple layers has a very concrete motivation.\nConsider a sentence like:\nThis movie is not very good\nWith a single convolution layer and a small window, we can detect simple local patterns such as: “very + good”\nBut we cannot yet detect higher-level patterns such as: “not + (very good)”\nThis is why CNNs are often stacked:\n * the first layer detects simple local patterns,\n * the second layer combines them into more complex ones.\n\n\nIn this article, we deliberately focus on **one convolution layer**. \nThis makes every step visible and easy to understand in Excel, while keeping the logic identical to deeper CNN architectures.\n## 2. Turning words into embeddings\nLet us start with some simple words. We will try to detect negation, so we will use these terms, with other words (that we will not model)\n * “good”\n * “bad”\n * “not good”\n * “not bad”\n\n\nWe keep the representation intentionally small so that every step is visible.\nWe will only use a dictionary of three words : good, bad and not.\nAll other words will have 0 as embeddings.\n### 2.1 Why one dimension is not enough\nIn a previous article on sentiment detection, we used a single dimension. \nThat worked for “good” versus “bad”.\nBut now we want to handle **negation**.\nOne dimension can only represent one concept well. \nSo we need **two dimensions** :\n * senti: sentiment polarity\n * neg: negation marker\n\n\n### 2.2 The embedding dictionary\nEach word becomes a 2D vector:\n * good → (senti = +1, neg = 0)\n * bad → (senti = -1, neg = 0)\n * not → (senti = 0, neg = +1)\n * any other word → (0, 0)\n\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/12/image-420-1024x591.png)\nThis is not how real embeddings look. Real embeddings are learned, high-dimensional, and not directly interpretable.\nBut for understanding how Conv1D works, this toy embedding is perfect.\nIn Excel, this is just a lookup table. \nIn a real neural network, this embedding matrix would be trainable.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/12/image-421-1024x488.png)\n## 3. Conv1D filters as sliding pattern detectors\nNow we arrive at the core idea of a 1D CNN.\nA Conv1D filter is nothing mysterious. It is just a **small set of weights plus a bias** that slides over the sentence.\nBecause:\n * each word embedding has 2 values (senti, neg)\n * our window contains 2 words\n\n\neach filter has:\n * 4 weights (2 dimensions × 2 positions)\n * 1 bias\n\n\nThat is all.\nYou can think of a filter as repeatedly asking the same question at every position:\n“Do these two neighboring words match a pattern I care about?”\n### 3.1 Sliding windows: how Conv1D sees a sentence\nConsider this sentence:\nit is not bad at all\nWe choose a window size of 2 words.\nThat means the model looks at every adjacent pair:\n * (it, is)\n * (is, not)\n * (not, bad)\n * (bad, at)\n * (at, all)\n\n\nImportant point: \n**The filters slide everywhere** , even when both words are neutral (all zeros).\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/12/image-423-1024x546.png)\n### 3.2 Four intuitive filters\nTo make the behavior easy to understand, we use four filters.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/12/image-424-1024x311.png)\n### Filter 1 – “I see GOOD”\nThis filter looks only at the sentiment of the **current word**.\nPlain-text equation for one window:\nz = senti(current_word)\nIf the word is “good”, z = 1 \nIf the word is “bad”, z = -1 \nIf the word is neutral, z = 0\nAfter ReLU, negative values become 0. But it is optional.\nUnderstanding the mechanism sometimes comes with [**MODEL_NOT_MAGIC**](https://ko-fi.com/angelashi/link/MODEL_NOT_MAGIC).\n### Filter 2 – “I see BAD”\nThis one is symmetric.\nz = -senti(current_word)\nSo:\n * “bad” → z = 1\n * “good” → z = -1 → ReLU → 0\n\n\n### Filter 3 – “I see NOT GOOD”\nThis filter looks at two things at the same time:\n * neg(previous_word)\n * senti(current_word)\n\n\nEquation:\nz = neg(previous_word) + senti(current_word) - 1\nWhy the “-1”? \nIt acts like a threshold so that both conditions must be true.\nResults:\n * “not good” → 1 + 1 - 1 = 1 → activated\n * “is good” → 0 + 1 - 1 = 0 → not activated\n * “not bad” → 1 - 1 - 1 = -1 → ReLU → 0\n\n\n### Filter 4 – “I see NOT BAD”\nSame idea, slightly different sign:\nz = neg(previous_word) + (-senti(current_word)) - 1\nResults:\n * “not bad” → 1 + 1 - 1 = 1\n * “not good” → 1 - 1 - 1 = -1 → 0\n\n\nThis is a very important intuition:\nA CNN filter can behave like a **local logical rule** , learned from data.\n### 3.3 Final result of sliding windows\nHere is the final results of these 4 filters.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/12/image-425-1024x434.png)\n## 4. ReLU and max pooling: from local to global\nReLU is a gate, max pooling is a memory.\n### 4.1 ReLU\nAfter computing z for every window, we apply ReLU:\nReLU(z) = max(0, z)\nMeaning:\n * negative evidence is ignored\n * positive evidence is kept\n\n\nEach filter becomes a **presence detector**.\nBy the way, it is an activation function in the Neural network. So a Neural network is not that difficult after all.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/12/image-425-1024x434.png)\n### 4.2 Global Max pooling\nThen comes **global max pooling**.\nFor each filter, we keep only: max activation over all windows\nInterpretation: “I do not care where the pattern appears, only whether it appears strongly somewhere.”\nAt this point, the whole sentence is summarized by 4 numbers:\n * strongest “good” signal\n * strongest “bad” signal\n * strongest “not good” signal\n * strongest “not bad” signal\n\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/12/image-427-1024x325.png)\n### 4.3 What happens if we remove ReLU?\nWithout ReLU:\n * negative values stay negative\n * max pooling may select negative values\n\n\nThis mixes two ideas:\n * absence of a pattern\n * opposite of a pattern\n\n\nThe filter stops being a clean detector and becomes a signed score.\nThe model could still work mathematically, but interpretation becomes harder.\n## 5. The final layer is logistic regression\nNow we combine these signals.\nWe compute a score using a linear combination:\nscore = 2 × F_good - 2 × F_bad - 3 × F_not_good - 3 × F_not_bad - bias\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/12/image-429-1024x256.png)\nThen we convert the score into a probability:\nprobability = 1 / (1 + exp(-score))\nThat is exactly logistic regression.\nSo yes:\n * the CNN extracts features: this step can be considered as feature engineering, right?\n * logistic regression makes the final decisions, it is a classic machine learning model we know well\n\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/12/image-432-1024x322.png)\n## 6. Full examples with sliding filters\n### Example 1\n\"it is bad, so it is not good at all\"\nThe sentence contains:\n * “bad”\n * “not good”\n\n\nAfter max pooling:\n * F_good = 1 (because “good” exists)\n * F_bad = 1\n * F_not_good = 1\n * F_not_bad = 0\n\n\nFinal score becomes strongly negative. \nPrediction: negative sentiment.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/12/image-430-1024x358.png)\n### Example 2\n\"it is good. yes, not bad.\"\nThe sentence contains:\n * “good”\n * “not bad”\n\n\nAfter max pooling:\n * F_good = 1\n * F_bad = 1 (because the word “bad” appears)\n * F_not_good = 0\n * F_not_bad = 1\n\n\nThe final linear layer learns that “not bad” should outweigh “bad”.\nPrediction: positive sentiment.\nThis also shows something important: max pooling keeps all strong signals. \nThe final layer decides how to combine them.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/12/image-431-1024x373.png)\n### Exemple 3 with A limitation that explains why CNNs get deeper\nTry this sentence:\n\"it is not very bad\"\nWith a window of size 2, the model sees:\n * (not, very)\n * (very, bad)\n\n\nIt never sees (not, bad), so the “not bad” filter never fires.\nIt explains why real models use:\n * larger windows\n * multiple convolution layers\n * or other architectures for longer dependencies\n\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/12/image-432-1024x322.png)\n## Conclusion\nThe strength of Excel is visibility.\nYou can see:\n * the embedding dictionary\n * all filter weights and biases\n * every sliding window\n * every ReLU activation\n * the max pooling result\n * the logistic regression parameters\n\n\nTraining is simply the process of adjusting these numbers.\nOnce you see that, CNNs stop being mysterious.\nThey become what they really are: **structured, trainable pattern detectors that slide over data.**\n···\nAll the Excel files are available through this [Kofi link](https://ko-fi.com/s/4ddca6dff1). 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