跟讀練習: PyTorch in 100 Seconds - 通過影片學習英語口語

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PyTorch, an open-source deep learning framework used to build some of the world's most famous artificial intelligence products.
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It was created at the Meta AI Research Lab in 2016, but is actually derived from the Lua-based Torch library that dates back to 2002.
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Fundamentally, it's a library for programming with tensors, which are basically just multi-dimensional arrays that represent data and parameters in deep neural networks.
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Sounds complicated, but its focus on usability will have you training machine learning models with just a few lines of Python.
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In addition, it facilitates high-performance parallel computing on a GPU, thanks to NVIDIA's CUDA platform.
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Developers love prototyping with it because it supports a dynamic computational graph, allowing models to be optimized at runtime.
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It does this by constructing a directed acyclic graph consisting of functions
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that keeps track of all the executed operations on the tensors, allowing you to change the shape, size, and operations after every iteration if needed.
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PyTorch has been used to train models for computer vision AI like Tesla Autopilot, image generators like Stable Diffusion, and speech recognition models like OpenAI Whisper, just to name a few.
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To get started, install PyTorch, and optionally CUDA, if you want to accelerate computing on your GPU.
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Now import it into a Python file or notebook.
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Like I mentioned, a tensor is similar to a multi-dimensional array.
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Create a 2D array or matrix with Python, then use Torch to convert it into a tensor.
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Now we can run all kinds of computations on it, like we might convert all these integers into random floating points.
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We can also perform linear algebra by taking multiple tensors and multiplying them together.
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What you came here to do though is build a deep neural network, like an image classifier.
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To handle that, we can define a new class that inherits from the Neural Network Module class.
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Inside the constructor, we can build it out layer by layer.
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The flattened layer will take a multi-dimensional input, like an image, and convert it to one dimension.
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From there, Sequential is used to create a container of layers that the data will flow through.
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Each layer has multiple nodes, where each node is like its own mini statistical model.
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As each data point flows through it, it'll try to guess the output, and gradually update a mapping of weights to determine the importance of a given variable.
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Linear is a fully connected layer that takes the flattened 28x28 image and transforms it to an output of 512.
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This layer is followed by a non-linear activation function.
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When activated, it means that feature might be important and outputs the node.
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Otherwise, it just outputs zero.
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And finally, we finish with a fully connected layer that outputs the 10 labels the model is trying to predict.
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With these pieces in place, the next step is to define a forward method that describes the flow of data.
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And now instantiate the model to a GPU and pass it some input data.
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This will automatically call its forward method for training and prediction.
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Congratulations, you just built a neural network.
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This has been PyTorch in 100 seconds.
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Thanks for watching, and I will see you in the next one.

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流利口語所需的一切

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詞彙遊戲邊玩邊學

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推薦影片

看影片學英語:通過技術講解提升口語的實用場景

想像你正在觀看一段關於人工智慧框架PyTorch的英文影片,想要邊學技術邊練英語。這種結合專業內容的學習場景,既能積累行業詞彙,又能通過真實語境提升聽力和口語。影片中清晰的邏輯講解、專業術語的使用,正是練習“shadowspeak”(影子跟讀)的絕佳素材——你需要緊跟 speaker 的節奏,模仿發音、語調和停頓,在理解內容的同時強化口語輸出。

必學實用表達與搭配

  • open-source deep learning framework:開源深度學習框架(技術類文本高頻搭配,注意“framework”的弱讀)
  • dynamic computational graph:動態計算圖(專業術語,模仿時注意“computational”的重音位置)
  • train machine learning models:訓練機器學習模型(動詞短語,“train”與“models”的連讀要自然)
  • non-linear activation function:非線性啟用函式(科技英語中長定語的處理,模仿時保持語速穩定)
  • forward method:前向傳播方法(AI領域核心術語,“forward”的發音要準確)

你的影子跟讀挑戰:現在就行動!

開啟影片,找到“Sounds complicated, but its focus on usability will have you training machine learning models with just a few lines of Python.”這段。先聽3遍,注意 speaker 的語調變化(“but”後輕微升調,強調轉折)和“usability”的發音。然後開始“shadow speech”訓練:按下播放鍵後立即跟讀,儘可能同步,重點模仿“just a few lines”的輕快節奏。重複5次後,錄音對比自己與原音的差異,尤其關注“training”的重音和“Python”的尾音。完成後,你會發現不僅對技術內容更熟悉,英語發音和流暢度也有明顯提升。記住,優質的“shadowing site”能提供多樣素材,但關鍵是堅持——每天10分鐘,用技術影片練口語,事半功倍!

影片中的語法

說話人最常用的結構,並附上影片中的原話:

結構影片中的用法
被動語態 be + 過去分詞 — 強調發生了什麼,而不是誰做的was created · be optimized · is followed
現在完成時 have/has + 過去分詞 — 過去發生但與現在仍有關聯的事has been

什麼是跟讀法?

跟讀法 (Shadowing) 是一種有科學依據的語言學習技巧,最初開發用於專業口譯員的培訓,並由多語言者Alexander Arguelles博士普及。這個方法簡單而強大:您在聽英語母語原聲的同時立即大聲重複——就像是一個延遲1-2秒緊跟說話者的影子。與被動聽力或語法練習不同,跟讀法強迫您的大腦和口腔肌肉同時處理並模模擬實的講話模式。研究表明它能顯著提高發音準確性,語調,節奏,連讀,聽力理解和口語流利度——使其成為雅思口語備考和真實英語交流最有效的方法之一。

影子跟讀法: 閱讀完整分步指南 →