Pratica di Shadowing: C4W1L01 Computer Vision - Impara a parlare inglese con i video
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Welcome to this course on convolutional networks.
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Computer vision is one of the areas that's been advancing rapidly thanks to deep learning.
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Deep learning computer vision is now helping self-driving cars figure out where are the other cars
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and the pedestrians around it so it's avoid them.
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It's making face recognition work much better than ever before so
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that perhaps some of you will soon or perhaps already be able to unlock a phone, unlock even a door using just your face.
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And if you look on your cell phone, I bet you have many apps that show you pictures of food
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or pictures of a hotel or just fun pictures of scenery.
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And some of the companies that build those apps are using deep learning to help show you the most attractive, the most beautiful, or the most relevant pictures.
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And I think deep learning is even enabling new types of art to be created.
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So I think the two reasons I'm excited about deep learning for computer vision and why I think you might be too.
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First, rapid advances in computer vision are enabling brand new applications to be built that just weren't possible a few years ago.
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And by learning these tools, perhaps you will be able to invent some of these new products and applications.
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Second, even if you don't end up building computer vision systems per se, I found that because the computer vision research community has been
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so creative and so inventive and coming up with new neural network architectures and algorithms, algorithms is actually inspired a lot of cross fertilization into other areas as well.
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For example, when I was working on speech recognition, I sometimes actually took inspiration from ideas from computer vision and borrowed them into the speech literature.
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So even if you don't end up working on computer vision, I hope
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that you find some of the ideas you learned about in this course helpful for some of your algorithms and your architectures.
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So with that, let's get started.
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Here are some examples of computer vision problems we'll study in this course.
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You've already seen image classification, sometimes also called image recognition, where you might take as input,
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say a 64 by 64 image and try to figure out is that a cat.
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Another example of a computer vision problem is object detection.
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So if you're building a self-driving car, maybe you don't just need to figure out if there are other cars in this image,
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but instead you need to figure out the position of the other cars in this picture
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so that your car can avoid them.
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So in object detection, usually we have to not just figure out that these other objects, say cause in the picture, but also draw boxes around them.
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We'll have some other way of recognizing where in the picture are these objects.
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And notice also in this example that there can be multiple calls in the same picture, or at least every one of them within a certain distance of your call.
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Here's another example, maybe a more fun one is neural style transfer.
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Let's say you have a picture and you want this picture repainted in a different style.
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So, neural style transfer, you have a content image and you have a style image.
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The image on the right is actually a Picasso.
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And you can have a neural network, put them together to repaint the content image that is the image on the left, but in the style of the image on the right,
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and you end up with the image at the bottom.
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So albums like this are enabling new types of artwork to be created.
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And in this course, you learn how to do this yourself as well.
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One of the challenges of computer vision problems is that the inputs can get really big.
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For example, in previous courses, you've worked with 64 by 64 images.
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And so that's 64 by 64 by 3 because there's three color channels.
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And if you multiply that out, that's 12288.
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So X, the input features has dimension 12288.
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And that's not too bad.
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But 64 by 64 is actually a very small image.
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If you work with larger images, maybe this is a thousand pixel by thousand pixel image, and that's actually just one megapixel,
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but the dimension of the input features will be thousand by thousand by three because you have three RGB channels,
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and that's three million.
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And if you're viewing this on a smaller screen, this might not be apparent, but this is actually a low-res 64 by 64 image, and this is a higher-res thousand by thousand image.
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But if you have 3 million input features, then this means that x here will be 3 million dimensional.
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And so if in the first hidden layer, maybe you have just a thousand hidden units,
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then the total number of weights that is the matrix W1,
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if you use a standard fully connected network like we have in causes one or two,
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this matrix will be a 1000 by 3 million dimensional matrix.
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Because X is now R by 3 million, 3M, I'm using it to denote 3 million.
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And this means that this matrix here will have 3 billion parameters, which is just very, very large.
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And with that many parameters, it's difficult to get enough data to prevent the neural network from overfitting,
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and also the computational requirements and memory requirements to train a neural network with three billion parameters is just a bit infeasible.
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But for computer vision applications, you don't want to be stuck using only tiny little images.
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You want to use large images.
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To do that, you need to implement the convolution operation, which is one of the fundamental building blocks of convolutional neural networks.
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Let's see what this means and how you can implement this in the next video.
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I will illustrate convolutions using the example of edge detection.
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Vocabolario e note di pronuncia per questa lezione
Questo video contiene 62 frasi e 981 parole da ripetere con lo shadowing. Il parlato dura 5:44. Chi parla ha un ritmo naturale di circa 171 parole al minuto, vicino a una conversazione quotidiana. Il 86% delle parole rientra nelle 3.000 più comuni dell’inglese; conviene studiare le altre prima di iniziare.
Vocaboli chiave di questo video
15 parole del video che vale la pena imparare, con pronuncia e significato:
| Parola | Pronuncia | Significato |
|---|---|---|
| neural aggettivo | /ˈnʊɹəl/ | neurale |
| input sostantivo | /ˈɪnpʊt/ | immissione |
| enable verbo | /ɪˈneɪ.bəl/ | permettere |
| detection sostantivo | /dɪˈtɛk.ʃən/ | identificazione, investigazione |
| algorithm sostantivo | /ˈælɡəɹɪðm̩/ | algoritmo |
| parameter sostantivo | /pəˈɹæm.ə.tɚ/ | parametro |
| architecture sostantivo | /ˈɑː.kɪˌtɛk.t͡ʃə/ | architettura |
| requirement sostantivo | /ɹɪˈkwaɪɹmənt/ | requisito |
| implement verbo | /ˈɪmpləmənt/ | mettere in pratica, attuare |
| dimensional aggettivo | /daɪˈmɛn.ʃə.nəl/ | -dimensionale |
| dimension sostantivo | /daɪˈmɛn.ʃən/ | aspetto |
| unlock verbo | /ʌnˈlɑk/ | aprire |
| pixel sostantivo | /ˈpɪk.səl/ | pixel |
| helpful aggettivo | /ˈhɛlp.fl̩/ | disponibile, utile |
| inspiration sostantivo | /ˌɪn.spɪˈɹeɪ.ʃən/ | inspirazione, inalazione |
I phrasal verb che sentirai
| Parola | Significato |
|---|---|
| figure out verbo | scoprire, rendersi conto |
| end up verbo | andare a finire, finire |
| come up with verbo | ideare, escogitare |
La grammatica di questo video
Le strutture che chi parla usa di più, con le parole esatte del video:
| Struttura | Nel video |
|---|---|
| Forma passiva be + participio passato — conta ciò che accade, non chi lo fa | be created · be built · is actually inspired |
| Present perfect have/has + participio passato — un’azione passata che conta ancora adesso | has been · You've already seen · you've worked |
| Frasi relative who / which + frase — un’informazione in più su una persona o una cosa | parameters, which is · operation, which is |
Pronuncia a cui fare attenzione
Chi parla usa 13 contrazioni e forme ridotte, come don't, I'm, we'll. Pronunciale nella forma breve, così come le senti.
- I suoni “sh” e “zh”: recognition /ˌɹɛkəɡˈnɪʃ(ə)n/, detection /dɪˈtɛk.ʃən/, dimensional /daɪˈmɛn.ʃə.nəl/, dimension /daɪˈmɛn.ʃən/, inspiration /ˌɪn.spɪˈɹeɪ.ʃən/
- Parole lunghe — attenzione all’accento: recognition /ˌɹɛkəɡˈnɪʃ(ə)n/, parameter /pəˈɹæm.ə.tɚ/, architecture /ˈɑː.kɪˌtɛk.t͡ʃə/, dimensional /daɪˈmɛn.ʃə.nəl/, inspiration /ˌɪn.spɪˈɹeɪ.ʃən/
Come esercitarsi con questo video
- Ascolta tutto il video una volta senza parlare e annota le parole che non conosci.
- Inizia a velocità 0,75×, fai shadowing frase per frase e torna alla velocità normale quando diventa facile.
- Registrati e confronta con l’originale, facendo attenzione a parole come neural, input, enable.
Cos'è la tecnica dello Shadowing?
Shadowing è una tecnica di apprendimento delle lingue supportata da studi scientifici, originariamente sviluppata per la formazione dei traduttori professionisti e resa popolare dal poliglotta Dr. Alexander Arguelles. Il metodo è semplice ma potente: ascolti un audio in inglese di madrelingua e lo ripeti immediatamente ad alta voce — come un'ombra che segue il parlante con un ritardo di solo 1–2 secondi. A differenza dell'ascolto passivo o degli esercizi di grammatica, lo shadowing costringe il tuo cervello e i muscoli della bocca a elaborare e riprodurre simultaneamente i modelli di discorso reale. La ricerca dimostra che migliora significativamente la precisione della pronuncia, l'intonazione, il ritmo, il discorso connesso, la comprensione dell'ascolto e la fluidità del parlato — rendendolo uno dei metodi più efficaci per la preparazione alla prova di speaking dell'IELTS e per la comunicazione reale in inglese.