Shadowing Practice: Vanishing & Exploding Gradient explained | A problem resulting from backpropagation - Learn English Speaking with Video

Hey, what's going on everyone?

In this video, we're going to discuss a problem

that creeps up time and time again during the training process of an artificial neural network.

This is the problem of unstable gradients, and is most popularly referred to as the vanishing gradient problem.

So let's get to it.

Alright, what do we really already know about gradients as it pertains to neural networks?

Well, for one, when we use the word gradient by itself, we're typically referring to the gradient of the loss function with respect to the weights in the network network.

We also know how this gradient is calculated, using backpropagation, which we covered in our earlier videos dedicated solely to backprop.

And finally, as we saw in our video that demonstrates how a neural network learns, we know what to do with this gradient after it's calculated.

We update our weights with it.

Well, we don't, per se, but stochastic gradient descent does, with the goal in mind to find the most optimal weight for each connection

that will minimize the total loss of the network.

So, with this understanding, we're now going to talk about the vanishing gradient problem.

We're first going to answer, well, what the heck is the vanishing gradient problem anyway?

Here we'll cover the idea conceptually.

We'll then move our discussion to talking about how this problem occurs.

Then, with the understanding that we'll have developed up to this point, we'll discuss the problem of exploding gradients, which we'll see is actually very similar to the vanishing gradient problem,

and so we'll be able to take what we learned about that problem and apply it to this new one.

So what is the vanishing gradient problem?

We'll first talk about this kind of generally, and then we'll get into the details in just a few moments.

In general, this is a problem that causes major difficulty when training a neural network.

More specifically, this is a problem that involves the weights in earlier layers of the network work.

Recall that during training, Stochastic Gradient Descent, or SGD, works to calculate the gradient of the loss with respect to weights in the network.

Now, sometimes, and we'll speak more about why this is in a bit, the gradient with respect to weights in earlier layers of the network becomes really small,

like vanishingly small.

Hence, vanishing gradient.

Okay, what's the big deal with a small gradient?

Well, once SGD calculates this gradient with respect to a particular weight, it uses this value to update that weight.

So the weight gets updated in some way that is proportional to the gradient.

If the gradient is vanishingly small, then this update is in turn going to be vanishingly small as well.

So if this new updated value of the weight has just barely moved from its original value, then it's not really doing much for the network.

This change is not going to carry through the network very well to help reduce the loss

because it's barely changed at all from where it was before the update occurred.

Therefore this weight becomes kind of stuck, never really updating enough to even get close to its optimal value,

which has implications for the remainder of the network to the right of this one weight

and impairs the ability of the network to learn.

So now that we know what this problem is, how exactly does this problem occur?

We know from what we learned about backpropagation

that the gradient of the loss with respect to any given

weight is going to be the product of some derivatives that depend on components that reside later in the network.

So given this, the earlier in the network a weight lives, the more terms will be needed in the product

that we just mentioned to get the gradient of the loss with respect to this weight.

What happens if the terms in this product, or at least some of them, are small?

And by small, we mean less than one small.

Well, the product of a bunch of numbers less than one is going to give us an even smaller number, right?

Okay, cool.

So as we mentioned earlier, we now take this result, the small number, and update our weight with it.

Recall that we do this update by first multiplying this number by our learning rate, which it itself is a small number,

usually ranging between 0 .01 and 0 .0001.

So now the result of this product is an even smaller number.

Then we subtract this number from the weight, and the final result of this difference is going to be the value of the updated weight.

Now you can think about if the gradient that we obtained with respect to this weight was already really small, i .e vanishing, then by the time we multiply it by the learning rate,

the product is going to be even smaller, and so when we subtract this teeny tiny number from the weight, it's just barely going to move the weight at all.

So, essentially, the weight gets into this kind of stuck state not moving,

not quote learning, and therefore not really helping to meet the overall objective of minimizing the loss of the network.

And we can see why earlier weights are subject to this problem.

Because as we said, the earlier in the network the weight resides, the more terms are going to be included in the product to calculate the gradient.

And the more terms we're multiplying together that are less than 1, the quicker the gradient is going to vanish.

So now let's talk about this problem in the opposite direction.

Not a gradient that vanishes, but rather a gradient that explodes.

Well, think about the conversation we just had about how the

vanishing gradient problem occurs with weights early in the network due to a product of at least some relatively small values.

Now think about calculating the gradient with respect to the same weight, but instead of really small terms, what if they were large?

And by large, we mean greater than 1.

Well, if we multiply a bunch of terms together that are all greater than 1, we're going to get something greater than 1, and perhaps even a lot greater than 1.

The same argument holds here that we discussed about the vanishing gradient, where the earlier in the network a weight lives, the more terms will be needed in the product we just mentioned.

And so the more of these larger value terms we have being multiplied together, the larger the gradient is going to be, thus essentially exploding in size.

And with this gradient, we go through the same process to proportionally update our weight with it that we talked about earlier.

But this time, instead of barely moving our weight with this update, we're going to greatly move it.

So much so, perhaps, that the optimal value for this weight won't ever be achieved

because the proportion to which the weight becomes updated with each epoch is just too large

and continues to move further and further away from its optimal value.

So a main takeaway that we should be able to gain from this discussion is

that the problem of vanishing gradients and exploding gradients is actually a more general problem of unstable gradients.

This problem was actually a huge barrier to training neural networks in the past, and now we can see why that is.

In a later video, we'll talk about techniques that have been developed to combat against this problem.

For now, let's take our discussion to the comments.

Let me know what you're thinking.

✨ Aanbevolen Video

321 zinnen

Mixture of Experts Explained Visually: How Trillion-Parameter Models Actually Work

Daily ConversationUniversity Students

158 zinnen

Woordenschat en spreektips bij deze les

Deze spreekles is gebaseerd op de video “Vanishing & Exploding Gradient explained”. Deze woorden komen het vaakst terug: gradient, weight, network, vanishing, product. Deze video bevat 78 zinnen en 1279 woorden om na te spreken. Het gesproken deel duurt 7:19. De spreker praat in een natuurlijk tempo van ongeveer 175 woorden per minuut, dicht bij een alledaags gesprek. 87% van de woorden hoort bij de 3.000 meest gebruikte Engelse woorden, dus de woordenschat is goed te volgen.

Belangrijke woorden in deze video

15 woorden uit de video die het leren waard zijn, met uitspraak en betekenis:

Woord	Uitspraak	Betekenis
calculate werkwoord	/ˈkælkjʊleɪt/	berekenen, uitwerken
neural bijvoeglijk naamwoord	/ˈnʊɹəl/	neuraal
occur werkwoord	/əˈkɜː/	plaatsvinden
explode werkwoord	/ɪkˈsploʊd/	exploderen, ontploffen
refer werkwoord	/ɹɪˈfɜː/	verwijzen, doorverwijzen
essentially bijwoord	/ɪˈsɛnʃəli/	essentieel
layer zelfstandig naamwoord	/ˈleɪ̯ɚ/	laag
descent zelfstandig naamwoord	/dəˈsɛnt/	afdaling
unstable bijvoeglijk naamwoord	/ʌnˈsteɪ.bəl/	instabiel
reside werkwoord	/ɹɪˈzaɪd/	wonen
technique zelfstandig naamwoord	/tɛkˈniːk/	techniek
obtain werkwoord	/əbˈteɪn/	verkrijgen
hence bijwoord	/ˈhɛns/	van hier, heen
objective zelfstandig naamwoord	/ɒbˈd͡ʒɛk.tɪv/	object
difficulty zelfstandig naamwoord	/ˈdɪfɪkəlti/	moeilijkheid

Zinnen om te herhalen

Korte, volledige zinnen uit de video die je in alledaagse gesprekken kunt gebruiken:

Hey, what's going on everyone?
Here we'll cover the idea conceptually.
For now, let's take our discussion to the comments.
Let me know what you're thinking.

Grammatica in deze video

De structuren die de spreker het meest gebruikt, met de exacte woorden uit de video:

Structuur	In de video
Lijdende vorm be + voltooid deelwoord — het gaat om wat er gebeurt, niet om wie het doet	is calculated · will be needed · be included
Present perfect have/has + voltooid deelwoord — iets uit het verleden dat nu nog telt	have developed · have been developed

Uitspraak om op te letten

De spreker gebruikt 20 samentrekkingen en verkorte vormen, zoals we'll, we're, don't. Spreek ze kort uit, zoals je ze hoort.

De klanken “sh” en “zh”: essentially /ɪˈsɛnʃəli/, artificial /ˌɑː.tɪˈfɪʃ.əl/, proportion /pɹəˈpɔɹʃən/, implication /ˌɪmpləˈkeɪʃən/, vanish /ˈvænɪʃ/
Lange woorden — let op de klemtoon: essentially /ɪˈsɛnʃəli/, difficulty /ˈdɪfɪkəlti/, artificial /ˌɑː.tɪˈfɪʃ.əl/, derivative /dɪˈɹɪvətɪv/, implication /ˌɪmpləˈkeɪʃən/

Klanken die Nederlandstaligen lastig vinden:

Stemhebbende eindmedeklinker — /b/, /d/, /g/, /z/, /v/ niet verscherpen: explode /ɪkˈsploʊd/, minimize /ˈmɪn.ɪˌmaɪz/, reside /ɹɪˈzaɪd/, objective /ɒbˈd͡ʒɛk.tɪv/, involve /ɪnˈvɑlv/
/æ/ — opener dan de Nederlandse “e”: calculate /ˈkælkjʊleɪt/, barrier /ˈbæɹi.ə/, vanish /ˈvænɪʃ/, stochastic /stəˈkæstɪk/, subtract /səbˈtɹækt/
/g/ — een harde plofklank, geen Nederlandse “g”: greatly /ˈɡɹeɪ̯t.li/, gradient /ˈɡɹeɪdiənt/

Zo oefen je met deze video

Luister de hele video één keer zonder te spreken en noteer de woorden die je niet kent.
Begin op 0,75× snelheid, spreek zin voor zin na en ga terug naar normale snelheid zodra het makkelijk gaat.
Neem jezelf op en vergelijk met het origineel; let daarbij op woorden als calculate, neural, occur.

Wat is de Shadowing-techniek?

Shadowing is een wetenschappelijk onderbouwde taalleermethode die oorspronkelijk is ontwikkeld voor professionele tolkentraining en gepopulariseerd door polyglot Dr. Alexander Arguelles. De methode is eenvoudig maar krachtig: je luistert naar native Engelse audio en herhaalt het onmiddellijk hardop — als een schaduw die de spreker volgt met slechts 1–2 seconden vertraging. In tegenstelling tot passief luisteren of grammaticadrills, dwingt shadowing je hersenen en mondspieren om echte spraakpatronen tegelijkertijd te verwerken en te reproduceren. Onderzoek toont aan dat het de uitspraaknauwkeurigheid, intonatie, ritme, verbonden spraak, luisterbegrip en spreekvaardigheid aanzienlijk verbetert — waardoor het een van de meest effectieve methoden is voor IELTS Speaking-voorbereiding en echte Engelse communicatie.

Shadowing-techniek: lees de volledige stap-voor-stap-gids →

Shadowing Practice: Vanishing & Exploding Gradient explained | A problem resulting from backpropagation - Learn English Speaking with Video

Mixture of Experts Explained Visually: How Trillion-Parameter Models Actually Work

OWASP Top 10 Explained

Vectors and 2D Motion: Crash Course Physics #4

BGP Overview

Enhancing AV Simulation with Generative AI - DRIVE Labs Ep. 36

Gaussian Naive Bayes, Clearly Explained!!!

P2 什么是机器学习【2024公认最好的 | 吴恩达机器学习 | 教程 | Machine Learning Specialization(超爽中英!)】

What is Inattentional Blindness? (Easiest Explanation)

Bỏ học để khởi nghiệp có phải lối tắt?

The GELU, SiLU and SwiGLU activation functions, clearly explained!!!

The End of Human Mathematics? AI's Recent Breakthrough -- And What's Next

LET THERE BE... Voltage? | Maxwell's Equation #2 Explained for Beginners