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Great explanation, Thank you so much !!

Brilliant explanation.

Hi! How many deep learning/computer science people here like seeing this kind of content in addition to our usual educational material as well? Let's build strong minds AND bodies! 🧠💪

2024 - had to install the latest version of py_game. also, had to use render mode human. it opened up the more animated display.

Awesome.

Thank God I came across your channel, scientist these days just like to sound smart

Bro brilliant work. Legendary!!

thank you so much

What exactly is the difference between the q-network and the policy-network? And doesnt solving the bellman equations require us to solve a huge system of equations, which would take way too long?

I always call anything that looks like that a grasshopper. I'm sure there is some difference between all the grasshopper like creatures, but I don't know them. I get big ones in my greenhouse.

At what age I started liking insects? Why you named deep lizard to your organisation?

Nice video❤❤❤❤

Hi! How many deep learning/computer science people here like seeing this kind of content in addition to our usual educational material as well? Let's develop both our minds AND our connection to nature! 🧘‍♀️🌿

Hi! How many deep learning/computer science people here like seeing this kind of content in addition to our usual educational material as well? Let's build strong minds AND bodies! 🧠💪

0.04876

proof that kettlebells > dumbbells

Thanks a lot, much appreciated

Great video bro. Massive respect from IIT kharagpur. Keeping those fun moments inside makes it more entertaining

Honestly. Ripped! What (lifting?) shoes are they? Looks like lifting barefoot shoes?

the exploration_decay_rate is directly proportional to 1 - epsilon. hence, we will have more no. of exploitation cases before we have explored our environment, hence the inconsistency.

Great explanation...also you are gorgeous :)

it's too sus to be able to do it at the gym

This is actually a great exercise to implement on your back day, and her form is good as well.

holy crap that's awful form

Thirst trap. Really lost some respect here. Plenty of this kind of “content” from women out there. Stick to the computer stuff and don’t demean yourself!

Hi! How many deep learning/computer science people here like seeing this kind of content in addition to our usual educational material as well? Let's build strong minds AND bodies! 🧠💪

Jensen should hit that nose candy more often 😂

Thanks a lot for the course.. It was very important to me!

Where is pooling layer

13913

Learned new things . thank you.

How can we deploy it?

It's a great series

I still don't understand why we use activation function. I don't think this was addressed properly in the video. What's wrong with just letting the output be the weighted sum? If it was about binarizing the output to imitate the activation and deactivation of a biological neuron then it would make sense but we don't really do that necessarily, do we? Sigmoid function and ReLU both will give granular values so what exactly was the point of recalling the activation of biological neurons? Disappointingly poor explanation.

I love the course due to her voice!!! It is exremly loveable

Everyone wishes they saw your video 5 years back 😅

I am wondering if I specially dumb or something. I am yet to finish this video but since I am totally confused I felt compelled to make this comment. In previous video of this series, you said that the dimension of a tensor is the number of indexes needed to obtain an element from the tensor. From the slide displayed at 1:00, it seems that rank is the same as dimension because you are asserting that a 2d array (indexes = 2 and thus, dimension = 2) has rank 2. Then, from 1:47 to 1:56, you state that a tensor of rank 2 means that tensor has 2 dimensions or 2 axes. You didn't at all define what these terms mean but merely pointed to the notion of indexes. In all this, I only understood what an indexes are, that's all. I am sorry but, I, personally, found this to be a frustratingly poor explanation.

How do you represent the trajectory including the final state? Like this? S_0, A_0, R_1, S_1, A_1, R_2, …, R_T, S_T If not, what is and why?

Briliantly explained! Super clear and esay to understand!

Hay errores: *Donde dice tensorflow.keras. ....., borrar tensorflow. , que comience desde keras. ..., porque keras trabaja ahora como módulo independiente *Esto no es error pero puede confundir: en os.chdir('data/dogs-vs-cats'), puede llevar dentro de las comillas el dir relativo o el absoluto, por ej: 'M:/Descargas F en M/Varios archivos/dogs-vs-cats' , respetando la barra hacia la derecha, y una sola barra después de la letra de unidad y los 2 puntos *El indentado está mal. Los for i in random. ... , van a la altura del if os.path.isdir ... y el os.chdir('../../') también a la misma altura de indentado. Si lo ejecutamos así, crea los directorios, pero no les carga las imágenes, puede cargar una sola la primer pasada, porque los for con esa indentación son una condición del if, que está chequeando la creación de los subdirectorios, condición que se cumple en la primer pasada del código

Amazing series of videos. Thank you so much

t.reshape(3, -1).squeeze(dim=1)

well that agent lost , this made me laugh so so much!! Thank you so much, even after all there years, it is still so so awesome

Is that you or a clone? I really can't tell 😅😅

Is that you or a clone? I really can't tell 😅😅

where can i find examples using DQN?

Hi! Just watched the video, thank you for the insight! However, I do have one question. Once the filter is applied to the input image and slid from pixel to pixel, wouldn't the value be stored in the second row, second pixel from the left? (Center pixel of the first 3x3 matrix), and it will result in a smaller feature map?

It's just a way of showcasing ass. Focus on ml vids please

Hi! How many deep learning/computer science people here like seeing this kind of content in addition to our usual educational material as well? Let's build strong minds AND bodies! 🧠💪