We are so lucky to be alive at a time when we can attend these types of lectures for free

Here's what I love about your lectures: You give the intuition and logic behind the architectures and this helps a lot as opposed to the stone tablet thrown down from the heavens approach. Not only is this important for learning but it also stimulates intuition for the next set of innovations!

This entire series on Deep Learning is a great pleasure to listen to and brainstorm about. There are limitless possibilities for AI applications, and I'm highly inspired for some of them.

I'm self-studying deep learning without going through any school so I need sharers like you . thank you very much!

the videos, slides and explanation keep getting better.

Such a brilliant session! I am totally in the awe of this course, and loved the way Dr. Alex dissects the concepts in simplified way!

Thanks Alex Amini and MIT ) 🥰😊

we are waiting Thanks Alex Amini

I really appreciate you posting this material!! Thank you 🙏

This presentation is really well put together.

Thank you for uploading this video ❤

Great Lecture. Thank you very much!

It's unbelievable that you're doing this for free. Thanks a lot Sir. Your explanation is very clear and in an easy manner. Thanks again Sir.

Hello Alex! It’s great to join your class here 👍

Thank you very much for sharing!

I am in awe. You have delivered these concepts so beautifully that I didn't need to look up into other resources. I have recently made a switch to this field and you happened to be my biggest motivator to pursue it further. Thank you.

Thanks Alex for sharing these lectures online. A quick comment about fully connected layer causing loss of spatial information @14:40. I don't think fully connected layers result in spatial information loss. All your network has to do is identify that certain indices in the flattened vector correspond to specific locations in the spatial map. We can lose some translation/spatial invariance but not necessary spatial information loss.

we are waiting excitingly 😀

Wow !! Really awesome lecture Alex sir . Nice explanation with perfect slides

Alexander Amini has splendid presentation skills

Damet garm professor Amini. What a pleasure to take these sessions.

Thank you so much for this lecture!

love from india as I'm not able to study at MIT but this series helps me a lot and I hope lots of people but if you can add the labs lecture that how we can build this practically so it would be a great honor

Thanks sir for this wonderful explanation.

Greatly thankful to your efforts for making this great lectures free and so easily accessible, thank you Alexander Amini

Thank you so much Alex. So much learning from you. God Bless you. 😊😊

Thank you, Max, for these amazing lessons ftom you & Ava. Could you please share a little more how I can learn more about the vision system example you used in the lectures which helps the visually impaired run the trail?

The Convolutional Neural Network, one of my Passion and with MIT is an ART

Thanks for sharing!

Thank you so much! I hope to see the 2024 version as soon as possible (I will brush it again

This free course on UKposts is WAY better then a $2k course I took online from Carnegie Mellon University on CV...These MIT lectures are far more in-depth and provide much better examples...

Now i have learned the whole CNN working. Great explanation

Wow, it's ridiculous, the more it goes, the better - I love every single minute of this course - A huge thank you!

I can just say many thanks! I’ve been taking courses online campus about such topics but all make no sense for me, now i understand it, many thanks!

What an insightful lecture! Appreaciations prof. Alexander

Super 👍

Hello Alexander, Please make a dedicated video on "Reinforcement Learning with Human Feedback"

Great. Thanks for sharing

Thanks a lot.

Awesome!

It calls for knowing the root of consciousness and creativity in life

thank you!

Thank you :)

Great Lecture, but last week Ava said this year's CV lecture will be about Vision Image Transformer!

DL MIT classes are great overall, but there are three small errors in this lecture, please correct if you can: - as mentioned in the comments before, fully connected networks do keep spacial relationships, they actually have a much more rigid spatial relationship retention than CNNS - CNNs can be seen as a fully connected network with weight sharing and the great advantage is to force the network to give the same feature for the same input anywhere in the image (this makes the network spatially equivariant, or sometimes wrongly referenced as spatially invariant). Of course CNNs require less compute also. - Pooling (while it effectively reducing image size) has the main objective of spatially invariance, meaning that we can shift the image and get the same feature at some latent level (up to a point).

Sir, I am fond of deep- learning. And these lectures are amazing. Sir may you please share something you do in lab. I get really curious about that. It will become amazing if those algorithms can be used directly in directly in form of code.

It is time we have to go further to sense, smell and feel. For this we need to look deep into life. The future exists in mimicking life. Knowing life beyond the mind and going inward.

thanks a lot

Thank you Dr

I hope to explain backpropagation in the conv layer

I get so excited about the use cases and various possibilities of using CNNs. Excellent presentation. A master class in simplifying a complex subject.

excellent.

Where do I find the labs for the practice?

Hi, After CNN performed, and pixels are flatten then can we add VAE with GAN to create the same probability distribution of input flattened array and as well some alternative derivativea Or distribution, like cycle gan, road to map. Am I connecting it correct or again watch the videos, Thank you for the videos

alex, i wanna ask you last lecture was sequencing in the website there's code lab related to that lecture can i walk through or you gonna assigning

great lectures, are the lab portions of the course going to be available?

So the random box instance is an n^2 algorithm and the pictures parameters by all means is just the color density and location plotted

Does converting audio to mel spectrograms and classify with image classification models is right approach?

Thank you to the author. Does anybody get from this video how all this works with shift/rotation/scale of the image?

Great lecture and amazing teaching but I am having difficulty to grasp the code of lab. Is there any resources or anything better solution for it?

Min 41:17. Why we look for 1024 layers as a "result" before the output?

HI Alexander Amini: Vivid Comprehensible Learner Sensitive presentation! Thanks! The AI sector currently the EXCLUSIVE domain of a MIGHTY minority Elite In comparison to billions of individuals - undoubtedly Future USERS?!! Mobile accessible to the fisherman- The English Teacher -Engineers - Ballet dancer - diverse clientele in ALL levels of Humanity- MIT And these AI Start Ups urgently expedite these kind of MIT classes to ensure Literacy in AI USE!!!! PLEASE ASAP 15.12.2023

wow, how i wish i could ever be in MIT

hello, I'm giving a course at my university on Brazil about Machine Learning, and i would like to ask to use some of your slides and translate your material for the next leasson which is about CNN

The math at 32:13, a double sum, is incorrect. Firstly, the filter is indexed with i,j starting at 1, so the input and output matrices are also probably indexed from 1. This means that the first output has p = 1, but since this is added to i, so we start with the row index of x being 2. Basically, we have to add p-1, rather than p, and q-1 rather than q. Secondly, The stride is meant to be 2, so we start our filter at double where it would be in the output. So instead of (p-1), we need to add 2(p-1). In conclusion, the subscript of x should be i + 2(p-1), j + 2(q-1) ; unless it shouldn't and I made a mistake 💀 Otherwise, loving the lectures 👍

7:00

Are NNs not always fully connected? I just assumed they were from the math, unless a given weight is zero.

Should we understand them as a sw developer ? do we need all these theoretical stuff?

Can we get access to software labs with some hands on learning? I know codes are available but something else where we can learn from scratch.

Waiting The Third Lesson~

7:38

12:00

im waiting

29:00

42:00

The best tutorial of CNN on earth.

I was very impressed when I heard that the transformer model was created by a Vietnamese person

❤

"How to drive and steer this car into... the future"😄

### Defining a network Layer ### # n_output_nodes: number of output nodes # input_shape: shape of the input # x: input to the layer class OurDenseLayer(tf.keras.layers.Layer): def __init__(self, n_output_nodes): super(OurDenseLayer, self).__init__() self.n_output_nodes = n_output_nodes def build(self, input_shape): d = int(input_shape[-1]) # Define and initialize parameters: a weight matrix W and bias b # Note that parameter initialization is random! self.W = self.add_weight("weight", shape=[d, self.n_output_nodes]) # note the dimensionality self.b = self.add_weight("bias", shape=[1, self.n_output_nodes]) # note the dimensionality def call(self, x): '''TODO: define the operation for z (hint: use tf.matmul)''' z =tf.matmul([x,self.n_output_nodes]) '''TODO: define the operation for out (hint: use tf.sigmoid)''' y = tf.sigmoid(z+self.b) return y # Since layer parameters are initialized randomly, we will set a random seed for reproducibility tf.random.set_seed(1) layer = OurDenseLayer(3) layer.build((1,2)) x_input = tf.constant([[1,2.]], shape=(1,2)) y = layer.call(x_input) # test the output! print(y.numpy()) mdl.lab1.test_custom_dense_layer_output(y) hello can anyone help me with this pls

Can you develop and AI that will teach me how to learn deep learning.

you try to figure out how to program the human mind, but you can't until you are able to create that spark of consciousness, that divine particle that makes a brain a brain.

the raw data comes from the universe where we live.

the spark is what the drive is, but the drive changes according to the input. but it is exhaustible, it is the human body that dies. that's your second problem hahaha And now tell me if the program that runs in a person is created by genes or by that spark. I would describe it as a synopsis.

I think I was the only one waiting 😅

why is lena still used in these lectures?

Don't use dark theme for code: many chars are badly visible.

I wonder if the 2 AIs can argue each other. haha and there could be a problem if you have 100 AI. 😂

Spider net

if I were to describe to you how I perceive the world, you would turn the brown thing into a textile.

you're still only describing how the human eye works and what it sees turning into sex. 😂

All man yone 2 videos blady chicken hahah

Where can I find the Tensor Flow labs to practice?

40:00