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deeplizard
United States
Приєднався 22 сер 2017
Building collective intelligence
Computational Graphs For Neural Networks - Visual Code Demo
Computational Graphs For Neural Networks - Visual Code Demo
Переглядів: 4 121
Відео
Transposed Convolutions in Neural Networks - Interactive Demo App
Переглядів 2,6 тис.7 місяців тому
Transposed Convolutions in Neural Networks - Interactive Demo App
Upsampling in Neural Networks - Interactive Demo App
Переглядів 2 тис.7 місяців тому
Upsampling in Neural Networks - Interactive Demo App
Training A Diffusion Model - Stable Diffusion Masterclass
Переглядів 5 тис.8 місяців тому
Training A Diffusion Model - Stable Diffusion Masterclass
Intuitive Intro to Image Generation - Stable Diffusion Masterclass
Переглядів 1,6 тис.8 місяців тому
Intuitive Intro to Image Generation - Stable Diffusion Masterclass
Components Of Stable Diffusion - Stable Diffusion Masterclass
Переглядів 5 тис.8 місяців тому
Components Of Stable Diffusion - Stable Diffusion Masterclass
Intro to Latent Diffusion Models - Stable Diffusion Masterclass
Переглядів 2,3 тис.8 місяців тому
Intro to Latent Diffusion Models - Stable Diffusion Masterclass
Stable Diffusion Masterclass - Course Overview
Переглядів 3 тис.9 місяців тому
Stable Diffusion Masterclass - Course Overview
Terence McKenna Explores Art, Psychedelics, Culture, Creativity, and Consciousness
Переглядів 2,8 тис.Рік тому
Terence McKenna Explores Art, Psychedelics, Culture, Creativity, and Consciousness
Does AI Art Have Meaning - Women of the World Project Overview
Переглядів 1,8 тис.Рік тому
Does AI Art Have Meaning - Women of the World Project Overview
AI Art Taking World By Storm - Diffusion Models Overview
Переглядів 1,8 тис.Рік тому
AI Art Taking World By Storm - Diffusion Models Overview
AI Art Course - Google Colab & Google Drive Setup - Automatic1111 & Stable Diffusion
Переглядів 7 тис.Рік тому
AI Art Course - Google Colab & Google Drive Setup - Automatic1111 & Stable Diffusion
AI Art Course Install & Setup - Automatic1111 & Stable Diffusion
Переглядів 4,3 тис.Рік тому
AI Art Course Install & Setup - Automatic1111 & Stable Diffusion
AI Art For Beginners - Stable Diffusion Crash Course Syllabus Overview
Переглядів 6 тис.Рік тому
AI Art For Beginners - Stable Diffusion Crash Course Syllabus Overview
Stable Diffusion CFG Scale Examples - Two Synths Facing Each Other
Переглядів 3 тис.Рік тому
Stable Diffusion CFG Scale Examples - Two Synths Facing Each Other
Stable Diffusion AI Hairball Inputs - CFG Scale 1 to 30 w/ High Resolution
Переглядів 1,5 тис.Рік тому
Stable Diffusion AI Hairball Inputs - CFG Scale 1 to 30 w/ High Resolution
AI Hairball - Stable Diffusion + ChatGPT
Переглядів 1,9 тис.Рік тому
AI Hairball - Stable Diffusion ChatGPT
AI Art with Stable Diffusion (Women of the World)
Переглядів 3,7 тис.Рік тому
AI Art with Stable Diffusion (Women of the World)
Intro to Natural Language Processing (NLP)
Переглядів 3,5 тис.Рік тому
Intro to Natural Language Processing (NLP)
NLP Intro For Text - Sentiment Analysis With Deep Learning - Course Overview
Переглядів 10 тис.Рік тому
NLP Intro For Text - Sentiment Analysis With Deep Learning - Course Overview
Relu Activation Function - Deep Learning Dictionary
Переглядів 26 тис.Рік тому
Relu Activation Function - Deep Learning Dictionary
Sigmoid Activation Function - Deep Learning Dictionary
Переглядів 9 тис.Рік тому
Sigmoid Activation Function - Deep Learning Dictionary
Activation Functions - Deep Learning Dictionary
Переглядів 8 тис.2 роки тому
Activation Functions - Deep Learning Dictionary
Neural Network Weights - Deep Learning Dictionary
Переглядів 13 тис.2 роки тому
Neural Network Weights - Deep Learning Dictionary
Neural Network Nodes - Deep Learning Dictionary
Переглядів 5 тис.2 роки тому
Neural Network Nodes - Deep Learning Dictionary
Neural Network Layers - Deep Learning Dictionary
Переглядів 4,9 тис.2 роки тому
Neural Network Layers - Deep Learning Dictionary
Deep Learning with TensorFlow - Course Reflection
Переглядів 2,7 тис.2 роки тому
Deep Learning with TensorFlow - Course Reflection
Deep Learning with PyTorch - Course Reflection
Переглядів 3,4 тис.2 роки тому
Deep Learning with PyTorch - Course Reflection
Max Pooling vs No Max Pooling - Deep Learning Course
Переглядів 3,9 тис.2 роки тому
Max Pooling vs No Max Pooling - Deep Learning Course
Training Multiple Networks - PyTorch Deep Learning Course
Переглядів 2,6 тис.2 роки тому
Training Multiple Networks - PyTorch Deep Learning Course
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
I thought she was doing rotator cuffs for a second 😂
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!
lol how is this a thirst trap??? Bozo incel
wtf? I mean i personally prefer the other content because im not into gym content in general, but calling her a thirst trap? Are you saying women cant upload gym content or else its a thirst trap? If it were a man doing this exercise youd have no complaints
Besides, there is an audience for this type of content even among her subscribers, as seen from the comments from other shorts.
@@poxi-rann9199 no, if it were a man who came out of left field with this I’d feel the same way. Great job feigning sexism tho you got some woke points :)
Sad. Please stay indoors and don't communicate with women... you're not qualified.
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! 🧠💪
Both are cool. Could you make some videos about KAN?
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 😅😅
_I could a tale unfold whose lightest word_ _Would harrow up thy soul_ 😅
@@deeplizard You are definitely AI!
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?
If it is an 28x28 image input, it will result in a 26x26 feature map? (28-3+1)
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! 🧠💪
Yes , physically and mentally strong is important
I used to be really into physical training, but left due to getting into a negative spiral and having a toxic relationship with it. Since then I have gotten really sedentary, which has really affected my quality of life. It is nice seeing these two different things together, and I think it is important from both sides (“gym culture” and from the computer science / STEM) that they promote and showcase each other. I am a bit worried about starting with going to the gym for “proper training” like you due to my history with gym culture and my tendency towards not being able to moderate myself. I’ll start with some easier body weight stuff maybe. It is really motivating seeing you post this stuff, because it is a reminder for me to get up and do something every once in a while. TLDR: I like it. Post more!
Thanks for taking the time to share! I hope you can develop a healthy relationship with fitness. Perhaps like you said, starting with basics and maybe even outdoors or at home. Glad to hear you see the value in the mix of content as well 😊 All the best on your journey!
This series really moves along nicely. New concepts are introduced at just the right level of detail to get the job done. This streamlines explanations and allows rapid progression by not overloading working memory.
0:50 Kerbal Space Program music is goated
Ai moved a long way
At 7:37, why do you have dense layer as the first layer in CNN?
Take a shot every time she blinks
Hi, where can I obtain the dogs and cats images?
At 7:53, why do we need to change the order of the training dataset? I think the original version is better since the last 10% of the samples (0.1 * 1050 = 105) contain both people (young + old) that are (positive + non-positive) of the test. *******************************************Old version******************** for i in range(1000): random_younger = randint(13, 64) train_samples.append(random_younger) train_labels.append(0) random_older = randint(65, 100) train_samples.append(random_older) train_labels.append(1) for i in range(50): random_younger = randint(13, 64) train_samples.append(random_younger) train_labels.append(1) random_older = randint(65, 100) train_samples.append(random_older) train_labels.append(0)
is there a new version for pytorch specifically?
Perfect introductory playlist for RL
Is the concept same as Actor Critic method? Is the 2nd network Critic?
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! 🧘♀️🌿
What do mean by acceptable levels? Is there any specific guide to define baseline it should reach?
I'm new into the RL algorithms and i didn't find any resources that explained as good as you, i watch the entire course and understand everything, thank you so much 💕
I did exactly the same thing but somehow my game is not rendering. i can only see the episode and you reached the goal text. Help please. Also I use version 1 of frozen lake
245 lbs?
205
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! 🧠💪
Well i got that part without having to go through these videos since I have some good maths background. But thanks for the revision.
He doesn’t explain tensor very well but overall good job
was watching a playlist and thought this was AI generated voice. Good to know its a real human :)