Deep Learning Interview Prep Course
ฝัง
- เผยแพร่เมื่อ 3 พ.ค. 2024
- Prepare for a job interview about deep learning. This course covers 50 common interview questions related to deep learning and gives detailed explanations.
✏️ Course created by Tatev Karen Aslanyan.
✏️ Expanded course with 100 questions: courses.lunartech.ai/courses/...
⭐️ Contents ⭐️
⌨️ 0:00:00 Introduction
⌨️ 0:08:20 Question 1: What is Deep Learning?
⌨️ 0:11:45 Question 2: How does Deep Learning differ from traditional Machine Learning?
⌨️ 0:15:25 Question 3: What is a Neural Network?
⌨️ 0:21:40 Question 4: Explain the concept of a neuron in Deep Learning
⌨️ 0:24:35 Question 5: Explain architecture of Neural Networks in simple way
⌨️ 0:31:45 Question 6: What is an activation function in a Neural Network?
⌨️ 0:35:00 Question 7: Name few popular activation functions and describe them
⌨️ 0:47:40 Question 8: What happens if you do not use any activation functions in a neural network?
⌨️ 0:48:20 Question 9: Describe how training of basic Neural Networks works
⌨️ 0:53:45 Question 10: What is Gradient Descent?
⌨️ 1:03:50 Question 11: What is the function of an optimizer in Deep Learning?
⌨️ 1:09:25 Question 12: What is backpropagation, and why is it important in Deep Learning?
⌨️ 1:17:25 Question 13: How is backpropagation different from gradient descent?
⌨️ 1:19:55 Question 14: Describe what Vanishing Gradient Problem is and it’s impact on NN
⌨️ 1:25:55 Question 15: Describe what Exploding Gradients Problem is and it’s impact on NN
⌨️ 1:33:55 Question 16: There is a neuron in the hidden layer that always results in an error. What could be the reason?
⌨️ 1:37:50 Question 17: What do you understand by a computational graph?
⌨️ 1:43:28 Question 18: What is Loss Function and what are various Loss functions used in Deep Learning?
⌨️ 1:47:15 Question 19: What is Cross Entropy loss function and how is it called in industry?
⌨️ 1:50:18 Question 20: Why is Cross-entropy preferred as the cost function for multi-class classification problems?
⌨️ 1:53:10 Question 21: What is SGD and why it’s used in training Neural Networks?
⌨️ 1:58:24 Question 22: Why does stochastic gradient descent oscillate towards local minima?
⌨️ 2:03:38 Question 23: How is GD different from SGD?
⌨️ 2:08:19 Question 24: How can optimization methods like gradient descent be improved? What is the role of the momentum term?
⌨️ 2:14:22 Question 25: Compare batch gradient descent, minibatch gradient descent, and stochastic gradient descent.
⌨️ 2:19:12 Question 26: How to decide batch size in deep learning (considering both too small and too large sizes)?
⌨️ 2:26:01 Question 27: Batch Size vs Model Performance: How does the batch size impact the performance of a deep learning model?
⌨️ 2:29:33 Question 28: What is Hessian, and how can it be used for faster training? What are its disadvantages?
⌨️ 2:34:12 Question 29: What is RMSProp and how does it work?
⌨️ 2:38:43 Question 30: Discuss the concept of an adaptive learning rate. Describe adaptive learning methods
⌨️ 2:43:34 Question 31: What is Adam and why is it used most of the time in NNs?
⌨️ 2:49:59 Question 32: What is AdamW and why it’s preferred over Adam?
⌨️ 2:54:50 Question 33: What is Batch Normalization and why it’s used in NN?
⌨️ 3:03:19 Question 34: What is Layer Normalization, and why it’s used in NN?
⌨️ 3:06:20 Question 35: What are Residual Connections and their function in NN?
⌨️ 3:15:05 Question 36: What is Gradient clipping and their impact on NN?
⌨️ 3:18:09 Question 37: What is Xavier Initialization and why it’s used in NN?
⌨️ 3:22:13 Question 38: What are different ways to solve Vanishing gradients?
⌨️ 3:25:25 Question 39: What are ways to solve Exploding Gradients?
⌨️ 3:26:42 Question 40: What happens if the Neural Network is suffering from Overfitting relate to large weights?
⌨️ 3:29:18 Question 41: What is Dropout and how does it work?
⌨️ 3:33:59 Question 42: How does Dropout prevent overfitting in NN?
⌨️ 3:35:06 Question 43: Is Dropout like Random Forest?
⌨️ 3:39:21 Question 44: What is the impact of Drop Out on the training vs testing?
⌨️ 3:41:20 Question 45: What are L2/L1 Regularizations and how do they prevent overfitting in NN?
⌨️ 3:44:39 Question 46: What is the difference between L1 and L2 regularisations in NN?
⌨️ 3:48:43 Question 47: How do L1 vs L2 Regularization impact the Weights in a NN?
⌨️ 3:51:56 Question 48: What is the curse of dimensionality in ML or AI?
⌨️ 3:53:04 Question 49: How deep learning models tackle the curse of dimensionality?
⌨️ 3:56:47 Question 50: What are Generative Models, give examples?
"Big thanks to the team of FreeCodeCamp, and especially to Beau for this incredible opportunity to collaborate on this AI content. ❤It's a privilege to contribute to worlds leading and most accessible online coding platforms, that shapes coding education and industry. Looking forward to more collaborations in the future!" Tatev Aslanyan
! Q.,.....
Finally a interview prep video other than Full stack Development.
😂😂
I was also tired of all these videos😂😂😂😂
I love this. Thinking about applying for another job but always nervous when doing interviews. I’ve always gotten hired but still would love to hit them with a woah factor during interview. I feel like with the skills I have and the blown out interview would help me with me negotiating my salary
AWESOME!!!!!!! Interview preparation is a whole process that involves a lot skills, beside technical skill you need to know how to transmit your knowledge in a clear and effective way. Thank you for this and for all the FCC fantastic content!
This was invaluable for interview prep! Not just the tech, but the communication tips really resonate.
Thanks for all the FCC gems!
This is just amazing, im having a interview this next week, and this course will be my todo of the weekend. Thanks a lot Tatever and FreeCodeCamp
3:05:36 small erratum - gpt style models are decoder only and bert model (sentiment analysis) architecture is encoder only. Btw, great stuff. Have a nice one.
Thank you so much, really helpful.
I'd correct a mistake: 47:00 the leaky ReLU should not be for but , and to generalize, that can be any number between 0 and 1.
Great job in explaining the content, looking forward to what you have got next in store.
Could you please consider creating a video discussing computer vision interview questions?
It's very clearly made vedio and make sure it helpful to us for clearing any interview
Most attractive thumbnail of freecodecamp 🥴
Good stuff, thanks!
ALSO REQ FOR IMP TOPICS IN DATA SCIENCE TOO
Please make a course on machine learning for data science interview prepration.
This is interesting and a nice refresher. Is there one for machine learning in the works?
Wow. The tech industry should really be ashamed of itself for creating an HR process this hostile.
She is so excellent but why she doesn't have a TH-cam channel?
Maybe she's not so used to content creation or doesn't get enough time to devote to it? :)
I also asked her and she said she didn't have time
Running a TH-cam channel requires a lot of time.
I have the same question 😢
There are many more people so good outside TH-cam.😅
Thank ypu for making a lot of helpful Stuff free ❤
its no free 9M subscribers is mony
She's so brave!
Why?
Thank you the lectures. for Answer 13, my understanding is reversed.
Thank you so much FCC for this great content! This will really help me.
In Answer 7, as shown in the chart on the right, shouldnt the formula be 'F(z) = 0.01z' for the negative case?
Do we have similar one for machine learning and natural language processing?
ML interviews seem far easier than Software interviews. (Assuming you've a basic understanding of calculus & linear algebra). Maybe cause software is more saturated & easy to outsource?
FYI, the mic orientation is incorrect.
Thank you
very helpfull i hope we are nice to meet you
☑️
❤
Thanks for this 🎉
The graph for leaky ReLU is so wrong at 44:50. It does not match the equation.
Good evening
It's different
Please put game start in unity
Why no sound in video?
Like like like❤❤❤❤❤❤❤
Thanks Karen! Great job!
Babita ji aap ?
Thanks for slime and very helpful explanation. Excellent work.
I think Hessian is pronounced as heh-see-an, not as Haitian.
Excellent.
Too easy
The right side
BANGLADESH
Great job
😊 thank you
Interesting educational video! Definitely a like!
and she is pretty
SPOT THE DRACO🐍=53
im first commenter
you want a medal?
😢
What’s your OF
I am sorry, but this video feels superficial and the vocab is being used just for the sake of being used and not to explain. I am a fan of FCC but not this one
If you need a video like this for a job interview, you should not be at that interview... Just saying
Thank you