Ikram Mihan

Ikram Mihan

1594452999

10 Hours of TOP PyTorch Questions

Watch me live figure out answers to common PyTorch Questions! Better time stamps are in the comment section and will let you navigate to the questions you find interesting. This has not been heavily edited and planned. I was just curious to read through the forum because I thought it could be educational and thought I’d share my process of doing it. I don’t know the answers to all of these questions and we try to figure it out as we go! Also I was a bit slow in the beginning but feel I got more into it after some time :)

OUTLINE:

00:00:00 - Introduction
00:00:38 - num_workers in DataLoader
00:09:38 - Manually set gradients to zero
00:24:10 - model.eval() vs with torch.no_grad()
00:34:37 - Load part of a pretrained model
00:47:55 - optimizer.step() and loss.backward()
00:54:33 - Save and load models in PyTorch
01:00:18 - view() vs unsqueeze()
01:07:49 - Tensor to another type
01:18:07 - nn.ModuleList vs nn.Sequential
01:27:07 - .grad of intermediate variable
01:31:09 - GPU memory leaks
01:38:55 - Feedback old PyTorch for Kaggle
01:44:32 - clone() vs detach()
01:51:28 - nn.Functional vs nn module
02:02:51 - Finetuning in PyTorch
02:10:06 - transforms.Normalize()
02:18:15 - Gradient Clipping
02:23:08 - Detach, no_grad and requires_grad
02:28:03 - Euclidean Distance
02:29:44 - Freeze Network layers
02:34:41 - torch.backends.cudnn.benchmark
02:36:41 - Visualizing network weights
02:39:08 - DataLoader for various length data
02:45:42 - RunTimeError: backward second time
02:50:27 - Autograd graph
02:52:20 - Release GPU memory cache
02:56:20 - Converting int to one-hot vector
03:04:28 - Parameters of Model
03:09:35 - Features of image from Model
03:21:00 - Own loss functions PyTorch
03:30:36 - Weights and biases default init
03:36:10 - Normalize embedding vectors
03:40:15 - Adaptive Learning Rate
03:44:57 - .backward()
03:53:50 - pin_memory=True
03:57:18 - Checkpoint to CPU but saved GPU
04:01:10 - Multi Label Classification
04:18:34 - Variable to Numpy
04:19:19 - In-place operation
04:21:20 - Multiple criterion to a single loss
04:26:53 - List of Tensors to a Tensor
04:28:58 - nn.Module
04:31:10 - model.train() vs model.eval()
04:32:00 - Pretrained Embeddings
04:33:37 - Default init methods
04:34:27 - Visualizing graph
04:36:26 - Making PyTorch runs on the GPU
04:40:05 - BCELoss vs BCEWithLogitsLoss
04:42:50 - List of nn.Module in a nn.Module
04:43:50 - DataLoader vs Dataset
04:47:55 - Initialize hidden states for RNNs
04:58:02 - Printing Tensor Type
04:58:44 - Data Augmentation
05:09:08 - DataLoader for variable-size input
05:13:06 - Update PyTorch
05:13:56 - Training Half Precision
05:21:21 - LogSoftmax vs Softmax
05:24:28 - Why 3D tensors in RNNs
05:28:28 - BCEWithLogitsLoss vs MultiLabelSoftMarginLoss
05:29:39 - L2 regularization
05:31:35 - Equivalent of np.reshape()
05:33:03 - torch.nn.utils.clip_grad_norm
05:36:18 - Balanced sampling between classes
05:41:50 - RuntimeError: input is not contiguous
05:44:23 - Backward function for multiple losses
05:57:25 - Contiguous vs non-contigous
06:02:10 - Check gradient flow
06:05:24 - Passing weights CrossEntropyLoss
06:07:33 - Torch.no_grad()
06:08:44 - Custom loss functions
06:10:07 - Difference between LSTM and LSTMCell
06:12:32 - torch.nn.functional vs torch.nn
06:15:29 - .clamp
06:17:26 - Inverse normalization
06:20:51 - Append to Tensor
06:23:41 - Batch Norm
06:35:31 - Inferring shape flatten
06:36:25 - Device model/tensor stored on
06:40:26 - Speeding up the DataLoader
06:53:49 - torch.repeat vs torch.expand
06:57:44 - Cuda Out of Memory
07:05:09 - Way to clone a Model
07:08:15 - Identical transforms
07:16:11 - Random Seed Initialization
07:20:58 - PyTorch Coding Conventions
07:37:16 - Weights specific module in nn.Sequential
07:38:11 - nn.ReLU in init
07:45:38 - Clear GPU memory
07:49:19 - RunTimeError: Expected Long
07:51:12 - L2 reg term in loss
07:51:47 - Tour of PyTorch Internals
07:58:09 - VGG output - no softmax
07:58:48 - Element-wise bmm
08:01:01 - Too Many TimeStamps: See Pinned Comment

#pytorch #python #interview-questions

What is GEEK

Buddha Community

10 Hours of TOP PyTorch Questions

Lokesh Kumar

1603438098

Top 10 Trending Technologies Must Learn in 2021 | igmGuru

Technology has taken a place of more productiveness and give the best to the world. In the current situation, everything is done through the technical process, you don’t have to bother about doing task, everything will be done automatically.This is an article which has some important technologies which are new in the market are explained according to the career preferences. So let’s have a look into the top trending technologies followed in 2021 and its impression in the coming future in the world.

  1. Data Science
    First in the list of newest technologies is surprisingly Data Science. Data Science is the automation that helps to be reasonable for complicated data. The data is produces in a very large amount every day by several companies which comprise sales data, customer profile information, server data, business data, and financial structures. Almost all of the data which is in the form of big data is very indeterminate. The character of a data scientist is to convert the indeterminate datasets into determinate datasets. Then these structured data will examine to recognize trends and patterns. These trends and patterns are beneficial to understand the company’s business performance, customer retention, and how they can be enhanced.

  2. DevOps
    Next one is DevOps, This technology is a mixture of two different things and they are development (Dev) and operations (Ops). This process and technology provide value to their customers in a continuous manner. This technology plays an important role in different aspects and they can be- IT operations, development, security, quality, and engineering to synchronize and cooperate to develop the best and more definitive products. By embracing a culture of DevOps with creative tools and techniques, because through that company will gain the capacity to preferable comeback to consumer requirement, expand the confidence in the request they construct, and accomplish business goals faster. This makes DevOps come into the top 10 trending technologies.

  3. Machine learning
    Next one is Machine learning which is constantly established in all the categories of companies or industries, generating a high command for skilled professionals. The machine learning retailing business is looking forward to enlarging to $8.81 billion by 2022. Machine learning practices is basically use for data mining, data analytics, and pattern recognition. In today’s scenario, Machine learning has its own reputed place in the industry. This makes machine learning come into the top 10 trending technologies. Get the best machine learning course and make yourself future-ready.

To want to know more click on Top 10 Trending Technologies in 2021

You may also read more blogs mentioned below

How to Become a Salesforce Developer

Python VS R Programming

The Scope of Hadoop and Big Data in 2021

#top trending technologies #top 10 trending technologies #top 10 trending technologies in 2021 #top trending technologies in 2021 #top 5 trending technologies in 2021 #top 5 trending technologies

Sigrid  Farrell

Sigrid Farrell

1623718560

Top 10 Critical Spring Boot Interview Questions and Answers [For Beginners & Experienced]

offers powerful features for the rapid development of deployment-ready applications. It is the most used and best java framework for the development of scalable microservices and web applications.

If you want to become a domain expert, you have come to the right place. We have curated some the most repeatedly asked spring boot interview questions and answers to help you ace the interview.

Basic Spring Boot Interview Questions And Answers

Technical Spring Boot Interview Questions And Answers

Conclusion

#full stack development #interview question answer #spring boot interview questions answer #top spring boot interview questions #top 10 critical spring boot interview questions #answers

Top 130 Android Interview Questions - Crack Technical Interview Now!

Android Interview Questions and Answers from Beginner to Advanced level

DataFlair is committed to provide you all the resources to make you an android professional. We started with android tutorials along with practicals, then we published Real-time android projects along with source code. Now, we come up with frequently asked android interview questions, which will help you in showing expertise in your next interview.

android interview questions

Android Interview Questions – Get ready for your next interview

Android – one of the hottest technologies, which is having a bright future. Get ready to crack your next interview with the following android interview questions. These interview questions start with basic and cover deep concepts along with advanced topics.

Android Interview Questions for Freshers

1. What is Android?

Android is an open-source mobile operating system that is based on the modified versions of Linux kernel. Though it was mainly designed for smartphones, now it is being used for Tablets, Televisions, Smartwatches, and other Android wearables.

2. Who is the inventor of Android Technology?

The inventors of Android Technology are- Andry Rubin, Nick Sears, and Rich Miner.

3. What is the latest version of Android?

The latest version of Android is Android 10.0, known as Android Q. The upcoming major Android release is Android 11, which is the 18th version of Android. [Note: Keep checking the versions, it is as of June 2020.]

4. How many Android versions can you recall right now?

Till now, there are 17 versions of Android, which have their names in alphabetical order. The 18th version of Android is also going to come later this year. The versions of Android are here:

  • Android 1.0 – Its release is 23 September 2008.
  • Android 1.1 – Its release date is 9 February 2009.
  • Android 1.5 – Its name is Cupcake, Released on 27 April 2009.
  • Android 1.6 – Its name is Donut, Released on 15 September 2009.
  • Android 2.0 – Its name is Eclair, Released on 26 October 2009
  • Android 2.2 – Its name is Froyo, Released on 20 May 2010.
  • Android 2.3 – Its name is Gingerbread, Released on 06 December 2010.
  • Android 3.0 – Its name is Honeycomb, Released on 22 February 2011.
  • Android 4.0 – Its name is Ice Cream Sandwich, Released on 18 October 2011.
  • Android 4.1 – Its name is Jelly Bean, Released on 9 July 2012.
  • Android 4.4 – Its name is KitKat, Released on 31 October 2013.
  • Android 5.0 – Its name is Lollipop, Released on 12 November 2014.
  • Android 6.0 – Its name is Marshmallow, Released on 5 October 2015.
  • Android 7.0 – Its name is Nougat, Released on 22 August 2016.
  • Android 8.0 – Its name is Oreo, Released on 21 August 2017.
  • Android 9.0 – Its name is Pie, Released on 6 August 2018.
  • Android 10.0 – Its name is Android Q, Released on 3 September 2019.
  • Android 11.0 – As of now, it is Android 11.

5. Explain the Android Architecture with its components.

This is a popular android developer interview question

Android Architecture consists of 5 components that are-

a. Linux Kernel: It is the foundation of the Android Architecture that resides at the lowest level. It provides the level of abstraction for hardware devices and upper layer components. Linux Kernel also provides various important hardware drivers that act as software interfaces for hardwares like camera, bluetooth, etc.

b. Native Libraries: These are the libraries for Android that are written in C/C++. These libraries are useful to build many core services like ART and HAL. It provides support for core features.

c. Android Runtime: It is an Android Runtime Environment. Android Operating System uses it during the execution of the app. It performs the translation of the application bytecode into the native instructions. The runtime environment of the device then executes these native instructions.

d. Application Framework: Application Framework provides many java classes and interfaces for app development. And it also provides various high-level services. This complete Application framework makes use of Java.

e. Applications: This is the topmost layer of Android Architecture. It provides applications for the end-user, so they can use the android device and compute the tasks.

6. What are the services that the Application framework provides?

The Android application framework has the following key services-

a. Activity Manager: It uses testing and debugging methods.

b. Content provider: It provides the data from application to other layers.

c. Resource Manager: This provides users access to resources.

d. Notification Manager: This gives notification to the users regarding actions taking place in the background.

e. View System: It is the base class for widgets, and it is also responsible for event handling.

7. What are the important features of Linux Kernel?

The important features of the Linux Kernel are as follows:

a. Power Management: Linux Kernel does power management to enhance and improve the battery life of the device.

b. Memory Management: It is useful for the maximum utilization of the available memory of the device.

c. Device Management: It includes managing all the hardware device drivers. It maximizes the utilization of the available resources.

d. Security: It ensures that no application has any such permission that it affects any other application in order to maintain security.

e. Multi-tasking: Multi-tasking provides the users the ease of doing multiple tasks at the same time.

8. What are the building blocks of an Android Application?

This is a popular android interview question for freshers.

The main components of any Android application are- Activity, Services, Content Provider, and Broadcast Receiver. You can understand them as follows:

a. Activity- It is a class that acts as the entry point representing a single screen to the user. It is like a window to show the user interface.

b. Services- Services are the longest-running component that runs in the background.

c. Content Provider- The content provider is an essential component that allows apps to share data between themselves.

d. Broadcast receivers- Broadcast receiver is another most crucial application component. It helps the apps to receive and respond to broadcast messages from the system or some other application.

9. What are the important components of Android Application?

The Components of Android application are listed below:

  1. Widgets
  2. Intents
  3. Views
  4. Notification
  5. Fragments
  6. Layout XML files
  7. Resources

10. What are the widgets?

Widgets are the variations of Broadcast receivers. They are an important part of home screen customization. They often display some data and also allow users to perform actions on them. Mostly they display the app icon on the screen.

11. Can you name some types of widgets?

Mentioned below are the types of widgets-

a. Informative Widgets: These widgets show some important information. Like, the clock widget or a weather widget.

b. Collective Widgets: They are the collection of some types of elements. For example, a music widget that lets us change, skip, or forward the song.

c. Control Widgets: These widgets help us control the actions within the application through it. Like an email widget that helps check the recent mails.

d. Hybrid Widgets: Hybrid widgets are those that consist of at least two or more types of widgets.

12. What are Intents?

Intents are an important part of Android Applications. They enable communication between components of the same application as well as separate applications. The Intent signals the Android system about a certain event that has occurred.

13. Explain the types of intents briefly?

Intent is of three types that are-

a. Implicit Intents: Implicit intents are those in which there is no description of the component name but only the action.

b. Explicit Intents: In explicit intents, the target component is present by declaring the name of the component.

c. Pending Intents: These are those intents that act as a shield over the Intent objects. It covers the intent objects and grants permission to the external app components to access them.

14. What is a View?

A view is an important building block that helps in designing the user interface of the application. It can be a rectangular box or a circular shape, for example, Text View, Edit Text, Buttons, etc. Views occupy a certain area of the screen, and it is also responsible for event handling. A view is the superclass of all the graphical user interface components.

15. What do you understand by View Group?

It is the subclass of the ViewClass. It gives an invisible container to hold layouts or views. You can understand view groups as special views that are capable of holding other views, that are Child View.

16. What do you understand about Shared Preferences?

It is a simple mechanism for data storage in Android. In this, there is no need to create files, and using APIs, it stores the data in XML files. It stores the data in the pair of key-values. SharedPreferences class lets the user save the values and retrieve them when required. Using SharedPreferences we can save primitive data like- boolean, float, integer, string and long.

17. What is a Notification?

A notification is just like a message that shows up outside the Application UI to provide reminders to the users. They remind the user about a message received, or some other timely information from the app.

18. Give names of Notification types.

There are three types of notifications namely-

a. Toast Notification- This notification is the one that fades away sometime after it pops up.

b. Status Notification- This notification stays till the user takes some action on it.

c. Dialog Notification- This notification is the result of an Active Activity.

19. What are fragments?

A fragment is a part of the complete user interface. These are present in Activity, and an activity can have one or more fragments at the same time. We can reuse a fragment in multiple activities as well.

20. What are the types of fragments?

There are three types of fragments that are: Single Fragment, List Fragment, Fragment Transactions.

  1. Single Transactions can only show a single view for the user.
  2. List Fragments have a special list view feature that provides a list from which the user can select one.
  3. Fragment Transactions are helpful for the transition between one fragment to the other.

Frequently asked Android Interview Questions and Answers

21. What are Layout XML files?

Layout XML files contain the structure for the user interface of the application. The XML file also contains various different layouts and views, and they also specify various GUI components that are there in Activity or fragments.

22. What are Resources in Android Application?

The resources in Android Apps defines images, texts, strings, colors, etc. Everything in resources directory is referenced in the source code of the app so that we can use them.

23. Can you develop Android Apps with languages other than Java? If so, name some.

Yes, there are many languages that we can work with, for the development of Android Applications. To name some, I would say Java, Python, C, C++, Kotlin, C#, Corona/LUA.

24. What are the states of the Activity Lifecycle?

Activity lifecycle has the following four stages-

a. Running State: As soon as the activity starts, it is the first state.

b. Paused State: When some other activity starts without closing the previous one, the running activity turns into the Paused state.

c. Resume State: When the activity opens again after being in pause state, it comes into the Resume State.

d. Stopped State: When the user closes the application or stops using it, the activity goes to the Stopped state.

25. What are some methods of Activity?

The methods of Activity are as follows:

  • onCreate()
  • onStart()
  • onPause()
  • onRestart()
  • onResume()
  • onStop()
  • onDestroy()

26. How can you launch an activity in Android?

We launch an activity using Intents. For this we need to use intent as follows:

  1. ntent intent_name= new Intent(this, Activity_name.class);
  2. startActivity(intent_name);

27. What is the service lifecycle?

There are two states of a service that are-

a. Started State: This is when the service starts its execution. A Services come in start state only through the startService() method.

b. Bounded State: A service is in the bounded state when it calls the method bindService().

28. What are some methods of Services?

The methods of service are as follows-

  • onStartCommand()
  • onBind()
  • onCreate()
  • onUnbind()
  • onDestroy()
  • onRebind()

29. What are the types of Broadcast?

Broadcasts are of two types that are-

a. Ordered Broadcast: Ordered broadcasts are Synchronous and work in a proper order. It decides the order by using the priority assigned to the broadcasts.

b. Normal Broadcast: These are asynchronous and unordered. They are more efficient as they run unorderly and all at once. But, they lack full utilization of the results.

30. What are useful impotent folders in Android?

The impotent folders in an Android application are-

  1. build.xml- It is responsible for the build of Android applications.
  2. bin/ – The bin folder works as a staging area to wrap the files packages into the APK.
  3. src/ – The src is a folder where all the source files of the project are present.
  4. res/ – The res is the resource folder that stores values of the resources that are used in the application. These resources can be colors, styles, strings, dimensions, etc.
  5. assets/ – It provides a facility to include files like text, XML, fonts, music, and video in the Android application.

31. What are the important files for Android Application when working on Android Studio?

This is an important android studio interview question

There are following three files that we need to work on for an application to work-

a. The AndroidManifest.xml file: It has all the information about the application.

b. The MainActivity.java file: It is the app file that actually gets converted to the dalvik executable and runs the application. It is written in java.

c. The Activity_main.xml file: It is the layout file that is available in the res/layout directory. It is another mostly used file while developing the application.

32. Which database do you use for Android Application development?

The database that we use for Android Applications is SQLite. It is because SQLite is lightweight and specially developed for Android Apps. SQLite works the same way as SQL using the same commands.

33. Tell us some features of Android OS.

The best features of Android include-

  1. Multi-tasking
  2. Support for a great range of languages
  3. Support for split-screen
  4. High connectivity with 5G support
  5. Motion Control

34. Why did you learn Android development?

Learning Android Studio is a good idea because of the following-

  1. It has a low application development cost.
  2. It is an open-source platform.
  3. It has multi-platform support as well as Multi-carrier support.
  4. It is open for customizations.
  5. Android is a largely used operating system throughout the world.

35. What are the different ways of storage supported in Android?

The various storage ways supported in Android are as follows:

  1. Shared Preference
  2. Internal Storage
  3. External Storage
  4. SQLite Databases
  5. Network Connection

36. What are layouts?

Layout is nothing but arrangements of elements on the device screen. These elements can be images, tests, videos, anything. They basically define the structure of the Android user interface to make it user friendly.

37. How many layout types are there?

The type of layouts used in Android Apps are as follows:

  1. Linear Layout
  2. Relative Layout
  3. Constraint Layout
  4. Table Layout
  5. Frame Layout
  6. Absolute Layout
  7. Scrollview layout

38. What is an APK?

An APK stands for Android Package that is a file format of Android Applications. Android OS uses this package for the distribution and installation of the Android Application.

39. What is an Android Manifest file?

The manifest file describes all the essential information about the project application for build tools, Android operating system, and google play. This file is a must for every Android project that we develop, and it is present in the root of the project source set.

#android tutorials #android basic interview questions #android basic questions #android developer interview questions #android interview question and answer #android interview questions #android interview questions for experienced #android interview questions for fresher

Ikram Mihan

Ikram Mihan

1594452999

10 Hours of TOP PyTorch Questions

Watch me live figure out answers to common PyTorch Questions! Better time stamps are in the comment section and will let you navigate to the questions you find interesting. This has not been heavily edited and planned. I was just curious to read through the forum because I thought it could be educational and thought I’d share my process of doing it. I don’t know the answers to all of these questions and we try to figure it out as we go! Also I was a bit slow in the beginning but feel I got more into it after some time :)

OUTLINE:

00:00:00 - Introduction
00:00:38 - num_workers in DataLoader
00:09:38 - Manually set gradients to zero
00:24:10 - model.eval() vs with torch.no_grad()
00:34:37 - Load part of a pretrained model
00:47:55 - optimizer.step() and loss.backward()
00:54:33 - Save and load models in PyTorch
01:00:18 - view() vs unsqueeze()
01:07:49 - Tensor to another type
01:18:07 - nn.ModuleList vs nn.Sequential
01:27:07 - .grad of intermediate variable
01:31:09 - GPU memory leaks
01:38:55 - Feedback old PyTorch for Kaggle
01:44:32 - clone() vs detach()
01:51:28 - nn.Functional vs nn module
02:02:51 - Finetuning in PyTorch
02:10:06 - transforms.Normalize()
02:18:15 - Gradient Clipping
02:23:08 - Detach, no_grad and requires_grad
02:28:03 - Euclidean Distance
02:29:44 - Freeze Network layers
02:34:41 - torch.backends.cudnn.benchmark
02:36:41 - Visualizing network weights
02:39:08 - DataLoader for various length data
02:45:42 - RunTimeError: backward second time
02:50:27 - Autograd graph
02:52:20 - Release GPU memory cache
02:56:20 - Converting int to one-hot vector
03:04:28 - Parameters of Model
03:09:35 - Features of image from Model
03:21:00 - Own loss functions PyTorch
03:30:36 - Weights and biases default init
03:36:10 - Normalize embedding vectors
03:40:15 - Adaptive Learning Rate
03:44:57 - .backward()
03:53:50 - pin_memory=True
03:57:18 - Checkpoint to CPU but saved GPU
04:01:10 - Multi Label Classification
04:18:34 - Variable to Numpy
04:19:19 - In-place operation
04:21:20 - Multiple criterion to a single loss
04:26:53 - List of Tensors to a Tensor
04:28:58 - nn.Module
04:31:10 - model.train() vs model.eval()
04:32:00 - Pretrained Embeddings
04:33:37 - Default init methods
04:34:27 - Visualizing graph
04:36:26 - Making PyTorch runs on the GPU
04:40:05 - BCELoss vs BCEWithLogitsLoss
04:42:50 - List of nn.Module in a nn.Module
04:43:50 - DataLoader vs Dataset
04:47:55 - Initialize hidden states for RNNs
04:58:02 - Printing Tensor Type
04:58:44 - Data Augmentation
05:09:08 - DataLoader for variable-size input
05:13:06 - Update PyTorch
05:13:56 - Training Half Precision
05:21:21 - LogSoftmax vs Softmax
05:24:28 - Why 3D tensors in RNNs
05:28:28 - BCEWithLogitsLoss vs MultiLabelSoftMarginLoss
05:29:39 - L2 regularization
05:31:35 - Equivalent of np.reshape()
05:33:03 - torch.nn.utils.clip_grad_norm
05:36:18 - Balanced sampling between classes
05:41:50 - RuntimeError: input is not contiguous
05:44:23 - Backward function for multiple losses
05:57:25 - Contiguous vs non-contigous
06:02:10 - Check gradient flow
06:05:24 - Passing weights CrossEntropyLoss
06:07:33 - Torch.no_grad()
06:08:44 - Custom loss functions
06:10:07 - Difference between LSTM and LSTMCell
06:12:32 - torch.nn.functional vs torch.nn
06:15:29 - .clamp
06:17:26 - Inverse normalization
06:20:51 - Append to Tensor
06:23:41 - Batch Norm
06:35:31 - Inferring shape flatten
06:36:25 - Device model/tensor stored on
06:40:26 - Speeding up the DataLoader
06:53:49 - torch.repeat vs torch.expand
06:57:44 - Cuda Out of Memory
07:05:09 - Way to clone a Model
07:08:15 - Identical transforms
07:16:11 - Random Seed Initialization
07:20:58 - PyTorch Coding Conventions
07:37:16 - Weights specific module in nn.Sequential
07:38:11 - nn.ReLU in init
07:45:38 - Clear GPU memory
07:49:19 - RunTimeError: Expected Long
07:51:12 - L2 reg term in loss
07:51:47 - Tour of PyTorch Internals
07:58:09 - VGG output - no softmax
07:58:48 - Element-wise bmm
08:01:01 - Too Many TimeStamps: See Pinned Comment

#pytorch #python #interview-questions

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Mery tris

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