How can I use free zoom to create screencast video for my youtube channel tutorial video

 To create a screencast video using Zoom, you can follow these steps:

1. **Download and Install Zoom**: If you haven't already, download and install the Zoom client software from the Zoom website (https://zoom.us/download).

2. **Sign in or Create an Account**: Launch the Zoom application and sign in using your Zoom account credentials. If you don't have an account, you can create one for free.

3. **Start a New Meeting**: Click on the "New Meeting" button to start a new meeting session. You don't need to invite anyone else to the meeting since you'll be recording a screencast video.

4. **Adjust Settings**: Before you start recording, you can adjust some settings for optimal screencasting:

   - Check your audio and video settings by clicking on the up arrow next to the microphone and camera icons at the bottom left corner of the Zoom window. Ensure that your desired microphone and camera are selected.

   - If you plan to include audio narration, make sure your microphone is working correctly.

   - Disable your webcam if you don't want your face to appear in the screencast video.

5. **Share Your Screen**: Click on the "Share Screen" button located at the bottom center of the Zoom window. A pop-up window will appear.

6. **Select Screen and Options**: In the screen-sharing pop-up window, choose the screen you want to capture. If you have multiple monitors, select the one you wish to share. You can also enable options like "Share computer sound" if you want to include audio from your computer in the recording.

7. **Start Recording**: Once you've chosen the screen and options, click on the "Share" button. Zoom will begin sharing your screen, and a toolbar will appear at the top of the screen.

8. **Start Screencasting**: To start recording, click on the "Record" button on the Zoom toolbar and select "Record on this Computer." The recording will begin, capturing your screen activities.

9. **Perform the Screencast**: Carry out the actions you want to record in your screencast video. Whether it's demonstrating software, presenting slides, or any other activity, Zoom will record everything on the screen.

10. **Stop Recording**: When you've finished recording, click on the "Stop Recording" button on the Zoom toolbar. Alternatively, you can use the hotkey Ctrl + Shift + R (Command + Shift + R on Mac) to start and stop recording.

11. **End Meeting**: Once you've stopped recording, you can end the meeting session by clicking on the "End Meeting" button at the bottom right corner of the Zoom window.

12. **Access the Recorded Video**: After the meeting ends, Zoom will convert and save the recording locally on your computer. By default, it is stored in the "Documents" folder in a subfolder named "Zoom." You can also access the recordings by clicking on the "Meetings" tab in the Zoom application, selecting the "Recorded" tab, and locating your recording.

That's it! You've successfully created a screencast video using the free version of Zoom. You can now edit or share the recording as needed.

What problem leads to Transformers in Neural network problems ?

Okay so when we have RNNs and CNNs , how they come up with the transformers ? what problem lead them to this solution ?

These are the basic quesiton come up in my mind whenver I think about some solution which create some kind of revolution changes in any field.

The development of transformers was driven by the need to overcome certain limitations of RNNs and CNNs when processing sequential data. The key problem that led to the creation of transformers was the difficulty in capturing long-range dependencies efficiently.

While RNNs are designed to model sequential data by maintaining memory of past information, they suffer from issues such as vanishing or exploding gradients, which make it challenging to capture dependencies that span long sequences. As a result, RNNs struggle to effectively model long-range dependencies in practical applications.

On the other hand, CNNs excel at capturing local patterns and hierarchical relationships in grid-like data, such as images. However, they are not explicitly designed to handle sequential data and do not naturally capture long-range dependencies.

Transformers were introduced as an alternative architecture that could capture long-range dependencies more effectively. The transformer model incorporates a self-attention mechanism, which allows the model to attend to different positions in the input sequence to establish relationships between words or tokens. This attention mechanism enables the transformer to consider the context of each word in relation to all other words in the sequence, irrespective of their relative positions.

By incorporating self-attention, transformers eliminate the need for recurrent connections used in RNNs, allowing for parallel processing and more efficient computation. This parallelism enables transformers to handle longer sequences more effectively and capture complex dependencies across the entire sequence.

The transformer architecture, first introduced in the context of machine translation with the "Transformer" model by Vaswani et al. in 2017, quickly gained popularity due to its ability to model sequential data efficiently and achieve state-of-the-art performance in various natural language processing tasks. Since then, transformers have been widely adopted in many domains, including language understanding, text generation, question answering, and even applications beyond natural language processing, such as image processing and time-series analysis.

DALL·E uses RNN or Transformers ?

  "DALL·E" is a model developed by OpenAI that generates images from textual descriptions. DALL·E combines both transformer and convolutional neural network (CNN) components.

The transformer architecture is used to process the textual input, allowing the model to understand and generate image descriptions. The transformer component is responsible for capturing the semantic relationships between words and learning the contextual information from the input text.

In addition to the transformer, DALL·E employs a decoder network that utilizes a variant of the autoregressive model, which includes recurrent neural network (RNN) components. The RNN helps generate the images pixel by pixel, incorporating both local and global context to create coherent and visually appealing images.

Therefore, DALL·E utilizes a combination of transformers and RNNs in its architecture to generate images based on textual descriptions. It leverages the strengths of both approaches to achieve its remarkable image generation capabilities.