Question

1) Draw and explain the ANN with one hidden layer
2) What is Data Visualisation? Explain with the picture. (DECISION TREE)
3)What is meant by DATA ACQUISITION and Problem scoping
4) Write any two significance of big data
5)Explain Problem identification with an example
6) What is meant by neural networks
serious warning no answer straight up report and i want only answer no other jokes guys please i need help

Answer 1

Answer:

1) In artificial neural networks, hidden layers are required if and only if the data must be separated non-linearly. Looking at next figure, it seems that the classes must be non-linearly separated. A single line will not work. As a result, we must use hidden layers in order to get the best decision boundary.

2) Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

3)Whenever we are starting any work, certain problems always associated with the work or process. Actually we are surrounded by problems! These problems can be small or big, sometimes we ignore them, sometimes we need an urgent solution otherwise your work will suffer.

4) Big Data helps the organizations to create new growth opportunities and entirely new categories of companies that can combine and analyze industry data. These companies have ample information about the products and services, buyers and suppliers, consumer preferences that can be captured and analyzed.

5) Problem Identification consists of: Clearly identifying the root cause of a problem. Developing a detailed problem statement that includes the problem's effect on a population's health.

6)The next big revolution in coding practice might be closer than we think, and it involves helping computers to code themselves. By utilizing natural language processing and neural networks, some researchers think that within a few years we can remove humans entirely from the coding process. If you work as a coder, you'll be glad to hear that they are wrong.

Explanation:

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Answer 2

Answer:

ʜᴇʀᴇ ɪs ʏᴏᴜʀ ᴀɴsᴡᴇʀ ⬇️

Explanation:

1. ɪɴ ɴᴇᴜʀᴀʟ ɴᴇᴛᴡᴏʀᴋs, ᴀ ʜɪᴅᴅᴇɴ ʟᴀʏᴇʀ ɪs ʟᴏᴄᴀᴛᴇᴅ ʙᴇᴛᴡᴇᴇɴ ᴛʜᴇ ɪɴᴘᴜᴛ ᴀɴᴅ ᴏᴜᴛᴘᴜᴛ ᴏғ ᴛʜᴇ ᴀʟɢᴏʀɪᴛʜᴍ, ɪɴ ᴡʜɪᴄʜ ᴛʜᴇ ғᴜɴᴄᴛɪᴏɴ ᴀᴘᴘʟɪᴇs ᴡᴇɪɢʜᴛs ᴛᴏ ᴛʜᴇ ɪɴᴘᴜᴛs ᴀɴᴅ ᴅɪʀᴇᴄᴛs ᴛʜᴇᴍ ᴛʜʀᴏᴜɢʜ ᴀɴ ᴀᴄᴛɪᴠᴀᴛɪᴏɴ ғᴜɴᴄᴛɪᴏɴ ᴀs ᴛʜᴇ ᴏᴜᴛᴘᴜᴛ. ɪɴ sʜᴏʀᴛ, ᴛʜᴇ ʜɪᴅᴅᴇɴ ʟᴀʏᴇʀs ᴘᴇʀғᴏʀᴍ ɴᴏɴʟɪɴᴇᴀʀ ᴛʀᴀɴsғᴏʀᴍᴀᴛɪᴏɴs ᴏғ ᴛʜᴇ ɪɴᴘᴜᴛs ᴇɴᴛᴇʀᴇᴅ ɪɴᴛᴏ ᴛʜᴇ ɴᴇᴛᴡᴏʀᴋ.
2. ᴅᴀᴛᴀ ᴠɪsᴜᴀʟɪᴢᴀᴛɪᴏɴ ɪs ᴛʜᴇ ɢʀᴀᴘʜɪᴄᴀʟ ʀᴇᴘʀᴇsᴇɴᴛᴀᴛɪᴏɴ ᴏғ ɪɴғᴏʀᴍᴀᴛɪᴏɴ ᴀɴᴅ ᴅᴀᴛᴀ. ʙʏ ᴜsɪɴɢ ᴠɪsᴜᴀʟ ᴇʟᴇᴍᴇɴᴛs ʟɪᴋᴇ ᴄʜᴀʀᴛs, ɢʀᴀᴘʜs, ᴀɴᴅ ᴍᴀᴘs, ᴅᴀᴛᴀ ᴠɪsᴜᴀʟɪᴢᴀᴛɪᴏɴ ᴛᴏᴏʟs ᴘʀᴏᴠɪᴅᴇ ᴀɴ ᴀᴄᴄᴇssɪʙʟᴇ ᴡᴀʏ ᴛᴏ sᴇᴇ ᴀɴᴅ ᴜɴᴅᴇʀsᴛᴀɴᴅ ᴛʀᴇɴᴅs, ᴏᴜᴛʟɪᴇʀs, ᴀɴᴅ ᴘᴀᴛᴛᴇʀɴs ɪɴ ᴅᴀᴛᴀ.
3. ᴀ ᴅᴀᴛᴀ ᴀᴄǫᴜɪsɪᴛɪᴏɴ sʏsᴛᴇᴍ (ᴏʀ ᴅᴀs ᴏʀ ᴅᴀǫ) ᴄᴏɴᴠᴇʀᴛs ᴘʜʏsɪᴄᴀʟ ᴄᴏɴᴅɪᴛɪᴏɴs ɪɴᴛᴏ ᴅɪɢɪᴛᴀʟ ғᴏʀᴍ, ғᴏʀ ғᴜʀᴛʜᴇʀ sᴛᴏʀᴀɢᴇ ᴀɴᴅ ᴀɴᴀʟʏsɪs. ᴛʏᴘɪᴄᴀʟʟʏ, sɪɢɴᴀʟs ғʀᴏᴍ sᴇɴsᴏʀs (sᴏᴍᴇᴛɪᴍᴇs ᴘʀᴏᴄᴇssᴇᴅ ʙʏ sᴇɴsᴏʀ ᴄᴏɴᴅɪᴛɪᴏɴᴇʀs) ᴀʀᴇ sᴀᴍᴘʟᴇᴅ, ᴄᴏɴᴠᴇʀᴛᴇᴅ ᴛᴏ ᴅɪɢɪᴛᴀʟ, ᴀɴᴅ sᴛᴏʀᴇᴅ ʙʏ ᴀ ᴄᴏᴍᴘᴜᴛᴇʀ, ᴏʀ ʙʏ ᴀ sᴛᴀɴᴅᴀʟᴏɴᴇ ᴅᴇᴠɪᴄᴇ.
4. ʙɪɢ ᴅᴀᴛᴀ ʜᴇʟᴘs ᴛʜᴇ ᴏʀɢᴀɴɪᴢᴀᴛɪᴏɴs ᴛᴏ ᴄʀᴇᴀᴛᴇ ɴᴇᴡ ɢʀᴏᴡᴛʜ ᴏᴘᴘᴏʀᴛᴜɴɪᴛɪᴇs ᴀɴᴅ ᴇɴᴛɪʀᴇʟʏ ɴᴇᴡ ᴄᴀᴛᴇɢᴏʀɪᴇs ᴏғ ᴄᴏᴍᴘᴀɴɪᴇs ᴛʜᴀᴛ ᴄᴀɴ ᴄᴏᴍʙɪɴᴇ ᴀɴᴅ ᴀɴᴀʟʏᴢᴇ ɪɴᴅᴜsᴛʀʏ ᴅᴀᴛᴀ. ᴛʜᴇsᴇ ᴄᴏᴍᴘᴀɴɪᴇs ʜᴀᴠᴇ ᴀᴍᴘʟᴇ ɪɴғᴏʀᴍᴀᴛɪᴏɴ ᴀʙᴏᴜᴛ ᴛʜᴇ ᴘʀᴏᴅᴜᴄᴛs ᴀɴᴅ sᴇʀᴠɪᴄᴇs, ʙᴜʏᴇʀs ᴀɴᴅ sᴜᴘᴘʟɪᴇʀs, ᴄᴏɴsᴜᴍᴇʀ ᴘʀᴇғᴇʀᴇɴᴄᴇs ᴛʜᴀᴛ ᴄᴀɴ ʙᴇ ᴄᴀᴘᴛᴜʀᴇᴅ ᴀɴᴅ ᴀɴᴀʟʏᴢᴇᴅ.
5. ᴘʀᴏʙʟᴇᴍ ɪᴅᴇɴᴛɪғɪᴄᴀᴛɪᴏɴ ᴄᴏɴsɪsᴛs ᴏғ: ᴄʟᴇᴀʀʟʏ ɪᴅᴇɴᴛɪғʏɪɴɢ ᴛʜᴇ ʀᴏᴏᴛ ᴄᴀᴜsᴇ ᴏғ ᴀ ᴘʀᴏʙʟᴇᴍ. ᴅᴇᴠᴇʟᴏᴘɪɴɢ ᴀ ᴅᴇᴛᴀɪʟᴇᴅ ᴘʀᴏʙʟᴇᴍ sᴛᴀᴛᴇᴍᴇɴᴛ ᴛʜᴀᴛ ɪɴᴄʟᴜᴅᴇs ᴛʜᴇ ᴘʀᴏʙʟᴇᴍ's ᴇғғᴇᴄᴛ ᴏɴ ᴀ ᴘᴏᴘᴜʟᴀᴛɪᴏɴ's ʜᴇᴀʟᴛʜ.
6. ᴀ ɴᴇᴜʀᴀʟ ɴᴇᴛᴡᴏʀᴋ ɪs ᴀ sᴇʀɪᴇs ᴏғ ᴀʟɢᴏʀɪᴛʜᴍs ᴛʜᴀᴛ ᴇɴᴅᴇᴀᴠᴏʀs ᴛᴏ ʀᴇᴄᴏɢɴɪᴢᴇ ᴜɴᴅᴇʀʟʏɪɴɢ ʀᴇʟᴀᴛɪᴏɴsʜɪᴘs ɪɴ ᴀ sᴇᴛ ᴏғ ᴅᴀᴛᴀ ᴛʜʀᴏᴜɢʜ ᴀ ᴘʀᴏᴄᴇss ᴛʜᴀᴛ ᴍɪᴍɪᴄs ᴛʜᴇ ᴡᴀʏ ᴛʜᴇ ʜᴜᴍᴀɴ ʙʀᴀɪɴ ᴏᴘᴇʀᴀᴛᴇs. ɪɴ ᴛʜɪs sᴇɴsᴇ, ɴᴇᴜʀᴀʟ ɴᴇᴛᴡᴏʀᴋs ʀᴇғᴇʀ ᴛᴏ sʏsᴛᴇᴍs ᴏғ ɴᴇᴜʀᴏɴs, ᴇɪᴛʜᴇʀ ᴏʀɢᴀɴɪᴄ ᴏʀ ᴀʀᴛɪғɪᴄɪᴀʟ ɪɴ ɴᴀᴛᴜʀᴇ.