Welcome, Guest: Register On Nairaland / LOGIN! / Trending / Recent / NewStats: 3,150,585 members, 7,809,114 topics. Date: Thursday, 25 April 2024 at 11:43 PM |
Nairaland Forum / Frawzey's Profile / Frawzey's Posts
(1) (2) (of 2 pages)
Business / Re: Forex Trade Alerts: Season 22 by Frawzey: 6:00pm On May 02, 2022 |
Pristine664: Thanks a lot. God bless. |
Business / Re: Forex Trade Alerts: Season 22 by Frawzey: 1:26pm On May 02, 2022 |
Feshizzy: Be humble. I don't need to tell a story to ask a question. You could have answered with all these notes you wrote here since you recently funded your account, you could just have read what I said here: Frawzey: |
Business / Re: Forex Trade Alerts: Season 22 by Frawzey: 10:44am On May 02, 2022 |
Pristine664: They do. When I tried paying through SWIFT (domiciliary account), they canceled the transaction. Can you share what has worked for you? What broker do you use? |
Business / Re: Forex Trade Alerts: Season 22 by Frawzey: 10:36am On May 02, 2022 |
Hi Traders, I'm having troubles funding my account. How can I fund my account with the restrictions from the CBN on Forex platforms? Will be glad if someone can truly help. Thanks in anticipation. |
Investment / Re: Nigerian Stock Exchange Market Pick Alerts by Frawzey: 8:26am On Feb 11, 2021 |
Hi, Please can someone tell me when MTNNGs Q4 2020 results would be out? I can't seem to find any info on their website. Thanks. |
Politics / Re: Rejoicing As Gov Seyi Makinde Goes On The Streets, Assures Residents Of Peace by Frawzey: 3:29pm On Oct 21, 2020 |
People should know that its not about north/south or tribal agitation. The same Yorubas who are looting Tinubu & Sanwo-olu's property in Lagos are praising Makinde in Oyo state. This is an agitation for good governance but was taken to the extreme in Lagos. 3 Likes |
Business / Re: Forex Trade Alerts: Season 20 by Frawzey: 3:48am On Jan 15, 2020 |
Frawzey: Hi guys, Any help here? Thanks. |
Business / Re: Forex Trade Alerts: Season 20 by Frawzey: 2:20am On Jan 14, 2020 |
Hi Traders, What broker is good to use in Nigeria right now? No hassle with withdrawals, no slippages and good customer support. Thanks. |
Crime / Re: Ghanaian Footballer, Arthur, Shot In Legs By South Africa Police by Frawzey: 1:35pm On Sep 10, 2019 |
Duggedised12: Yes you are right. Apologies. 1 Like |
Crime / Re: Ghanaian Footballer, Arthur, Shot In Legs By South Africa Police by Frawzey: 1:34pm On Sep 10, 2019 |
IntrovertedK: Yes you are right. Apologies. |
Crime / Re: Ghanaian Footballer, Arthur, Shot In Legs By South Africa Police by Frawzey: 9:10am On Sep 09, 2019 |
wiseone28: Didn't the Nigerian government chase out Ghanaians during Shagari's regime? Isn't that Xenophobia? Don't act like we (Nigeria) are one white clean horse. Learn from history |
Travel / Re: Canadian Express Entry/federal Skilled Workers Program - Connect Here Part 8 by Frawzey: 11:42pm On Jun 30, 2019 |
Mummychichi: No I did not use my spouse's WES & IELTS to claim points. Also requested that its sent to a friend in the UK and have it scanned. Inshort, if one gets ITA few days to your account expiry date, will it extend one's profile or I have to provide all necessary documents before the expiration date? Thanks. |
Travel / Re: Canadian Express Entry/federal Skilled Workers Program - Connect Here Part 8 by Frawzey: 10:29pm On Jun 30, 2019 |
I got ITA last week but my EE account expires Aug 4. I have the following documents pending: a) My spouse's WES and IELTS b) My police report from UK (Once lived in the UK) Does it mean I have to provide all these documents before my profile expires? or the 60day waiting period still holds? Inshort, if you get your ITA few days to your account expiry date, will it extend your profile or you have to do all that is necessary before the expiration date? Thanks for your anticipated response. 1 Like |
Investment / Re: Nigerian Stock Exchange Market Pick Alerts by Frawzey: 12:15pm On Nov 02, 2018 |
BullBearMkt: I'll do just that. Thanks. |
Investment / Re: Nigerian Stock Exchange Market Pick Alerts by Frawzey: 12:15pm On Nov 02, 2018 |
maishai: Thanks Maishai. I'll reach out. |
Investment / Re: Nigerian Stock Exchange Market Pick Alerts by Frawzey: 12:48pm On Nov 01, 2018 |
GonFreecss1: The measure of accuracy of my model would determine if I'd monetize it. If it has a high accuracy, then I would definitely monetize. I only got for Zenith bank from 2012 here: https://www.investing.com/equities/zenithbank-historical-data Still yet to get all the data though Thanks. |
Investment / Re: Nigerian Stock Exchange Market Pick Alerts by Frawzey: 12:43pm On Nov 01, 2018 |
sellydion: Can you please point me to them? Thanks. |
Investment / Re: Nigerian Stock Exchange Market Pick Alerts by Frawzey: 11:37am On Nov 01, 2018 |
Agbalowomeri: Its a project that aims to see the trends in the NSE. There are a lot of these abroad but none in Nigeria. So I hope to create one. Thanks. |
Investment / Re: Nigerian Stock Exchange Market Pick Alerts by Frawzey: 11:32am On Nov 01, 2018 |
Hi All, I am trying to work on the historical data for some machine learning prediction dating from year 2000. Can anyone please help me with where I can download the data for various companies? Thanks. |
Programming / Re: Data Science, AI & Machine Learning Tutorial Series by Frawzey: 10:24am On Oct 03, 2018 |
Hi All, Apologies for the delay. I have been engaged with some things. I'm preparing the next tutorial by trying to simplify it even for a 5 year old and it would be out this week. Meanwhile, if you haven't learned Python at all, and you want to start building the skills for when we need it on this tutorial. Please register and start here: http://www.dataquest.io (highly recommended and free) Also, feel free to ask question or share ideas. Thanks. 1 Like |
Programming / Re: Data Science, AI & Machine Learning Tutorial Series by Frawzey: 8:24am On Sep 28, 2018 |
Darivie04: Thanks for pointing that out. Really appreciate such response. |
Programming / Re: Data Science, AI & Machine Learning Tutorial Series by Frawzey: 9:26am On Sep 26, 2018 |
To read lesson 1, click here Lesson 2. Explaining the terms in neural network Brief introduction A neural network consists of a input layer, hidden layer (middle layer) and the output layer. The input layer takes in the input (images, files, audio, video etc), passes it to the hidden layer where come processing/learning is done and passed to the output layer for results. Take a moment to think about this: let's assume you are in a group of 3 friends and you want to tell your 3rd friend you love her. You are the first friend, your second friend, Jay is the middleman or the channel of communication between you and your 3rd friend, Lola. It means you are the input node(s), Jay is the hidden/middle node and Lola is the output node. Let's say you made a casual whisper to Jay to inform Lola you love her. Jay is reluctant but goes on to say it to Lola. Its easy for Lola to smile and discard it - meaning the output was not strong enough. Let's assume you call Jay to a corner and tell him with all seriousness that you love Lola and that he should tell Lola the same say you told him. Jay did exactly what you told him. Lola would likely take it more seriously put that into consideration. She might even reply telling you she loves you too. The words you said are the input, the whispered joking word can be said to have a small weight - its not really serious. While the seriousness you added to the corner talk had more weight in shaping Lola's response. This is explains the basics of how neural network works. Its takes the product of the input from each layer multiplies it by the weight to give an output. Now, let's assume Jay told Lola that you "like" her instead of "love". That's an error. You had it mind that he would tell Lola with all seriousness that you love her but he didn't. What you had in mind was an intended output (target) while what Jay said was the actual output. Mathematically we can calculate this as: output error = intended output - actual output. We can moderate this output error by including a learning rate. This learning rate is a figure that we'd multiply with the output error to reduce it so that when next we tell Jay to speak with Lola, the error is minimized. The learning rate is usually a small number. Before we wrap up, lets assume that Lola has a level or threshold that must be met before she takes people's word into consideration. I mean, there's a level of 'trust' that must met for her to "believe" the speaker. Mathematically, the threshold that measures the level of 'trust' is called the activation/sigmoid/logistic function. So it means, Jay must meet that level for her to believe him. Trust me, we all have it. So it means even for you & Jay, there's also a level of seriousness or trust that must be overcome before you can pass your message across. Putting this analogy to the neural network, all layers (input, hidden & output layers) have an activation function that must be overcome for a successful message transfer. Finally, you can improve the output by increasing the number of times you relay your message to Jay as he also speaks to Lola. If you notice that Lola's output was way below our intended result, you can call Jay to the corner again to tell him the same message to tell. Hoping that it would minimize Jay's error and improve Lola's output. The number of times we relay our message (input) is called an epoch. We can have 5 epochs so as to improve our output. And the process of relaying the message is called training. Now lets make a recap of the terms used: Input layer: is the entrance of the neural network Hidden layer: where communication (learning) happens Output layer: where the results happen Output error: intended output - actual error Learning rate: moderating factor used to minimize the error Activation function: threshold that must be overcome for an input to move to the next layer. It takes in the input at every layer. Epoch: number of times a training is carried out. Putting it all together, a neural network combines the inputs, learning rate, weights, errors and the activation function to give us the output. We would be using these terms going forward. 3 Likes |
Programming / Re: Data Science, AI & Machine Learning Tutorial Series by Frawzey: 1:18pm On Sep 19, 2018 |
A little delay ---> Next lesson is coming up tomorrow. Thanks. |
Programming / Re: Data Science, AI & Machine Learning Tutorial Series by Frawzey: 1:17pm On Sep 19, 2018 |
Efiko: Yes you are correct! |
Programming / Re: I Need A Dev For Small Work by Frawzey: 2:10pm On Sep 17, 2018 |
tizzdi: You would need to learn python. Not difficult since you already have a programming background. |
Programming / Re: Data Science, AI & Machine Learning Tutorial Series by Frawzey: 2:08pm On Sep 17, 2018 |
Efiko: Good questions: 1) Is the Machine in the term "Machine Learning" referring to these items above or computer system ? It generally refers to a computer. However, you can have computerized machine like robots, dispensers, filters and so on. These are also machines than can be computerized to learn how to do want humans do. So yes, the machine in machine learning cuts across all fields once the 'machines' can be computerized to learn how to improve its performance as it does more of the task - without being explicitly being programmed to improve its outcome. Lets say a self-driving car has been programmed to drive you within Lagos. Now if you wanna get to Ibadan and it gets to drive to you unknown places (places it has not seen or programmed with) because it can learn like a human being to navigate unknown territories - noting and storing the landmarks, potholes, traffic stops and so on. Its a machine that is learning. You don't have to be updating or coding new location everytime it needs to go an unknown place. It has learned by itself to do that. And if it improves next time and gets to Ibadan faster or smoother than the first time, then its learned to improve its performance. This is a classic example of machine learning. Its applications are limitless. 2) Still on terminology, what is Deep Learning (DL) and how do these terms AI, ML, DL and Neural Network (or Artificial Neural Network_ANN) relate to each other To give a perspective, AI is the god, machine learning is its main oracle while deep learning is one of the prophets that talks to the oracle. Artifical Intelligence is human intelligence in machines. Machine learning is when the machine learns - feeding the machine with things I would learn with - primarily data. Deep learning is an approach to machine learning just like machine learning is to AI. Machine learning aims to bring artificial intelligence through learning from the data. Thus ML can be considered as one of the approaches towards Artificial Intelligence. Deep learning was inspired by the structure and function of the brain, namely the interconnecting of many neurons. Artificial Neural Networks (ANNs) as I explained above are networks that mimic the biological structure of the brain so that they cam be used in deep learning and machine learning. Its like this. AI --> ML --> DL --> NN god --> Oracle --> Prophet --> worshipers PS: Remember you need to be a worshiper to access the gods. And that's why we are starting from there. Reference for more understanding: here 3 Likes |
Programming / Re: I Need A Dev For Small Work by Frawzey: 1:42pm On Sep 17, 2018 |
tizzdi: Yes you are making sense and its very possible. Do you know a little bit of programming? |
Programming / Re: I Need A Dev For Small Work by Frawzey: 9:35am On Sep 15, 2018 |
tizzdi: Python is suitable for it. And there are libraries specifically built to make it easy for you like Pytorch. Do you need help with this? Do you have the data to work with? Let me know |
Programming / Re: Data Science, AI & Machine Learning Tutorial Series by Frawzey: 10:34am On Sep 14, 2018 |
billpete89: Depends on what you want to use it for. I recommend learning Python for starters. Its easy and versatile. As you advance, you will learn a few more libraries in python that helps you build your own solutions. Thanks. 1 Like |
Programming / Re: Data Science, AI & Machine Learning Tutorial Series by Frawzey: 4:37am On Sep 13, 2018 |
Lesson 1: AI came from one simple question: Can computers learn to think like humans? Humans can easily identify objects because the brain has “seen” (learned) the images of those objects before – as input, processes it and then identifies the object as a dog, book, stick e.t.c. However, this is extremely difficult for computers. Why? Because computers are primarily programmed for computational results. Computers compute! A computer can multiply 123736 by 352635 and give the results in nanoseconds but finds it hard to identify a puppy in a picture. To solve this problem, what if we try to replicate how the human brain works in a computer? Just what if… Enters Neural Network Now, think about a simple network that takes many input, processes them and gives an output then you have a neural network. How it works: Neural networks take in a large number of input, known as training examples, then uses the examples to create rules for recognizing patterns. This means, many pixels of a dog’s image can be ‘fed’ into the computer today to train it, so that when next you ask it to recognize dogs in various pictures, it would give the correct answer. By increasing the number of training examples of different dog pics you show to the computer, it can learn more and so improve its results – it’d even identify a dog in superman’s costume. Why neural networks in computers? As explained earlier, simply to replicate how the brain works in computers and enable computers to recognize patterns/objects. It resembles the brain in two aspects: a) input is acquired by the network through a learning process. (The learning process is the process whereby the image is shown to the computer so that it learns to identify objects in the image in the future) b) interconnection strengths between the network are used in results (will explain this later) I’d continue with the developments on neural networks. Let me know if you have questions. 3 Likes 1 Share |
Programming / Data Science, AI & Machine Learning Tutorial Series by Frawzey: 8:22am On Sep 12, 2018 |
Hi, Brief introduction: I have almost 5 years experience working in eCommerce and I am transitioning to the field of AI. I have seen AI & machine learning being used in my field and I believe there is still more to come in the future. Given that AI is a very important technology and a key components of the 4th industrial revolution, I think its important to share my knowledge to spur someone to take an interest in this field. This tutorial will start from the scratch and will also contain links to important readings. Please do read these links. I will make sure my explanation is as easy as possible so that a 5 year old can assimilate the concepts. Requirements: Come with your drive & passion, dedication, a laptop and a notebook. As we advance on this topic, we would do some coding and maths - don't worry its easy if you are committed to it. The first few weeks would be an introduction to understand the terms and concepts. Trust me, machine learning as a lot of terms that are sweet to know. Finally, I will be posting twice a week or more when I have the time. Feel free to ask questions on the thread and I'll answer you. Thanks. 3 Likes |
Politics / Re: NL Monthly Political Debate by Frawzey: 12:58pm On Feb 13, 2018 |
These were threads we used to have when Nairaland was a forum and not a blog that it is now posting cleavages and obscenities on the front page. |
(1) (2) (of 2 pages)
(Go Up)
Sections: politics (1) business autos (1) jobs (1) career education (1) romance computers phones travel sports fashion health religion celebs tv-movies music-radio literature webmasters programming techmarket Links: (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Nairaland - Copyright © 2005 - 2024 Oluwaseun Osewa. All rights reserved. See How To Advertise. 74 |