You want to know something about Bots? You want to know something about A.I.? You want both combined? Better Subscribe, because thats exactly what You'll get. You won't regret!
Hey guys can someone help me i have a project where i need to define an automata for the handwritten digit recognition and i still don't know how to define the states and transitions for my automaton
Excellent video and accompanying code. I just keep staring at the code, its art. And the naming convention with the legend is insightful, the comments tell the story like a first class narrator. Thank you for sharing this.
The second part of the video is very hard to follow for someone who is just starting: too many abstract concepts packed in a very short time span "maximize the error". What does that mean on intuitive level?
We expected the output [1 0 0] but we got the output [0.67 0.53 0.52]. Now we want to improve the net, so that we really get the desired output [1 0 0] in the future. To achieve our goal, we will use the faulty output to modify the weights and biases of your net. 1. We subtract the expected output from the faulty one: [0.67 0.53 0.52] - [1 0 0] = [-0.33 0.53 0.52] This shows us that the first position was 0.33 to small und position 2 and 3 were 0.53 and 0.52 to big. 2. We use these values to modify the output of the previous layer: We simply Matrix multiply the calculated difference [-0.33 0.53 and 0.52] to the output of the hidden layer. This means, our hidden layer will now actually be even more incorrect. My matrix multiplying the [-0.33 0.53 0.52] to the output, we pushed the output even further into delivering us results that have this exact error. We basically told the net "hey your output was wrong by -0.33. Repeat the exact same calculation next time and then add another -0.33 on top of that!" We do not want that. 3. We multiply our matrix by -1 to Inverse each entry. Now we tell our net "Hey your output was wrong by -0.33. Make sure to add +0.33 next time!" Maximizing the error means that the original -0.33 would have increased the difference between our expected and actual output.
11:32 why are you checking for the highest value I dont understand when the highest is 0.67 its classified as 0 can you please explain? Like what this number has to be for example for input to be classified as 1
Well , your brain is basically a complex neural network Plus, our body isn't us; our brain is us. We are just a complex meat neural network controlling a big fleshy, meaty and boney body.
The first minute of this video got myself asking who is this dude and does he make more videos explaining compicated topics in a simple way. pls do more
It's great to see content that helps demystify complex topics like neural networks, especially using a versatile language like Python! Understanding neural networks is so vital in today's tech-driven world, and Python is a fantastic tool for hands-on learning. It's amazing how such concepts, once considered highly specialized, are now accessible to a wider audience. This kind of knowledge-sharing really empowers more people to dive into the fascinating world of AI and machine learning! 🌟🐍💻
for the first node in the hidden layer you added the bias node of 1, for the rest of the nodes in the hidden layer you multiplied the bias node of 1 ??
