DEV Community: Ravi Prasad

Idea Generation: the Nikola Tesla way.

Ravi Prasad — Wed, 02 Feb 2022 15:08:30 +0000

When I get an idea I start at once building it up in my imagination. I change the construction, make improvements and operate the device in my mind. It is absolutely immaterial to me whether I run my turbine in thought or test it in my shop. I even note if it is out of balance. There is no difference whatever, the results are the same. In this way I am able to rapidly develop and perfect a conception without touching anything. When I have gone so far as to embody in the invention every possible improvement I can think of and see no fault anywhere, I put into concrete form this final product of my brain. Invariably my device works as I conceived that it should, and the experiment comes out exactly as I planned it. In twenty years there has not been a single exception.

In an interview with M. K. Wisehart, published in the American Magazine of April 1921, and in Mr. O’Neill’s book, Tesla describes his faculty as follows:

During my boyhood I had suffered from a peculiar affliction due to the appearance of images, which were often accompanied by strong flashes of light. When a word was spoken, the image of the object designated would present itself so vividly to my vision that I could not tell whether what I saw was real or not. . . . Even though I reached out and passed my hand through it, the image would remain fixed in space

In trying to free myself from these tormenting appearances, I tried to concentrate my thoughts on some peaceful, quieting scene I had witnessed. This would give me momentary relief; but when I had done it two or three times the remedy would begin to lose its force. Then I began to take mental excursions beyond the small world of my actual knowledge. Day and night, in imagination, I went on journeys — saw new places, cities, countries, and all the time I tried hard to make these imaginary things very sharp and clear in my mind. I imagined myself living in countries I had never seen, and I made imaginary friends, who were very dear to me and really seemed alive.

This I did constantly until I was seventeen, when my thoughts turned seriously to invention. Then to my delight, I found I could visualize with the greatest facility. I needed no models, drawings, or experiments. I could picture them all in my mind . . .

By that faculty of visualizing, which I learned in my boyish efforts to rid myself of annoying images, I have evolved what is, I believe, a new method of materializing inventive ideas and conceptions. It is a method which may be of great usefulness to any imaginative man, whether he is an inventor, businessman or artist.

Some people, the moment they have a device to construct or any piece of work to perform, rush at it without adequate preparation, and immediately become engrossed in details, instead of the central idea. They may get results, but they sacrifice quality.

Here in brief, is my own method

After experiencing a desire to invent a particular thing, I may go on for months or years with the idea in the back of my head. Whenever I feel like it, I roam around in my imagination and think about the problem without any deliberate concentration. This is a period of incubation.

Then follows a period of direct effort. I choose carefully the possible solutions of the problem I am considering, and gradually center my mind on a narrowed field of investigation. Now, when I am deliberately thinking of the problem in its specific features, I may begin to feel that I am going to get the solution. And the wonderful thing is, that if I do feel this way, then I know I have really solved the problem and shall get what I am after.

The feeling is as convincing to me as though I already had solved it. I have come to the conclusion that at this stage the actual solution is in my mind subconsciously though it may be a long time before I am aware of it consciously.

Before I put a sketch on paper, the whole idea is worked out mentally. In my mind I change the construction, make improvements, and even operate the device. Without ever having drawn a sketch I can give the measurements of all parts to workmen, and when completed all these parts will fit, just as certainly as though I had made the actual drawings. It is immaterial to me whether I run my machine in my mind or test it in my shop.

The inventions I have conceived in this way have always worked. In thirty years there has not been a single exception. My first
electric motor, the vacuum tube wireless light, my turbine engine and many other devices have all been developed in exactly this way.

Purely Copied from

https://teslauniverse.com/nikola-tesla/articles/miracle-mind-nikola-tesla

Convolutional Neural Network : The Easy Way

Ravi Prasad — Thu, 27 Jan 2022 17:16:55 +0000

Before diving into CNN, lets get the background clear.

✍Background

Deep learning is a type of machine learning and artificial intelligence (AI) that follow the way humans get certain types of grasp. Deep learning is an chief part of data science, which contains statistics and predictive modeling. It is very helpful to data scientists who are tasked with collecting, analyzing and interpreting large amounts of data; deep learning makes this procedure easier and faster.
When the volume of data expands, Machine learning techniques, no matter how enhanced, starts to become ineffective in terms of execution and accuracy, whereas Deep learning performs so much better in such cases.

The Neural Network is the heart of deep learning models, and it was at first designed to copy the working of the neurons in the human brain. They are comprised of node layers, containing an input layer, one or more hidden layers, and an output layer. Each node links to another and has an associated threshold and weight. If the output of any individual node is above the described threshold value, that node is activated, sending data to the next layer of the network. Otherwise, no data is passed along to the next layer of the network.

Now, Lets understand Convolutional Neural Network.

Convolutional Neural Network

One of the main parts of Neural Networks is Convolutional neural networks (CNN). CNNs use image recognition and classification in order to detect objects, recognize faces, etc. They are made up of neurons with learnable weights and biases. Each specific neuron receives numerous inputs and then takes a weighted sum over them, where it passes it through an activation function and responds back with an output.
CNNs are primarily used to classify images, cluster them by similarities, and then perform object recognition. Many algorithms using CNNs can identify faces, street signs, animals, etc.

Architecture of CNN

Working of CNN

Before we go to the working of CNN’s let’s cover the basics such as what is an image and how is it represented. An RGB image is nothing but a matrix of pixel values having three planes whereas a grayscale image is the same but it has a single plane.

1. Input Layer:

Read the required input data, scale it to pixel dimension between 0–255 and specify the range of bandwidth either grayscale or RGB

2. Convolution Layer:

The first layer in a CNN network is the CONVOLUTIONAL LAYER, which is the core building block and does most of the computational heavy lifting. Data or imaged is convolved using filters or kernels. Filters are small units that we apply across the data through a sliding window. The depth of the image is the same as the input, for a color image that RGB value of depth is 4, a filter of depth 4 would also be applied to it. This process involves taking the element-wise product of filters in the image and then summing those specific values for every sliding action. The output of a convolution that has a 3d filter with color would be a 2d matrix. For example, imagine as if a flashlight shines its light and covers a 5 x 5 area. And now, imagine this flashlight sliding across all the areas of the input image. This flashlight is called a filter(or sometimes referred to as a neuron or a kernel) and the region that it is shining over is called the receptive field. This filter is also an array of numbers (the numbers are called weights or parameters).

3. Pooling Layer:

Pooling is done to reduce the dimensionality of the input image, which involves downsampling of features. It is applied through every layer in the 3d volume. Typically there are hyperparameters within this layer:
The dimension of spatial extent: which is the value of n which we can take N cross and feature representation and map to a single value
Stride: which is how many features the sliding window skips along the width and height
There are two main types of pooling:

Max pooling:

As the filter moves across the input, it selects the pixel with the maximum value to send to the output array. As an aside, this approach tends to be used more often compared to average pooling.

Average pooling:

As the filter moves across the input, it calculates the average value within the receptive field to send to the output array.
A common POOLING LAYER uses a 2 cross 2 max filter with a stride of 2, this is a non-overlapping filter. A max filter would return the max value in the features within the region. Example of max pooling would be when there is 26 across 26 across 32 volume, now by using a max pool layer that has 2 cross 2 filters and astride of 2, the volume would then be reduced to 13 crosses, 13 crosses 32 feature map.

4. Fully Connected Layer:

Fully Connected layers in a neural networks are those layers where all the inputs from one layer are connected to every activation unit of the next layer. This involves transforming the entire pooled feature map matrix into a single column which is then fed to the neural network for processing. With the fully connected layers, we combined these features together to create a model. Finally, we have an activation function such as softmax or sigmoid to classify the output.

5. Dense Layer or Output Layer:

It takes the input and return the output using appropriate activation function.

Summary

Provide input image into convolution layer
Choose parameters, apply filters with strides, padding if requires. Perform convolution on the image and apply ReLU activation to the matrix.
Perform pooling to reduce dimensionality size
Add as many convolutional layers until satisfied
Flatten the output and feed into a fully connected layer (FC Layer)
Output the class using an activation function (Logistic Regression with cost functions) and classifies images.

References:

Cover : pixabay.com
Neural Network : giphy.com