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How to download torrent files using python?

 Downloading torrent using utorrent or bittorrent is quite easy. But for any reason if you want to download torrent using codding then this article is for you. In this post I am using google colab to download torrent file directly to my google drive so downloading speed will be faster. So first I will connect my colab notebook to google drive. After this I will install "libtorrent" library which will help us in downloading torrent. Now, If you want to download using torrent file then you can use below code. It will take a torrent file as input. You can add multiple files. And If you want to download using magnet URL then use below code. This will take magnet URL as input. You can enter multiple magnet URLs. After adding URLs use "Exit" to exit the cell. Now, finally its time to download the file. So just use the below code and It will start downloading the torrent file. Don't forget to change the destination path to

Python | How to download file from google drive url?

If you want to download a file from URL using python then you can do it easily using "request" library but if you are trying to download file from google drive URL then thing get a bit complicated. In this post I am going to share the code which will help you to download files from google drive URL. To download file from google drive URL you need the key. What key?, the key at the last of the link. For example take the below URL. The text in the orange colour in the URL is the key. You need this key instead of the complete URL. After copying the key you can put it into the below code to download the file. For testing purpose you can use the key provided in the code.   That's it. This is how you can download file from google drive link using python. There are other methods also but for those you need the authorization while with the above method you only need a public google drive link.

Story behind Mount Rushmore

Between 1927 and 1941, 400 workers blasted 450,000 tons of rock from a mountainside using chisels, jackhammers, and a lot of dynamite. Gradually, they carved out Mount Rushmore. Now, the monument draws nearly 3 million people to South Dakota’s Black Hills every year. But its fa├žade belies a dark history. Photo by Ronda Darby on Unsplash About 10,000 years ago, Native American people began inhabiting the Black Hills. The area became especially sacred to the Lakota people, who formed the western branch of what the US called the Sioux Nation. The Lakota believed one cave within the Black Hills to be where they first emerged. And they named one of the Black Hills mountain peaks the Six Grandfathers after their sacred directional spirits. But in the 1800s, Lakota access to this land came under threat. White settlers in North America expanded their territories by using physical violence or negotiating with Indigenous peoples. After its establishment in the late 1700s, the US government rati

Why Mona Lisa is so famous?

As dawn broke over Paris on August 21st, 1911, Vincenzo Peruggia hoisted a painting off the wall and slipped down the back stairs of the Louvre. He was close to freedom, the exit just before him when he encountered a two-pronged problem: the door was locked and footsteps were approaching. Tucked under Peruggias arm was Leonardo da Vincis Mona Lisa. It's arguably the world's most famous painting today. But how did it achieve its status? Photo by Alina Grubnyak on Unsplash Leonardo is thought to have started the portrait in 1503 at the request of a Florentine businessman who wanted a portrait of his wife, Lisa Gherardini. Leonardo continued working on the painting for more than 10 years, but it was unfinished by the time he died. Over his lifetime, Leonardo conducted groundbreaking studies on human optics, which led him to pioneer certain artistic techniques. Some can be seen in the Mona Lisa. Using atmospheric perspective, he made images at greater distances hazier, producing t

Can we build an elevator to the space?

Sending rockets into space requires sacrificing expensive equipment, burning massive amounts of fuel, and risking potential catastrophe. So in the space race of the 21st century, some engineers are abandoning rockets for something much more exciting "elevators". Photo by NASA on Unsplash Okay, so maybe riding an elevator to the stars isn't the most thrilling mode of transportation. But using a fixed structure to send smaller payloads of astronauts and equipment into orbit would be safer, easier, and cheaper than conventional rockets. On a spacex falcon 9 rocket, every kilogram of cargo costs roughly $7,500 to carry into orbit. Space elevators are projected to reduce that cost by 95%. Researchers have been investigating this idea since 1895, when a visit to what was then the world's tallest structure inspired russian scientist konstantin tsiolkovsky. Tsiolkovsky imagined a structure thousands of kilometers tall, but even a century later, no known material is strong en

You can only save one - Who would you choose?

You are the captain of the Mallory 7, an interstellar cargo transport. On your way to the New Lindley spaceport, you receive a distress call. There’s been an explosion on the Telic 12 and its passengers are running out of oxygen. As you set a course to intercept,  you check the Telic 12′s manifest. It’s currently transporting  30 middle-aged individuals from some of Earth’s poorest districts to the labor center on New Lindley, where they'll be assigned jobs on the spaceport. But as you approach the Telic 12, you receive a second distress call. A luxury space cruiser called the Pareto has lost a thruster, sending them careening towards an asteroid belt. Without your help, the 20 college students headed for vacation aboard the Pareto are all doomed. So with only enough time to save one ship, which one should you choose? Photo by Keegan Houser on Unsplash This dilemma is an example of a broader class of problems where a life-saving resource, such as a donated organ or vaccine is scar

Importance of pooling layer in CNN

I recently came across a bunch of question regarding pooling in CNN. Will removing the max pooling layer from the CNN architecture effect the accuracy? Is pooling necessary for convolutional neural network? What will happen if we remove pooling layers from the General CNN architecture? Does removing pooling layers from CNN will improve results?   There are many other similar questions like these and this article will answers all those questions. Photo by Thomas Tucker on Unsplash So, firstly if you don't know what pooling is then you can go through this article , it will help you in understanding pooling deeply. And if you just want to know the answers to the above questions then continue with this article. So, we know that pooling helps in reducing the dimension. But why we want to reduce the dimension? The answer is to reduce the computational power required to train the model. If we don't reduce the dimension then our model will take very long or most probably our machine w