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How to download all your college and other textbook pdfs for free?

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Many times we just need pdf instead of buying a paperback edition. Google can sometimes be helpful but in most the cases, search for a textbothe ok leads to some spam or fake sites. Here is an easy way to download the latest edition of the textbook that you've been searching for.

1. Goto libgen.is or http://gen.lib.rus.ec/

2. Type in your textbook name, make sure the spelling is correct.  To confirm the spelling search the book in Amazon or Google and then copy the book’s title.

3. Click on sethe arch. If you find the book, click on yethe ar to make sure you see all the editions arranged year-wise.

4. Click on the title of the book now.
5. You'll see all the details of the book.

6. Click again on the title of the book to go to download page.
7. Click on the GET to download the book.
And the book is downloaded.
Feel free to comment if you’ve any questions.


Using torrent on iPad or iPhone without any extra apps or tools for free

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Here is a straightforward and simple guide on how to use a torrent on an iPad or iPhone. Apple Inc doesn't allow torrent apps to be hosted on AppStore so it is relatively tiresome for iOS users to download any torrent on PC first and then to copy to the device. Here is a very simple and legal way to download any torrent file to your iPhone or iPad for free without using any kind of tools or apps.



Step 1. Open the Safari browser on your device.
Step 2. Type seedr.cc in the address bar.

Step 3. Sign up using your email or Facebook. I would recommend using Facebook as it is simpler to log in without the hassle of remembering passwords.
Step 4. Now in another tab open any torrent site. For eg. https://yts.lt/ [for best quality movies] or https://www.1337x.to/ [for everything else]

Step 5. Download the torrent file


Step 6. Click on "+" sign on seedr.cc webpage. 

Step 7. Click on upload button. Upload the torrent file.



Step 8. The file would now get hosted on seedr.cc server.…

Deep learning matches the performance of dermatologists at skin cancer classification

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A research paper published by Stanford in Nature shows the Artificial Intelligence model trained to classify images of skin lesions as benign lesions or malignant skin cancers achieves the accuracy of board-certified dermatologists.



Benign and malignant skin lesions can be separated to a certain extent by looking at the margin, texture and color of lesions in skin by a dermatologist. Skin cancers such as melanoma is one of the most common neoplasm of skin, but in the meantime it also has a higher 5 year survival rate if detected earlier.

They pretrained a deep neural network at general object recognition, then fine-tune it on a dataset of ~130,000 skin lesion images comprised of over 2000 diseases to obtain the result.

This is  a proof that Artificial Intelligence is going to redefine medicine.

Check the lesion on your skin

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This is a test version. Please consult your dermatologist before checking this out. Model prepared by Han ([email protected])

AI outperforms cardiologists in diagnosing arrhythmias from ECG

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Artificial Intelligence researchers from Stanford AI lab developed a deep neural network which can diagnose irregular heart rhythms, also known as arrhythmias, from single-lead ECG signals at a high diagnostic performance similar to that of cardiologists.


An ECG (electrocardiogram) provides the information about heart rate and it's rhythm, and shows if there is enlargement of the heart due to high blood pressure (hypertension) or evidence of a previous heart attack (myocardial infarction).

The term "arrhythmia" refers to any change from the normal sequence of electrical impulses. The electrical impulses may happen too fast, too slowly, or erratically – causing the heart to beat too fast, too slowly, or erratically  . It is one of the most common cause of sudden death.

In a study published in Nature Medicine, they developed a deep neural network to classify 10 arrhythmias as well as sinus rhythm and noise from a single-lead ECG signal, and …

AI predicts who would benefit from high blood pressure treatment

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High blood pressure(hypertension) is a common disease in which blood flows through blood vessels, or arteries, at higher than normal pressures. i.e. systolic blood pressure is consistently higher than 140 mmHg and diastolic blood pressure is consistently higher than 90 mmHg.  Hereditary, obesity, sedentary lifestyle, increased salt intake, etc are some of the known risk factors of high blood pressure.

Having high blood pressure puts you at risk for heart disease and stroke, which are leading causes of death worldwide. High blood pressure usually does not have any symptoms untill the complications occur. The complications of high blood pressure is usually fatal and disabling.

For most people with high blood pressure, a doctor will develop a treatment plan that may include heart-healthy lifestyle changes alone or with medicines. Heart-healthy lifestyle changes, such as heart-healthy eating, can be highly effective in treating high blood pressure. The absolute risk reduction (ARR) in ca…

AI replaces radiologist doctors on diagnosing pneumonia

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A group of Artificial Intelligence researchers at Stanford AI Lab used Deep Learning to create an algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists.


An x-ray (radiograph) is a noninvasive medical test that helps physicians diagnose and treat medical conditions. A chest x-ray is an x-ray of the chest showing the shadows of lungs, heart, large arteries, ribs, diaphragm, etc. 

They trained their Deep Learning (AI) algorithm, named CheXNet, using the dataset provided by National Institutes of Health, which contained 112,120 frontal-view X-ray images of 30,805 unique patients, annotated with up to 14 different diseases of the lungs. CheXNet is a convolutional neural network. A convolutional neural network is a type of Deep Learning algorithm which is used to detect and classify images such as photo tagging, face detection, etc.

Their algorithm is a Deep Learning model which inputs a chest X-ray image and outputs the probability of pneumonia a…