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12 Best Deep Learning Courses on Coursera

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This is another specialization program offered by Coursera. This specialization program is for both computer science professionals and healthcare professionals. In this specialization program, you will learn how to identify the healthcare professional's problems that can be solved by machine learning. You will also learn the fundamentals of the U.S. healthcare system, the framework for successful and ethical medical data mining, the fundamentals of machine learning as it applies to medicine and healthcare, and much more. This specialization program has 5 courses. Let's see the details of the courses-


Using regression techniques to predict a student's grade for a course

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I will be using Keras and TensorFlow to train a deep neural network to predict the grade using 2 hidden layers, mean squared error loss, and an RMSprop optimizer. Let's graph the error and the loss during training and evaluate the model We are getting a 0.69 mean absolute error with this approach. We also need to save the model to deploy it in an API. Since I am using google Colab I can easily save it to google drive. Initialize a random forest with 100 decision trees and train it on the same data.


Math for Machine Learning: 14 Must-Read Books - Machine Learning Techniques

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It is possible to design and deploy advanced machine learning algorithms that are essentially math-free and stats-free. People working on that are typically professional mathematicians. These algorithms are not necessarily simpler. See for instance a math-free regression technique with prediction intervals, here. Or supervised classification and alternative to t-SNE, here. Interestingly, this latter math-free machine


Identifying Cyber Threats Before They Happen: Deep Learning

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Crypto.com, Microsoft, NVidia, and Okta all got hacked this year. In some hacks, attackers are looking to take data, while some are just trying things out. Either way, it is in the interest of companies to patch up the holes in their security systems as more attackers are learning to take advantage of them. The project I am working on now is one to prevent cyber threats like these from happening. When a company is hacked, there is a lot at stake.


Machine Learning Tutorial For Complete Beginners

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Let us start with an easy example, say you are teaching a kid to differentiate dogs from cats. How would you do it? You may show him/her a dog and say "here is a dog" and when you encounter a cat you would point it out as a cat. When you show the kid enough dogs and cats, he may learn to differentiate between them. If he is trained well, he may be able to recognise different breeds of dogs which he hasn't even seen. Similarly, in Supervised Learning, we have two sets of variables.


Special Issue! Foundational Algorithms, Where They Came From, Where They're Going

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Years ago, I had to choose between a neural network and a decision tree learning algorithm. It was necessary to pick an efficient one, because we planned to apply the algorithm to a very large set of users on a limited compute budget. I went with a neural network. I hadn't used boosted decision trees in a while, and I thought they required more computation than they actually do -- so I made a bad call. Fortunately, my team quickly revised my decision, and the project was successful. This experience was a lesson in the importance of learning, and continually refreshing, foundational knowledge. If I had refreshed my familiarity with boosted trees, I would have made a better decision.


Machine Learning: Theory and Hands-on Practice with Python

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In the Machine Learning specialization, we will cover Supervised Learning, Unsupervised Learning, and the basics of Deep Learning. You will apply ML algorithms to real-world data, learn when to use which model and why, and improve the performance of your models. Starting with supervised learning, we will cover linear and logistic regression, KNN, Decision trees, ensembling methods such as Random Forest and Boosting, and kernel methods such as SVM. Then we turn our attention to unsupervised methods, including dimensionality reduction techniques (e.g., PCA), clustering, and recommender systems. We finish with an introduction to deep learning basics, including choosing model architectures, building/training neural networks with libraries like Keras, and hands-on examples of CNNs and RNNs.


What all you need to become a data scientist?

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There is no single starting point or path you can follow to become a data scientist. You can start from anywhere -- from a science, engineering, commerce graduate, Ph. D degree and continue your journey with coding any kind of problem you see around, to attending online courses, participating in a Kaggle competition or doing a data science project under a mentor. Even there is no single starting point or path still there is set of common skills and passions that you must possess. Mathematics & reasoning comes first and along that you should have a passion for coding/programming and problem solving.


Scaling assistive healthcare technology with 5G

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With recent advances in communication networks and machine learning (ML), healthcare is one of the key application domains which stands to benefit from many opportunities, including remote global healthcare, hospital services on cloud, remote diagnosis or surgeries, among others. One of those advances is network slicing, making it possible to provide high-bandwidth, low-latency and personalized healthcare services for individual users. This is important for patients using healthcare monitoring devices that capture various biological signals (biosignals) such as from the heart (ECG), muscles (EMG), brain (EEG), or activities from other parts of the body. In this blog, we discuss the challenges to building a scalable delivery platform for such connected healthcare services, and how technological advances can help to transform this landscape significantly for the benefit of both users and healthcare service providers. Our specific focus is on assistive technology devices which are increasingly being used by many individuals.


Deep Studying with Label Differential Privateness - Channel969

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Over the past a number of years, there was an elevated give attention to growing differential privateness (DP) machine studying (ML) algorithms. DP has been the idea of a number of sensible deployments in business -- and has even been employed by the U.S. Census -- as a result of it allows the understanding of system and algorithm privateness ensures. The underlying assumption of DP is that altering a single person's contribution to an algorithm mustn't considerably change its output distribution. In the usual supervised studying setting, a mannequin is educated to make a prediction of the label for every enter given a coaching set of instance pairs {[input1,label1], …, [inputn, labeln]}. Within the case of deep studying, earlier work launched a DP coaching framework, DP-SGD, that was built-in into TensorFlow and PyTorch.