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AI is reinventing the way we invent

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Drug discovery is a hugely expensive and often frustrating process. Medicinal chemists must guess which compounds might make good medicines, using their knowledge of how a molecule's structure affects its properties. They synthesize and test countless variants, and most are failures. "Coming up with new molecules is still an art, because you have such a huge space of possibilities," says Barzilay. "It takes a long time to find good drug candidates." By speeding up this critical step, deep learning could offer far more opportunities for chemists to pursue, making drug discovery much quicker.


Introduction to Federated Learning

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There are over 5 billion mobile device users all over the world. Such users generate massive amounts of data--via cameras, microphones, and other sensors like accelerometers--which can, in turn, be used for building intelligent applications. Such data is then collected in data centers for training machine/deep learning models in order to build intelligent applications. However, due to data privacy concerns and bandwidth limitations, common centralized learning techniques aren't appropriate--users are much less likely to share data, and thus the data will be only available on the devices. This is where federated learning comes into play. According to Google's research paper titled, Communication-Efficient Learning of Deep Networks from Decentralized Data [1], the researchers provide the following high-level definition of federated learning: A learning technique that allows users to collectively reap the benefits of shared models trained from [this] rich data, without the need to centrally store it.


A debate between AI experts shows a battle over the technology's future

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Since the 1950s, artificial intelligence has repeatedly overpromised and underdelivered. While recent years have seen incredible leaps thanks to deep learning, AI today is still narrow: it's fragile in the face of attacks, can't generalize to adapt to changing environments, and is riddled with bias. All these challenges make the technology difficult to trust and limit its potential to benefit society. On March 26 at MIT Technology Review's annual EmTech Digital event, two prominent figures in AI took to the virtual stage to debate how the field might overcome these issues. Gary Marcus, professor emeritus at NYU and the founder and CEO of Robust.AI, is a well-known critic of deep learning.


Automated histologic diagnosis of CNS tumors with machine learning

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A new mass discovered in the CNS is a common reason for referral to a neurosurgeon. CNS masses are typically discovered on MRI or computed tomography (CT) scans after a patient presents with new neurologic symptoms. Presenting symptoms depend on the location of the tumor and can include headaches, seizures, difficulty expressing or comprehending language, weakness affecting extremities, sensory changes, bowel or bladder dysfunction, gait and balance changes, vision changes, hearing loss and endocrine dysfunction. A mass in the CNS has a broad differential diagnosis, including tumor, infection, inflammatory or demyelinating process, infarct, hemorrhage, vascular malformation and radiation treatment effect. The most likely diagnoses can be narrowed based on patient demographics, medical history, imaging characteristics and adjunctive laboratory studies. However, accurate histopathologic interpretation of tissue obtained at the time of surgery is frequently required to make a diagnosis and guide intraoperative decision making. Over half of CNS tumors in adults are metastases from systemic cancer originating elsewhere in the body [1]. An estimated 9.6% of adults with lung cancer, melanoma, breast cancer, renal cell carcinoma and colorectal cancer have brain metastases [2].


Top 10 Courses to Learn AI, Machine Learning and Deep Learning

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Supervised, semi-supervised or unsupervised deep learning is part of a broader family of machine learning methods, that teach you the basics of neural networks. Learn from the Top 10 Deep Learning Courses curated exclusively by Analytics Insight and build your deep learning models with Python and NumPy. Taught by one of the best Data Science experts of 2020 Andrew Ng, this course teaches you how to build a successful machine learning project. You will understand the complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance. Over 20 videos spread across the entire module will explain you error analysis and different kind of the learning techniques.


Machine Learning vs Deep Learning vs AI - WebSystemer.no

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I had an interview with a big company and was asked a number of questions, but one that stuck out to me was when the interviewer asked "Do…


11 Essential Neural Network Architectures, Visualized & Explained

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The perceptron is the most basic of all neural networks, being a fundamental building block of more complex neural networks. It simply connects an input cell and an output cell. The feed-forward network is a collection of perceptrons, in which there are three fundamental types of layers -- input layers, hidden layers, and output layers. During each connection, the signal from the previous layer is multiplied by a weight, added to a bias, and passed through an activation function. Feed-forward networks use backpropagation to iteratively update the parameters until it achieves a desirable performance.


MLDB is the Database Every Data Scientist Dreams Of

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Machine learning solutions in the real world are rarely just a matter of building and testing models. Managing and automating the lifecycle of machine learning models from training to optimization is, by far, the hardest problem to solve in machine learning solutions. To control the lifecycle of a model, data scientists need to be able to persist and query its state at scale. This problem might seem trivial until you consider that any average deep learning model can include hundreds of hidden layers and millions of interconnected nodes;) Storing and accessing large computation graphs is far from trivial. Most of the times, data science teams spend a lot of time trying to adapt commodity NOSQL databases to machine learning models before arriving to the not-so-obvious conclusion: Machine learning solutions need a new type of database.


19 Frameworks, Libraries, and Tools That Developers Love (and Hate)

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Developers generally exhibit a strong affinity (usually paired with an equally strong hatred) for certain frameworks, libraries, and tools. But which ones do they love, dread, and want the most? Stack Overflow, as part of its enormous, annual Developers Survey, asked that very question, and the answers provide some interesting insights into how developers work. Some 65,000 developers responded to the survey, and the sheer size of that sample makes these breakdowns a bit more interesting to parse. For example, although game developers might have strong opinions about Unreal Engine and Unity 3D (which placed high on the following lists), those aren't used at all by the bulk of developers concerned with A.I. and machine learning, who have strong feelings about TensorFlow that many other developers might not share.


Getting Started with TensorFlow 2 - KDnuggets

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But wait… What is Tensorflow? Tensorflow is a Deep Learning Framework by Google, which released its 2nd version in 2019. It is one of the world's most famous Deep Learning frameworks widely used by Industry Specialists and Researchers. Tensorflow v1 was difficult to use and understand as it was less Pythonic, but with v2 released with Keras now fully synchronized with Tensorflow.keras, it is easy to use, easy to learn, and simple to understand. Remember, this is not a post on Deep Learning so I expect you to be aware of Deep Learning terms and the basic ideas behind it.