New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
We always heard that Neural Networks (NNs)are inspired by biological neural networks. This huge representation was done in a fantastic way. Figure 1 shows the anatomy of a single neuron. The central part is called the cell body where the nucleus resides. There are various wires which pass the stimulus to the cell body and few wires which send the output to the other neurons. The thickness of the dendrites implies the weight/bias/power of the stimulus.
Today the UK natural intelligence company Opteran has raised around €2.3 million in seed funding to pioneer its lightweight, silicon-based approach to autonomy, created by testing insect brains, in what to some would sound a little like a Black Mirror episode. Opteran is a University of Sheffield spin-out based on eight years of research by Professor James Marshall and Dr. Alex Cope into insect brains as part of the Green Brain and Brains on Board projects. Although insects have smaller brains, they are still capable of sophisticated decision making and navigation using optic flow to perceive depth and distance. The Opteran team state that this is a far more efficient, robust and transparent way to achieve autonomy than current deep learning techniques, enabling the team to reverse-engineer insect brains to produce algorithms requiring no data centre or extensive pre-training. It means Opteran can mimic tasks such as seeing, sensing objects, obstacle avoidance, navigation and decision making.
Training machine learning/deep learning models can take a really long time, and understanding what is happening as your model is training is absolutely crucial. Depending on the library or framework, this can be easier or more difficult, but pretty much always it is doable. Let me show how to monitor machine learning models in each case. Some frameworks, especially lower-level ones, don't have an elaborate callback system in place, and you have direct access to the training loop. One such framework example is PyTorch.
As deep learning has grown in popularity over the last two decades, more and more companies and developers have created frameworks to make deep learning more accessible. Now there are so many deep learning frameworks available that the average deep learning practitioner probably isn't even aware of all of them. With so many options available, which framework should you pick? In this article, I will give you a tour of some of the most common Python deep learning frameworks and compare them in a way that allows you to decide which framework is the right one to use in your projects. I have purposely bundled these two frameworks together because the latest versions of TensorFlow are tightly integrated with Keras.
Deep learning models (aka neural nets) now power everything from self-driving cars to video recommendations on a YouTube feed, having grown very popular over the last couple of years. Despite their popularity, the technology is known to have some drawbacks, such as the deep learning "reproducibility crisis"-- as it is very common for researchers at one to be unable to recreate a set of results published by another, even on the same data set. Additionally, the steep costs of deep learning would give any company pause, as the FAANG companies have spent over $30,000 to train just a single (very) deep net. Even the largest tech companies on the planet struggle with the scale, depth, and complexity of venturing into neural nets, while the same problems are even more pronounced for smaller data science organizations as neural nets can be both time-and cost-prohibitive. Also, there is no guarantee that neural nets will be able to outperform benchmark models like logistic regression or gradient-boosted ones, as neural nets are finicky and typically require added data and engineering complexities.
In machine learning (ML), if the situation when the model does not generalize well from the training data to unseen data is called overfitting. As you might know, it is one of the trickiest obstacles in applied machine learning. The first step in tackling this problem is to actually know that your model is overfitting. That is where proper cross-validation comes in. After identifying the problem you can prevent it from happening by applying regularization or training with more data. Still, sometimes you might not have additional data to add to your initial dataset. Acquiring and labeling additional data points may also be the wrong path. Of course, in many cases, it will deliver better results, but in terms of work, it is time-consuming and expensive a lot of the time.
COVID-19 virus hit us hard. Warnings from Nicolas Taleb that our interconnectedness could cause wide pandemic were true. Schools are closed and most of us are working from home, spending time in isolation and trying not to spread the virus. At the moment when I am writing this, all the borders in my home country are closed, all bars and malls are closed and you can not go out after 5 PM. Apart from that, this pandemic has a huge impact on the economy.
Deep Learning is now powering numerous AI technologies in daily life, and convolutional neural networks (CNNs) can apply complex treatments to images at high speeds. At Unity, we aim to propose seamless integration of CNN inference in the 3D rendering pipeline. Unity Labs, therefore, works on improving state-of-the-art research and developing an efficient neural inference engine called Barracuda. Deep learning has long been confined to supercomputers and offline computation, but their usability at real-time on consumer hardware is fast approaching thanks to ever-increasing compute capability. With Barracuda, Unity Labs hopes to accelerate its arrival in creators' hands.
Computational molecular physics (CMP) aims to leverage the laws of physics to understand not just static structures but also the motions and actions of biomolecules. Applying CMP to proteins has required either simplifying the physical models or running simulations that are shorter than the time scale of the biological activity. Brini et al. reviewed advances that are moving CMP to time scales that match biological events such as protein folding, ligand unbinding, and some conformational changes. They also highlight the role of blind competitions in driving the field forward. New methods such as deep learning approaches are likely to make CMP an increasingly powerful tool in describing proteins in action. Science , this issue p. [eaaz3041] ### BACKGROUND Understanding biology, particularly at the level of actionable drug discovery, is often a matter of developing accurate stories about how proteins work. This requires understanding the physics of the system, and physics-based computer modeling is a prime tool for that. However, the computational molecular physics (CMP) of proteins has previously been much too expensive and slow. A large fraction of public supercomputing resources worldwide is currently running CMP simulations of biologically relevant systems. We review here the history and status of this large and diverse scientific enterprise. Among other things, protein modeling has driven major computer hardware advances, such as IBM's Blue Gene and DE Shaw's Anton computers. Further, protein modeling has advanced rapidly over 50 years, even slightly faster than Moore's law. We also review an interesting scientific social construct that has arisen around protein modeling: community-wide blind competitions. They have transformed how we test, validate, and improve our computational models of proteins. ### ADVANCES For 50 years, two approaches to computer modeling have been mainstays for developing stories about protein molecules and their biological actions. (i) Inferences from structure-property relations: Based on the principle that a protein's action depends on its shape, it is possible to use databases of known proteins to learn about unknown proteins. (ii) Computational molecular physics uses force fields of atom-atom interactions, sampled by molecular dynamics (MD), to develop biological action stories that satisfy principles of chemistry and thermodynamics. CMP has traditionally been computationally costly, limited to studying only simple actions of small proteins. But CMP has recently advanced enormously. (i) Force fields and their corresponding solvent models are now sufficiently accurate at capturing the molecular interactions, and conformational searching and sampling methods are sufficiently fast, that CMP is able to model, fairly accurately, protein actions on time scales longer than microseconds, and sometimes milliseconds. So, we are now accessing important biological events, such as protein folding, unbinding, allosteric change, and assembly. (ii) Just as car races do for auto manufacturers, communal blind tests such as protein structure-prediction events are giving protein modelers a shared evaluation venue for improving our methods. CMP methods are now competing and often doing quite well. (iii) New methods are harnessing external information—like experimental structural data—to accelerate CMP, notably, while preserving proper physics. What are we learning? For one thing, a long-standing hypothesis is that proteins fold by multiple different microscopic routes, a story that is too granular to learn from experiments alone. CMP recently affirmed this principle while giving accurate and testable microscopic details, protein by protein. In addition, CMP is now contributing to physico-chemical drug design. Structure-based methods of drug discovery have long been able to discern what small-molecule drug candidates might bind to a given target protein and where on the protein they might bind. However, such methods don't reveal some all-important physical properties needed for drug discovery campaigns—the affinities and the on- and off-rates of the ligand binding to the protein. CMP is beginning to compute these properties accurately. A third example is shown in the figure. It shows the spike protein of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2), the causative agent of today's coronavirus disease 2019 (COVID-19) pandemic. A large, hinge-like movement of this sizable protein is the critical action needed for the virus to enter and infect the human cell. The only way to see the details of this motion—to attempt to block it with drugs—is by CMP. The figure shows CMP simulation results of three dynamical states of this motion. ### OUTLOOK A cell's behavior is due to the actions of its thousands of different proteins. Every protein has its own story to tell. CMP is a granular and principled tool that is able to discover those stories. CMP is now being tested and improved through blind communal validations. It is attacking ever larger proteins, exploring increasingly bigger and slower motions, and with ever more accurate physics. We are reaching a physical understanding of biology at the microscopic level as CMP reveals causations and forces, step-by-step actions in space and time, conformational distributions along the way, and important physical quantities such as free energies, rates, and equilibrium constants. ![Figure] CMP modeling of COVID-19 infecting the human cell. SARS-CoV-2 spike glycoprotein (green, with its glycan shield in yellow) attaching to the human angiotensin-converting enzyme 2 (ACE2) receptor protein (purple) through its spike receptor-binding domain (red). (Left) The receptor binding domain (RBD) is hidden. (Middle) The RBD is open and accessible. (Right) The RBD binds human ACE2 receptor. This is followed by a cascade of larger conformational changes in the spike protein, leading to viral fusion to the human host cell. Credit: Lucy Fallon Every protein has a story—how it folds, what it binds, its biological actions, and how it misbehaves in aging or disease. Stories are often inferred from a protein’s shape (i.e., its structure). But increasingly, stories are told using computational molecular physics (CMP). CMP is rooted in the principled physics of driving forces and reveals granular detail of conformational populations in space and time. Recent advances are accessing longer time scales, larger actions, and blind testing, enabling more of biology’s stories to be told in the language of atomistic physics. : /lookup/doi/10.1126/science.aaz3041 : pending:yes
Online Courses Udemy - Data science techniques for professionals and students - learn the theory behind logistic regression and code in Python BESTSELLER Created by Lazy Programmer Inc English [Auto-generated], Portuguese [Auto-generated], 1 more Students also bought Data Science: Deep Learning in Python Natural Language Processing with Deep Learning in Python Advanced AI: Deep Reinforcement Learning in Python Deep Learning: Advanced NLP and RNNs Deep Learning A-Z: Hands-On Artificial Neural Networks Preview this course GET COUPON CODE Description This course is a lead-in to deep learning and neural networks - it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own logistic regression module in Python. This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for free.