A web developer is a programmer who develops World Wide Web applications using a client–server model. The applications typically use HTML, CSS, and JavaScript in the client, and any general-purpose programming language in the server. HTTP is used for communications between client and server. A web developer may specialize in client-side applications (Front-end web development), server-side applications (back-end development), or both (full-stack development). == Prerequisite == There are no formal educational or license requirements to become a web developer. However, many colleges and trade schools offer coursework in web development. There are also many tutorials and articles which teach web development, often freely available on the web - for example, on JavaScript. Even though there are no formal requirements, web development projects require web developers to have knowledge and skills such as: Using HTML, CSS, and JavaScript Programming/coding/scripting in one of the many server-side languages or frameworks Understanding server-side/client-side architecture and communication of the kind mentioned above Ability to utilize a database
Texture compression
Texture compression is a specialized form of image compression designed for storing texture maps in 3D computer graphics rendering systems. Unlike conventional image compression algorithms, texture compression algorithms are optimized for random access. Texture compression can be applied to reduce memory usage at runtime. Texture data is often the largest source of memory usage in a mobile application. == Tradeoffs == In their seminal paper on texture compression, Beers, Agrawala and Chaddha list four features that tend to differentiate texture compression from other image compression techniques. These features are: Decoding Speed It is highly desirable to be able to render directly from the compressed texture data and so, in order not to impact rendering performance, decompression must be fast. Random Access Since predicting the order that a renderer accesses texels would be difficult, any texture compression scheme must allow fast random access to decompressed texture data. This tends to rule out many better-known image compression schemes such as JPEG or run-length encoding. Compression Rate and Visual Quality In a rendering system, lossy compression can be more tolerable than for other use cases. Some texture compression libraries, such as crunch, allow the developer to flexibly trade off compression rate vs. visual quality, using methods such as rate–distortion optimization (RDO). Encoding Speed Texture compression is more tolerant of asymmetric encoding/decoding rates as the encoding process is often done only once during the application authoring process. Given the above, most texture compression algorithms involve some form of fixed-rate lossy vector quantization of small fixed-size blocks of pixels into small fixed-size blocks of coding bits, sometimes with additional extra pre-processing and post-processing steps. Block Truncation Coding is a very simple example of this family of algorithms. Because their data access patterns are well-defined, texture decompression may be executed on-the-fly during rendering as part of the overall graphics pipeline, reducing overall bandwidth and storage needs throughout the graphics system. As well as texture maps, texture compression may also be used to encode other kinds of rendering map, including bump maps and surface normal maps. Texture compression may also be used together with other forms of map processing such as mipmaps and anisotropic filtering. == Availability == Some examples of practical texture compression systems are S3 Texture Compression (S3TC), PVRTC, Ericsson Texture Compression (ETC) and Adaptive Scalable Texture Compression (ASTC); these may be supported by special function units in modern graphics processing units (GPUs). OpenGL and OpenGL ES, as implemented on many video accelerator cards and mobile GPUs, can support multiple common kinds of texture compression - generally through the use of vendor extensions. == Supercompression == A compressed-texture can be further compressed in what is called "supercompression". Fixed-rate texture compression formats are optimized for random access and are much less efficient compared to image formats such as PNG. By adding further compression, a programmer can reduce the efficiency gap. The extra layer can be decompressed by the CPU so that the GPU receives a normal compressed texture, or in newer methods, decompressed by the GPU itself. Supercompression saves the same amount of VRAM as regular texture compression, but saves more disk space and download size. == Neural Texture Compression == Random-Access Neural Compression of Material Textures (Neural Texture Compression) is a Nvidia's technology which enables two additional levels of detail (16× more texels, so four times higher resolution) while maintaining similar storage requirements as traditional texture compression methods. The key idea is compressing multiple material textures and their mipmap chains together, and using a small neural network, that is optimized for each material, to decompress them.
Sepp Hochreiter
Josef "Sepp" Hochreiter (born 14 February 1967) is a German computer scientist. Since 2018 he has led the Institute for Machine Learning at the Johannes Kepler University of Linz after having led the Institute of Bioinformatics from 2006 to 2018. In 2017 he became the head of the Linz Institute of Technology (LIT) AI Lab. Hochreiter is also a founding director of the Institute of Advanced Research in Artificial Intelligence (IARAI). Previously, he was at Technische Universität Berlin, at University of Colorado Boulder, and at the Technical University of Munich. He is a chair of the Critical Assessment of Massive Data Analysis (CAMDA) conference. Hochreiter has made contributions in the fields of machine learning, deep learning and bioinformatics, most notably the development of the long short-term memory (LSTM) neural network architecture, but also in meta-learning, reinforcement learning and biclustering with application to bioinformatics data. == Scientific career == === Long short-term memory (LSTM) === Hochreiter developed the long short-term memory (LSTM) neural network architecture in his diploma thesis in 1991 leading to the main publication in 1997. LSTM overcomes the problem of numerical instability in training recurrent neural networks (RNNs) that prevents them from learning from long sequences (vanishing or exploding gradient). In 2007, Hochreiter and others successfully applied LSTM with an optimized architecture to very fast protein homology detection without requiring a sequence alignment. LSTM networks have also been used in Google Voice for transcription and search, and in the Google Allo chat app for generating response suggestion with low latency. === Other machine learning contributions === Beyond LSTM, Hochreiter has developed "Flat Minimum Search" to increase the generalization of neural networks and introduced rectified factor networks (RFNs) for sparse coding which have been applied in bioinformatics and genetics. Hochreiter introduced modern Hopfield networks with continuous states and applied them to the task of immune repertoire classification. Hochreiter worked with Jürgen Schmidhuber in the field of reinforcement learning on actor-critic systems that learn by "backpropagation through a model". Hochreiter has been involved in the development of factor analysis methods with application to bioinformatics, including FABIA for biclustering, HapFABIA for detecting short segments of identity by descent and FARMS for preprocessing and summarizing high-density oligonucleotide DNA microarrays to analyze RNA gene expression. In 2006, Hochreiter and others proposed an extension of the support vector machine (SVM), the "Potential Support Vector Machine" (PSVM), which can be applied to non-square kernel matrices and can be used with kernels that are not positive definite. Hochreiter and his collaborators have applied PSVM to feature selection, including gene selection for microarray data. == Awards == Hochreiter was awarded the IEEE CIS Neural Networks Pioneer Prize in 2021 for his work on LSTM.
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François Chollet
François Chollet (French: [fʁɑ̃swa ʃoˈlɛ]; born 20 October 1989) is a French software engineer, artificial intelligence (AI) researcher, and former Senior Staff Engineer at Google. Chollet is the creator of the Keras deep-learning library released in 2015. His research focuses on computer vision, the application of machine learning to formal reasoning, abstraction, and how to achieve greater generality in artificial intelligence (AGI). == Education and career == In 2012, Chollet graduated with a Diplôme d'Ingénieur (Master of Engineering) from ENSTA Paris, a school of the Polytechnic Institute of Paris. In 2015, Chollet started working at Google shortly after releasing Keras. In 2019, he published the Abstraction and Reasoning Corpus for Artificial General Intelligence (ARC-AGI) benchmark, which measures the ability of AI systems to solve novel reasoning problems. In 2024, Chollet launched ARC Prize, a US$1 million competition to solve the ARC-AGI benchmark. He left Google in November 2024 after more than 9 years with the company to found with Zapier co-founder Mike Knoop a new startup focused on developing AGI with program synthesis. In early 2025, Chollet announced the expansion of ARC Prize into a full-fledged non-profit foundation, to further the mission of guiding and accelerating research progress towards artificial general intelligence. == Books and publications == Chollet's research papers in artificial intelligence have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference on Neural Information Processing Systems (NeurIPS), and the International Conference on Learning Representations (ICLR). Chollet is the author of Xception: Deep Learning with Depthwise Separable Convolutions, which is among the top ten most cited papers in CVPR proceedings at more than 18,000 citations. Chollet is the author of the book Deep Learning with Python, which sold over 100,000 copies, and the co-author with Tomasz Kalinowski of Deep Learning With R. == Awards == On December 1, 2021, Chollet won the Global Swiss AI Award for breakthroughs in AI. In September 2024, Chollet was named by TIME as one of the 100 most influential people in AI.
Face Swap Live
Face Swap Live is a mobile app created by Laan Labs that enables users to swap faces with another person in real-time using the device's camera. It was released on December 14, 2015. In addition to swapping faces with another person, the app enables users to create videos using a set of bundled live filters. The app is available on iOS and Android devices. Face Swap Live was named Apple's #2 best-selling paid app in 2016.
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