# Keras Published articles for Keras. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Decoding cosmic signals with deep learning and Keras DevFeed: [Decoding cosmic signals with deep learning and Keras]() Original publisher: [Read original article]() Author: Yufeng Guo; Jonas Glombitza, PhD Published: 2026-09-12T11:04:33.891311Z Content type: article Language: en Sources: [Google Developers Blog]() Topics: [Deep learning](), [Keras](), [data]() Tags: [analysis](), [blog-post](), [data](), [deep-learning](), [keras](), [particle-physics](), [physics](), [research]() ### AI overview The article explains how deep learning and Keras can help analyze the enormous, complex datasets produced by astroparticle-physics observatories. These methods may improve instrument sensitivity, reveal hidden patterns, and identify anomalies in signals from cosmic messengers such as photons, neutrinos, and cosmic rays. ### Source excerpt Astroparticle physics sits at the exciting intersection of astrophysics and particle physics and stu... ## How Keras 3 Helped Modernise Expedia Group's Lodging Ranking Stack DevFeed: [How Keras 3 Helped Modernise Expedia Group's Lodging Ranking Stack]() Original publisher: [Read original article]() Author: Conor Worthington Published: 2026-08-11T11:01:02Z Content type: article Language: en Sources: [Expedia]() Topics: [Keras](), [recommendation systems](), [Latency](), [Inference Performance](), [Machine learning]() Tags: [data-science](), [features](), [framework](), [improvements](), [inference](), [keras](), [latency](), [llms](), [machine-learning](), [mixture-of-experts](), [model-training](), [neural-networks](), [ranking](), [recommendation-system]() ### AI overview Expedia Group describes modernising its lodging-ranking stack around Keras 3. The changes included rewriting parts of its pipelines, making model training 30% faster and reducing P99 inference latency by two-thirds. The article discusses limitations of the previous Keras 2, eager-training and combined-serving setup, along with the use of newer APIs and accelerator-oriented optimisations. ### Source excerpt Expedia Group Technology -- DataWhat happened when we treated a framework migration as an architecture modernisation -- and cut P99 inference latency by two-thirdsSt Paul's and millennium bridge, London Expedia Group™ has always been a market leader in providing personalised search experiences for travellers. As our ranking models evolved, we saw an opportunity not just to migrate to Keras 3, but to modernise the broader stack around it so we can better serve travellers. This led us to rewrite key parts of our pipelines that made model training 30% faster and cut P99 inference latency by two-thirds. Our main focus in this blog is to discuss our improvements to lodging ranking -- our service which handles users' search requests and returns a personalised property ranking on top of a lightweight candidate generator. This service is frequently retrained to improve customer experience as we get new signals over time. Example of personalised lodging ranking on Expedia search result page for a London search As such, our velocity to make changes needs to be fast, but more importantly we need to be able to build models which are state-of-the-art, enabling customers to easily find and book the most relevant property for their trip. The problem with Keras 2, eager training and combined serving The challenge was not that the old stack was broken. It was that it had gradually become a limiting factor. On the training side, we were carrying dependencies on older components and missing out on modern Keras APIs, new optimisers, new layers and cleaner support for accelerator-oriented training. On the serving side, we had a path that was simple and operationally familiar, but not especially well suited to aggressively optimised inference for heavier ranking architectures. This blockage on utilising new layers and optimisers has become more pronounced as LLMs see blistering amounts of innovation. Naturally, these new features are only available in Keras 3 or as separate backends from ou ## Gesture Recognition Based on TFLite DevFeed: [Gesture Recognition Based on TFLite]() Original publisher: [Read original article]() Author: John Lee Published: 2026-04-10T00:00:00Z Content type: tutorial Language: en Sources: [Blog on Developer Portal]() Topics: [TensorFlow Lite](), [Espressif](), [model-deployment](), [Tensorflow](), [Keras](), [On-device AI]() Tags: [ai](), [blog](), [deep-learning](), [deployment](), [development-board](), [edge-ai](), [edge-computing](), [esp-idf](), [espressif](), [how-to](), [inference](), [keras](), [model-deployment](), [model-training](), [tensorflow-lite]() ### AI overview This tutorial demonstrates gesture recognition on Espressif SoCs using TensorFlow Lite Micro. It covers data collection, model training, conversion for TFLite Micro, and deployment with C++ code for model loading, preprocessing, and inference. ### Source excerpt This article demonstrates how to implement gesture recognition using TensorFlow Lite Micro on Espressif SoCs. It covers the complete workflow from data collection and model training to model deployment, showcasing TensorFlow Lite Micro's applications in edge AI. ## 4M Models Scanned: Protect AI + Hugging Face 6 Months In DevFeed: [4M Models Scanned: Protect AI + Hugging Face 6 Months In]() Original publisher: [Read original article]() Author: Sean Morgan Published: 2025-04-14T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [hugging face](), [Machine Learning, Security Attacks](), [Threat Research](), [Security](), [threat detection](), [Zero Trust](), [obfuscation](), [vulnerability](), [payload](), [Script](), [llamafile](), [Tensorflow]() Tags: [ai-ml](), [hub](), [hugging-face](), [keras](), [llamafile](), [obfuscation](), [partnerships](), [payload](), [security](), [tensorflow](), [threat-detection](), [vulnerability](), [zero-trust]() ### AI overview Hugging Face and Protect AI report six months of collaboration on Guardian, a scanning system that improves security checks for models hosted on the Hugging Face Hub. Guardian added four detection modules, expanded format and obfuscation coverage, identified a Keras vulnerability, and provides inline alerts and vulnerability reports. The system uses a zero trust approach that treats arbitrary code execution as unsafe, including code hidden through obfuscation. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## What's new in TensorFlow 2.19 DevFeed: [What's new in TensorFlow 2.19]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2025-03-13T16:00:00Z Content type: release Language: en Sources: [The TensorFlow Blog]() Topics: [Tensorflow](), [LiteRT](), [releases](), [API](), [C++](), [Release notes](), [Keras](), [migration]() Tags: [2025](), [announcement](), [api](), [c-plus-plus](), [keras](), [libentensorflow](), [litert](), [migration-guide](), [pypi](), [release](), [release-notes](), [tensorflow](), [tensorflow-core](), [tensorflowlite]() ### AI overview TensorFlow 2.19 introduces C++ API changes in LiteRT, adds bfloat16 support for the tflite casting operation, and deprecates tf.lite.Interpreter in favor of ai_edge_litert.interpreter. The release also stops publishing libtensorflow packages, although they remain extractable from the PyPI package. Updates for multi-backend Keras are directed to keras.io. ### Source excerpt Posted by the TensorFlow team TensorFlow 2.19 has been released! Highlights of this release include changes to the C++ API in LiteRT, bfloat16 support for tflite casting, discontinue of releasing libtensorflow packages. Learn more by reading the full release notes. Note: Release updates on the new multi-backend Keras will be published on keras.io, starting with Keras 3.0. For more information, please see https://keras.io/keras_3/. TensorFlow Core LiteRT The public constants tflite::Interpreter:kTensorsReservedCapacity and tflite::Interpreter:kTensorsCapacityHeadroom are now const references, rather than constexpr compile-time constants. (This is to enable better API compatibility for TFLite in Play services while preserving the implementation flexibility to change the values of these constants in the future.) TF-Lite tfl.Cast op is now supporting bfloat16 in the runtime kernel. tf.lite.Interpreter gives a deprecation warning redirecting to its new location at ai_edge_litert.interpreter, as the API tf.lite.Interpreter will be deleted in TF 2.20. See the migration guide for details. Libtensorflow We have stopped publishing libtensorflow packages but it can still be unpacked from the PyPI package. ## Hugging Face and JFrog partner to make AI Security more transparent DevFeed: [Hugging Face and JFrog partner to make AI Security more transparent]() Original publisher: [Read original article]() Author: Luc Georges; Shachar M Published: 2025-03-04T00:00:00Z Content type: news Language: en Sources: [Hugging Face - Blog]() Topics: [Jfrog](), [Securing AI](), [hugging face](), [Exploit](), [Security](), [Machine Learning, Security Attacks](), [Open Source](), [Keras]() Tags: [ai-security](), [exploit](), [hub](), [hugging-face](), [jfrog](), [keras](), [open-source](), [partnerships](), [security]() ### AI overview Hugging Face and JFrog are partnering to improve security on the Hugging Face Hub. JFrog's scanner analyzes code embedded in model weights and supports detection of malicious usage across formats, including pickle and Keras Lambda layers, while public model repositories are scanned automatically. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## How good are LLMs at fixing their mistakes? A chatbot arena experiment with Keras and TPUs DevFeed: [How good are LLMs at fixing their mistakes? A chatbot arena experiment with Keras and TPUs]() Original publisher: [Read original article]() Author: Martin Görner Published: 2024-12-05T00:00:00Z Content type: opinion Language: en Sources: [Hugging Face - Blog]() Topics: [Large Language Model](), [Chat Bot](), [gradio]() Tags: [chatbots](), [gradio](), [hallucinations](), [keras](), [llm](), [llms](), [open-source](), [spaces](), [tpu]() ### AI overview The author describes a small experiment testing whether LLMs can correct code-generation mistakes after receiving feedback in plain English. The setup uses a mobile-assistant prompt that requires single-line executable Python API calls and compares conversations with multiple chatbots in a Gradio interface. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## "Llama 3.2 in Keras" DevFeed: ["Llama 3.2 in Keras"]() Original publisher: [Read original article]() Author: Martin Görner Published: 2024-10-21T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Keras](), [llama3](), [Large Language Model](), [tokenization](), [Fine-tuning](), [PyTorch](), [Tensorflow](), [Transformers]() Tags: [fine-tuning](), [keras](), [llama3](), [llm](), [llms](), [open-source](), [pytorch](), [tensorflow](), [tokenization](), [training](), [transformers]() ### AI overview The article introduces Llama 3.2 support in Keras, explaining that Hugging Face checkpoints can be loaded directly and converted on the fly when needed. It also describes Keras and keras-hub support for multiple backends, pretrained models, tokenization, preprocessing, training, and fine-tuning. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## What's new in TensorFlow 2.17 DevFeed: [What's new in TensorFlow 2.17]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2024-07-18T16:00:00Z Content type: release Language: en Sources: [The TensorFlow Blog]() Topics: [releases]() Tags: [announcement](), [cuda](), [gpu](), [keras](), [python](), [tensorflow](), [tensorflow-core](), [tensorrt]() ### AI overview TensorFlow 2.17 adds dedicated CUDA kernels for compute capability 8.9 GPUs and ends precompiled Python-package support for compute capability 5.0. The article also previews NumPy 2.0 support in TensorFlow 2.18 and the removal of TensorRT support after 2.17. ### Source excerpt Posted by the TensorFlow team TensorFlow 2.17 has been released! Highlights of this release (and 2.16) include CUDA update, upcoming Numpy 2.0, and more. For the full release notes, please click here. Note: Release updates on the new multi-backend Keras will be published on keras.io, starting with Keras 3.0. For more information, please see https://keras.io/keras_3/. TensorFlow Core CUDA Update TensorFlow binary distributions now ship with dedicated CUDA kernels for GPUs with a compute capability of 8.9. This improves the performance on the popular Ada-Generation GPUs like NVIDIA RTX 40**, L4 and L40. To keep Python wheel sizes in check, we made the decision to no longer ship CUDA kernels for compute capability 5.0. That means the oldest NVIDIA GPU generation supported by the precompiled Python packages is now the Pascal generation (compute capability 6.0). For Maxwell support, we either recommend sticking with TensorFlow version 2.16, or compiling TensorFlow from source. The latter will be possible as long as the used CUDA version still supports Maxwell GPUs. Numpy 2.0 Upcoming TensorFlow 2.18 release will include support for Numpy 2.0. This may break some edge cases of TensorFlow API usage. Drop TensorRT support Starting with TensorFlow 2.18, support for TensorRT will be dropped. TensorFlow 2.17 will be the last version to include it. ## Announcing New Hugging Face and KerasHub integration DevFeed: [Announcing New Hugging Face and KerasHub integration]() Original publisher: [Read original article]() Author: Aritra Roy Gosthipaty Published: 2024-07-10T00:00:00Z Content type: news Language: en Sources: [Hugging Face - Blog]() Topics: [hugging face](), [Keras](), [Transformers](), [gemma](), [llama](), [Open Source]() Tags: [gemma](), [hugging-face](), [json](), [keras](), [llama](), [nlp](), [open-source](), [open-source-collab](), [pytorch](), [tensorflow](), [transformers]() ### AI overview The article announces an integration between Hugging Face Transformers and KerasHub through a shared model save format. It allows KerasHub users to load many Transformers checkpoints, initially including Gemma, Llama 3, and PaliGemma, and use them with TensorFlow, JAX, or PyTorch backends. The integration handles conversion of configuration variables, weight names, and tokenizer vocabularies internally. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## What's new in TensorFlow 2.16 DevFeed: [What's new in TensorFlow 2.16]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2024-03-13T20:11:00Z Content type: release Language: en Sources: [The TensorFlow Blog]() Topics: [TensorFlow Core](), [Keras](), [Compiler](), [LLVM](), [Windows](), [pip](), [Python](), [MSVC]() Tags: [announcement](), [compiler](), [estimator](), [keras](), [llvm](), [msvc](), [python](), [release](), [tensorflow-core](), [windows]() ### AI overview TensorFlow 2.16 introduces Clang as the default compiler for TensorFlow CPU wheels on Windows, makes Keras 3 the default Keras version, and adds Python 3.12 support. The release also removes the tf.estimator API and changes the recommended installation method for Apple Silicon. ### Source excerpt Posted by the TensorFlow team TensorFlow 2.16 has been released! Highlights of this release (and 2.15) include Clang as default compiler for building TensorFlow CPU wheels on Windows, Keras 3 as default version, support for Python 3.12, and much more! For the full release note, please click here. Note: Release updates on the new multi-backend Keras will be published on keras.io starting with Keras 3.0. For more information, please see https://keras.io/keras_3/. TensorFlow Core Clang 17 Clang is now the preferred compiler to build TensorFlow CPU wheels on the Windows Platform starting with this release. The currently supported version is LLVM/clang 17. The official Wheels-published on PyPI will be based on Clang; however, users retain the option to build wheels using the MSVC compiler following the steps mentioned, as has been the case before. Intel owned the implementation and delivery of this change within the 3P Official Build program. Keras 3 Keras 3 will be the default Keras version for TensorFlow 2.16 onwards. You may need to update your script to use Keras 3. Please refer to the new Keras documentation for Keras 3 (https://keras.io/keras_3). Keras 2 will continue to be released alongside TensorFlow as tf_keras. To continue using Keras 2 with TensorFlow 2.16+: Install tf-keras vía pip install tf-keras~=2.16 Switch tf.keras to use Keras 2 (tf-keras), by setting environment variable TF_USE_LEGACY_KERAS=1 directly or in your Python program by doing import os;os.environ["TF_USE_LEGACY_KERAS"]="1". Please note that this needs to be set before importing TensorFlow and will set it for all packages in your Python runtime program. Estimator API tf.estimator API is removed. If you need to use the estimator API, you need to use TF 2.15 or an earlier version. Apple Silicon If you previously installed TensorFlow using pip install tensorflow-macos, please update your installation method. Use pip install tensorflow from now on. tensorflow-macos package will no longer receive ## Graph neural networks in TensorFlow DevFeed: [Graph neural networks in TensorFlow]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2024-02-06T19:00:00Z Content type: release Language: en Sources: [The TensorFlow Blog]() Topics: [AI, ML & Data Engineering](), [Training AI Models]() Tags: [ai](), [deep-learning](), [graph-mining](), [graphs](), [keras](), [learn](), [machine-learning](), [networks](), [python](), [release](), [release-notes](), [tensorflow](), [training]() ### AI overview Announces TensorFlow GNN 1.0, a production-tested library for building and training large-scale graph neural networks, including heterogeneous graphs. ### Source excerpt Posted by Dustin Zelle - Software Engineer, Research and Arno Eigenwillig - Software Engineer, CoreML This article is also shared on the Google Research Blog Objects and their relationships are ubiquitous in the world around us, and relationships can be as important to understanding an object as its own attributes viewed in isolation -- for example: transportation networks, production networks, knowledge graphs, or social networks. Discrete mathematics and computer science have a long history of formalizing such networks them as graphs, consisting of nodes arbitrarily connected by edges in various irregular ways. Yet most machine learning (ML) algorithms allow only for regular and uniform relations between input objects, such as a grid of pixels, a sequence of words, or no relation at all. Graph neural networks, or GNNs for short, have emerged as a powerful technique to leverage both the graph's connectivity (as in the older algorithms DeepWalk and Node2Vec) and the input features on the various nodes and edges. GNNs can make predictions for graphs as a whole (Does this molecule react in a certain way?), for individual nodes (What's the topic of this document, given its citations?) or for potential edges (Is this product likely to be purchased together with that product?). Apart from making predictions about graphs, GNNs are a powerful tool used to bridge the chasm to more typical neural network use cases. They encode a graph's discrete, relational information in a continuous way so that it can be included naturally in another deep learning system. We are excited to announce the release of TensorFlow GNN 1.0 (TF-GNN), a production-tested library for building GNNs at large scale. It supports both modeling and training in TensorFlow as well as the extraction of input graphs from huge data stores. TF-GNN is built from the ground up for heterogeneous graphs where types and relations are represented by distinct sets of nodes and edges. Real-world objects and their relatio ## TensorFlow 2.15 update: hot-fix for Linux installation issue DevFeed: [TensorFlow 2.15 update: hot-fix for Linux installation issue]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2023-12-05T22:00:00Z Content type: release Language: en Sources: [The TensorFlow Blog]() Topics: [pip](), [Linux](), [TensorRT](), [CUDA](), [Python]() Tags: [announcement](), [dependencies](), [explore](), [installation](), [keras](), [linux](), [nvdia](), [nvidia](), [packages](), [release](), [tensorflow](), [tensorflow-core](), [tensorrt](), [update](), [x86-64]() ### AI overview TensorFlow released version 2.15.0.post1 to fix Linux x86_64 installation problems caused by missing TensorRT-related Python dependencies when installing TensorFlow 2.15 with NVIDIA CUDA dependencies via pip. The hotfix restores the intended installation behavior, while users pinning versions must specify 2.15.0.post1 explicitly. ### Source excerpt Posted by the TensorFlow team We are releasing a hot-fix for an installation issue affecting the TensorFlow installation process. The TensorFlow 2.15.0 Python package was released such that it requested tensorrt-related packages that cannot be found unless the user installs them beforehand or provides additional installation flags. This dependency affected anyone installing TensorFlow 2.15 alongside NVIDIA CUDA dependencies via pip install tensorflow[and-cuda]. Depending on the installation method, TensorFlow 2.14 would be installed instead of 2.15, or users could receive an installation error due to those missing dependencies. To solve this issue as quickly as possible, we have released TensorFlow 2.15.0.post1 for the Linux x86_64 platform. This version removes the tensorrt Python package dependencies from the tensorflow[and-cuda] installation method. Support for TensorRT is otherwise unaffected as long as TensorRT is already installed on the system. Now, pip install tensorflow[and-cuda] works as originally intended for TensorFlow 2.15. Using .post1 instead of a full minor release allowed us to push this release out quickly. However, please be aware of the following caveat: for users wishing to pin their Python dependency in a requirements file or other situation, under Python's version specification rules, tensorflow[and-cuda]==2.15.0 will not install this fixed version. Please use ==2.15.0.post1 to specify this exact version on Linux platforms, or a fuzzy version specification, such as ==2.15.*, to specify the most recent compatible version of TensorFlow 2.15 on all platforms. ## What's new in TensorFlow 2.15 DevFeed: [What's new in TensorFlow 2.15]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2023-11-17T18:55:00Z Content type: release Language: en Sources: [The TensorFlow Blog]() Topics: [TensorFlow Core](), [CUDA](), [cpu](), [Linux](), [pip](), [Windows](), [Compiler]() Tags: [announcement](), [compiler](), [cpu](), [cuda](), [explore](), [installation](), [keras](), [linux](), [nvdia](), [performance](), [release](), [tensorflow](), [tensorflow-core](), [windows]() ### AI overview TensorFlow 2.15 introduces simpler optional installation of NVIDIA CUDA libraries through pip on Linux, provided the NVIDIA driver is already installed. It also enables oneDNN CPU optimizations by default for Windows x64 and x86 packages, adds full tf.function type support, upgrades CUDA to 12.2, and uses Clang 17 for package builds and source compilation. ### Source excerpt Posted by the TensorFlow team TensorFlow 2.15 has been released! Highlights of this release (and 2.14) include a much simpler installation method for NVIDIA CUDA libraries for Linux, oneDNN CPU performance optimizations for Windows x64 and x86, full availability of tf.function types, an upgrade to Clang 17.0.1, and much more! For the full release note, please check here. Note: Release updates on the new multi-backend Keras will be published on keras.io starting with Keras 3.0. For more information, please check here. TensorFlow Core NVIDIA CUDA libraries for Linux The tensorflow pip package has a new, optional installation method for Linux that installs necessary NVIDIA CUDA libraries through pip. As long as the NVIDIA driver is already installed on the system, you may now run pip install tensorflow[and-cuda] to install TensorFlow's NVIDIA CUDA library dependencies in the Python environment. Aside from the NVIDIA driver, no other pre-existing NVIDIA CUDA packages are necessary. In TensorFlow 2.15, CUDA has been upgraded to version 12.2. oneDNN CPU performance optimizations For Windows x64 & x86 packages, oneDNN optimizations are now enabled by default on X86 CPUs. These optimizations can be enabled or disabled by setting the environment variable TF_ENABLE_ONEDNN_OPTS to 1 (enable) or 0 (disable) before running TensorFlow. To fall back to default settings, simply unset the environment variable. tf.function tf.function types are now fully available. tf.types.experimental.TraceType now allows custom tf.function inputs to declare Tensor decomposition and type casting support. Introducing tf.types.experimental.FunctionType as the comprehensive representation of the signature of tf.function callables. It can be accessed through the function_type property of tf.function's and ConcreteFunctions. See the tf.types.experimental.FunctionType documentation for more details. Introducing tf.types.experimental.AtomicFunction as the fastest way to perform TF computations in Python. ## Simulated Spotify Listening Experiences for Reinforcement Learning with TensorFlow and TF-Agents DevFeed: [Simulated Spotify Listening Experiences for Reinforcement Learning with TensorFlow and TF-Agents]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2023-10-19T19:00:00Z Content type: article Language: en Sources: [The TensorFlow Blog]() Topics: [TensorFlow Agents](), [recommendation systems](), [AI, ML & Data Engineering](), [Training AI Models](), [TFX]() Tags: [ai](), [documentation](), [keras](), [learn](), [machine-learning](), [offline](), [reinforcement-learning](), [tensorflow](), [tensorflow-agents](), [tfx]() ### AI overview Spotify describes using TensorFlow and TF-Agents to build an offline simulator for reinforcement-learning-based music recommendations. The simulator supported training and evaluating recommendation models and RL agents, with offline estimates reported as strongly correlated with live experimental results. ### Source excerpt Posted by Surya Kanoria, Joseph Cauteruccio, Federico Tomasi, Kamil Ciosek, Matteo Rinaldi, and Zhenwen Dai - Spotify Introduction Many of our music recommendation problems involve providing users with ordered sets of items that satisfy users' listening preferences and intent at that point in time. We base current recommendations on previous interactions with our application and, in the abstract, are faced with a sequential decision making process as we continually recommend content to users. Reinforcement Learning (RL) is an established tool for sequential decision making that can be leveraged to solve sequential recommendation problems. We decided to explore how RL could be used to craft listening experiences for users. Before we could start training Agents, we needed to pick a RL library that allowed us to easily prototype, test, and potentially deploy our solutions. At Spotify we leverage TensorFlow and the extended TensorFlow Ecosystem (TFX, TensorFlow Serving, and so on) as part of our production Machine Learning Stack. We made the decision early on to leverage TensorFlow Agents as our RL Library of choice, knowing that integrating our experiments with our production systems would be vastly more efficient down the line. One missing bit of technology we required was an offline Spotify environment we could use to prototype, analyze, explore, and train Agents offline prior to online testing. The flexibility of the TF-Agents library, coupled with the broader advantages of TensorFlow and its ecosystem, allowed us to cleanly design a robust and extendable offline Spotify simulator. We based our simulator design on TF-Agents Environment primitives and using this simulator we developed, trained and evaluated sequential models for item recommendations, vanilla RL Agents (PPG, DQN) and a modified deep Q-Network, which we call the Action-Head DQN (AH-DQN), that addressed the specific challenges imposed by the large state and action space of our RL formulation. Through li ## What's new in TensorFlow 2.13 and Keras 2.13? DevFeed: [What's new in TensorFlow 2.13 and Keras 2.13?]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2023-07-25T16:00:00Z Content type: release Language: en Sources: [The TensorFlow Blog]() Topics: [TensorFlow Core](), [Keras](), [TensorFlow Lite](), [datasets](), [API]() Tags: [apple](), [datasets](), [google](), [keras](), [mac](), [python](), [quantization](), [release](), [tensorflow](), [tensorflow-core](), [tensorflow-lite](), [usability]() ### AI overview TensorFlow 2.13 and Keras 2.13 introduce Apple Silicon wheels, make the Keras V3 format the default for .keras files, and add improvements to TensorFlow Lite, tf.data, dataset padding, and CPU BF16 execution. ### Source excerpt Posted by the TensorFlow and Keras Teams TensorFlow 2.13 and Keras 2.13 have been released! Highlights of this release include publishing Apple Silicon wheels, the new Keras V3 format being default for .keras extension files and many more! TensorFlow Core Apple Silicon wheels for TensorFlow TensorFlow 2.13 is the first version to provide Apple Silicon wheels, which means when you install TensorFlow on an Apple Silicon Mac, you will be able to use the latest version of TensorFlow. The nightly builds for Apple Silicon wheels were released in March 2023 and this new support will enable more fine-grained testing, thanks to technical collaboration between Apple, MacStadium, and Google. tf.lite The Python TensorFlow Lite Interpreter bindings now have an option to use experimental_disable_delegate_clustering flag to turn-off delegate clustering during delegate graph partitioning phase. You can set this flag in TensorFlow Lite interpreter Python API interpreter = new Interpreter(file_of_a_tensorflowlite_model, experimental_preserve_all_tensors=False) The flag is set to False by default. This is an advanced feature in experimental that is designed for people who insert explicit control dependencies via with tf.control_dependencies() or need to change graph execution order. Besides, there are several operator improvements in TensorFlow Lite in 2.13 add operation now supports broadcasting up to 6 dimensions. This will remove explicit broadcast ops from many models. The new implementation is also much faster than the current one which calculates the entire index for both inputs the the input instead of only calculating the part that changes. Improve the coverage for 16x8 quantization by enabling int16x8 ops for exp, mirror_pad, space_to_batch_nd, batch_to_space_nd Increase the coverage of integer data types enabled int16 for less, greater_than, equal, bitcast, bitwise_xor, right_shift, top_k, mul, and int16 indices for gather and gather_nd enabled int8 for floor_div and floor_m ## KotlinDL 0.3: поддержка ONNX, Object Detection API, 20+ новых моделей в ModelHub, и много новых слоев DevFeed: [KotlinDL 0.3: поддержка ONNX, Object Detection API, 20+ новых моделей в ModelHub, и много новых слоев]() Original publisher: [Read original article]() Author: zaleslaw (JetBrains) Published: 2021-10-04T13:56:53Z Content type: release Language: ru Sources: [JetBrains RU]() Topics: [onnx](), [object-detection](), [Maven](), [Maven Central](), [Artificial Intelligence](), [Keras](), [PyTorch]() Tags: [big-data](), [central](), [deep-learning](), [fine-tuning](), [image-recognition](), [keras](), [kotlin](), [machine-learning](), [maven-central](), [object-detection](), [onnx](), [onnx-runtime](), [pytorch](), [tensorflow]() ### AI overview This release article presents KotlinDL 0.3, adding ONNX model support through ONNX Runtime Java API, new ModelHub models, an experimental high-level image-recognition API, additional layers, and Maven Central availability. It also describes object-detection support and fine-tuning limitations for ONNX models. ### Source excerpt Представляем версию 0.3 библиотеки глубокого обучения KotlinDL! Вас ждет множество новых фич: новые модели в ModelHub (включая модели для обнаружения объектов и распознавания лиц), возможность дообучать модели распознавания изображений, экспортированные из Keras и PyTorch в ONNX, экспериментальный высокоуровневый API для распознавания изображений и множество новых слоев, добавленных контрибьюторами. Также KotlinDL теперь доступен в Maven Central. В этой статье мы коснемся самых главных изменений релиза 0.3. Полный список изменений доступен по ссылке. Узнать больше о релизе ## KotlinDL 0.2: Functional API, зоопарк моделей c ResNet и MobileNet, DSL для обработки изображений DevFeed: [KotlinDL 0.2: Functional API, зоопарк моделей c ResNet и MobileNet, DSL для обработки изображений]() Original publisher: [Read original article]() Author: zaleslaw (JetBrains) Published: 2021-05-25T08:54:42Z Content type: release Language: ru Sources: [JetBrains RU]() Topics: [Kotlin](), [Deep neural networks](), [Maven Central](), [Tensorflow](), [Keras](), [ImageNet]() Tags: [dataset](), [deep-learning](), [image-processing](), [keras](), [kotlin](), [machine-learning](), [maven-central](), [neural-networks](), [tag-00486ebd8d87](), [tag-9d8cf70dc46c](), [tensorflow]() ### AI overview This article presents KotlinDL 0.2, a deep-learning library written in Kotlin with TensorFlow used for low-level computations. The release adds a Functional API for graph-based neural networks, an image-preprocessing DSL, new dataset types, and a model zoo containing ResNet, MobileNet, and VGG models. KotlinDL 0.2 is available on Maven Central. ### Source excerpt Представляем вам версию 0.2 библиотеки KotlinDL. Это библиотека глубокого обучения, где для низкоуровневых вычислений используется TensorFlow, но с высокоуровневым API и логикой, написанными на Kotlin. KotlinDL 0.2 теперь доступен на Maven Central (до этого он лежал на bintray, но закатилось солнышко земли опенсорсной). Появилось столько всего нового: новые слои, специальный DSL для препроцессинга изображений, новые типы датасетов, зоопарк моделей с несколькими моделями из семейства ResNet, MobileNet и старой доброй моделью VGG (рабочая лошадка, впрочем). В этой статье мы коснемся самых главных изменений релиза 0.2. Полный список изменений доступен по ссылке. Читать далее ## Neural network inference pipeline for videos in Tensorflow DevFeed: [Neural network inference pipeline for videos in Tensorflow]() Original publisher: [Read original article]() Author: Suresh Alse (noreply@blogger.com) Published: 2019-08-08T18:03:00Z Content type: tutorial Language: en Sources: [Life Plus Linux]() Topics: [Tensorflow](), [Inference](), [Machine learning](), [GPU](), [cpu](), [dataset](), [Keras]() Tags: [cpu](), [dataset](), [deeplearning](), [gpu](), [inference](), [keras](), [performance](), [pipeline](), [tensorflow]() ### AI overview A tutorial on building an efficient TensorFlow pipeline for video inference. It describes processing video frames in batches with tf.data.Dataset, parallelizing CPU preprocessing and I/O, and prefetching to keep CPU and GPU work supplied. ### Source excerpt Just as we saw a huge influx of images in the past decade or so, we are now seeing a lot of videos being produced on social media. The need to understand and moderate videos using machine learning has never been greater. In this post, I will show you how to build an efficient pipeline to processes videos in Tensorflow. For simplicity, let us consider a Resnet50 model pre-trained on Imagenet. Pretty straightforward, using tf.keras.applications Now, let us break it up to see what exactly is happening: We are loading the model with weights. We are reading an image and resizing it to 224x224. Do some preprocessing of the image. Run inference. Do some post processing. If we want to do something similar for large videos, we need to have a pipeline that takes a stream of frames from the video, applies preprocess transformations, run inference of frames, unravel the inferences and apply post processing. We can see that doing all these in a sequence - frame by frame is clearly not the right thing as it is slow and inefficient. In order to tackle this, we will use tf.data.Dataset and run inference in batch. First, lets create a generator that can produce frames from a video: We will use the tf.data.Dataset.from_generator method to create a dataset object out of this. Now let us define a function which does resizing, normalization and other preprocessing steps that are required on a batch of frames. Then, using the batch operation on the dataset created above, create a batch of size 64. Map the preprocess method that we defined onto the batch in parallel on CPU as it is a CPU intensive task. It is important to make sure that I/O is parallelized as much as possible. For best performance, instructions that are well suited for CPU should run on CPU and the ones suited for GPU should run on GPU. Also, If you observe the code above, we are prefetching. What this means is that, before consuming the dataset, a batch of 64 frames are preprocessed and is ready for consumption. By the t ## ThisEmoteDoesNotExist: Training a GAN for Twitch Emotes DevFeed: [ThisEmoteDoesNotExist: Training a GAN for Twitch Emotes]() Original publisher: [Read original article]() Author: Avery Gnolek Published: 2019-07-24T22:05:46Z Content type: tutorial Language: en Sources: [Twitch]() Topics: [Twitch](), [Deep learning](), [Keras](), [datasets](), [Python](), [pixel]() Tags: [dataset](), [deep-learning](), [engineering](), [keras](), [machine-learning](), [neural-networks](), [python](), [training](), [twitch]() ### AI overview A developer describes building a progressive-growing GAN for Twitch emotes as a personal machine-learning project. The article covers scraping roughly 2 million Twitch emote assets, implementing a proof-of-concept GAN with Keras, and training it on a random 200,000-image sample. ### Source excerpt The idea for this project began when a coworker and I were talking about NVIDIA's photo-realistic generated human faces using StyleGAN and they mentioned "I wish someone made one of those for Twitch emotes." I had always wanted to take some time to learn more about convolution neural networks and was in the middle of a machine learning project for work, so it seemed like trying to build this would be a quick and relevant personal project. The original plan (with my time estimates): Scrape all of the Twitch emote image assets (1 day) Write a progressive growing GAN implementation using Keras as a proof of concept (1 week) Adapt the emote dataset for use with a real research-caliber GAN implementation (1-2 days) Train the GAN on the emote dataset (1 day) 1 . Scrape all of the emote image assets This part of the project was actually quite straightforward. Twitch stores all of the emote images on a CDN using a monotonically increasing numeric emote_id. Each emote is available in three different sizes (1.0, 2.0, 3.0) corresponding to the resolutions of 28x28, 56x56, and 112x112. emoticons/778927/1.0emoticons/778927/2.0emoticons/v1/778927/3.0 I wrote a quick python scraper which would go through ~2 million emote_ids of three different sizes and download the images locally. Even at a modest 200 requests per second, this was able to finish overnight. In fact, I think the limiting factor for the speed of this step was actually writing the image files to my HDD, not the network requests themselves. So far, I was right on track with my time estimate. 2. Write a progressive growing GAN implementation using Keras as a proof of concept As I had no experience with CNNs or deep learning before this project, I wanted to at least attempt to write a progressive growing GAN myself as a way to learn before switching to an existing implementation. As Twitch emotes are not even power of two sizes, it was not possible to exactly replicate the 4x4 pixel to 128x128 pixel growing architecture ## How to Create a Malware Detection System With Machine Learning DevFeed: [How to Create a Malware Detection System With Machine Learning]() Original publisher: [Read original article]() Author: Simone Margaritelli Published: 2019-05-22T21:59:13Z Content type: tutorial Language: en Sources: [evilsocket]() Topics: [Malware](), [Machine Learning & Artificial Intelligence](), [Neural Network](), [Windows](), [Code]() Tags: [ai](), [antivirus](), [binary-analysis](), [classification](), [computer-virus](), [cuda](), [cybersecurity](), [deep-learning](), [deep-neural-networks](), [deployment](), [dnn](), [ergo](), [feature-engineering](), [features](), [gpu](), [how-to](), [keras](), [machine-learning](), [malware](), [malware-detection](), [neural-network](), [neural-networks](), [nvidia](), [portable-executable](), [security-research](), [tensorflow](), [tf](), [windows-pe]() ### AI overview A practical tutorial on using machine learning and artificial neural networks to detect Windows malware without relying on an explicit signatures database. It uses malware detection as an example for the ergo project, which automates parts of model creation, data encoding, GPU training, benchmarking, and deployment. ### Source excerpt In this post we'll talk about two topics I love and that have been central elements of my (private) research for the last ~7 years: machi ## Presenting Project Ergo: How to Build an Airplane Detector for Satellite Imagery With Deep Learning DevFeed: [Presenting Project Ergo: How to Build an Airplane Detector for Satellite Imagery With Deep Learning]() Original publisher: [Read original article]() Author: Simone Margaritelli Published: 2018-11-22T17:15:50Z Content type: tutorial Language: en Sources: [evilsocket]() Topics: [Keras](), [Tensorflow](), [Framework](), [Deep learning](), [Deep neural networks](), [datasets](), [Open Source](), [Code](), [Development](), [Back end](), [Python](), [Shell]() Tags: [cnn](), [code](), [computer-vision](), [convolutional-neural-networks](), [cpu](), [cuda](), [cudnn](), [cybersecurity](), [deep-learning](), [deep-neural-networks](), [dnn](), [ergo](), [how-to](), [image-classification](), [keras](), [machine-learning](), [neural-networks](), [nvidia](), [open-source](), [planes](), [planes-detector](), [planesnet](), [project-release](), [python](), [shell](), [tensorflow](), [tf](), [train](), [training](), [tutorial]() ### AI overview The article introduces Project Ergo, an open-source framework and manager for Keras-based machine-learning projects. It demonstrates prototyping, training, and testing a convolutional neural network with the PlanesNet dataset to build an airplane detector for satellite imagery, including CPU and GPU setup considerations. ### Source excerpt It's been a while that i've been quite intensively playing with Deep Learning both for work related research and personal projects. More specifically, I've been using the Keras framework on top of a TensorFlow backend for all sorts of stuff. From big and complex projects for malware detection, to smaller and simpler experiments about ideas i just wanted to quickly implement and test - it didn't really matter the scope of the project, I always found myself struggling with the same issues: code reuse over tens of crap python and shell scripts, datasets and models that are spread all over my dev and prod servers, no real standard for versioning them, no order, no structure. So a few days ago I started writing what it was initially meant to be just a simple wrapper for the main commands of my training pipelines but quickly became a full-fledged framework and manager for all my Keras based projects. Today I'm pleased to open source and present project Ergo by showcasing an example use-case: we'll prototype, train and test a Convolutional Neural Network on top of the PlanesNet raw dataset in order to build an airplane detector for satellite imagery. ## Using neural networks to predict customers' needs DevFeed: [Using neural networks to predict customers' needs]() Original publisher: [Read original article]() Author: Ik-Hwan Kim Published: 2017-10-05T18:09:46Z Content type: article Language: en Sources: [Square Corner Blog](), [Square Corner Blog RSS Feed]() Topics: [Neural Network](), [Deep learning](), [Machine learning](), [data](), [Library]() Tags: [data](), [data-science](), [deep-learning](), [keras](), [machine-learning](), [neural-networks]() ### AI overview Square describes using web-page browsing and path data from its Seller Dashboard to understand seller behavior. It compares traditional analytic methods with machine learning and reports that a neural network model identified usage patterns associated with sellers experiencing problems. ### Source excerpt Deep learning for browsing and path analysis. ## Deep NLP-based Recommenders at Finn.no DevFeed: [Deep NLP-based Recommenders at Finn.no]() Original publisher: [Read original article]() Author: Simen Eide Published: 2017-09-08T13:56:49Z Content type: tutorial Language: en Sources: [Finn.no]() Topics: [Natural language processing](), [Deep learning](), [Embeddings](), [recommendations](), [Keras](), [Hackathon]() Tags: [data](), [deep-learning](), [embeddings](), [hackathon](), [keras](), [model-architecture](), [neural-networks](), [nlp](), [recommendations]() ### AI overview The article describes a FINN.no hackathon project exploring deep NLP-based recommendations for classified ads. The team used categorized ad data, word embeddings, and a convolutional neural network architecture to model similarity, but the supplied text does not include the final performance results. ### Source excerpt During a hackathon at FINN.no, we figured we wanted to learn more about deep NLP-models. FINN.no has a large database with ads of people trying to sell stuff (around 1 million active ads at any time), and they are categorized into a category tree with three or four layers. For example, full suspension bikes can be found under "Sport and outdoor activities" / "Bike sport" / "Full suspension bikes". In our daily jobs we are working on recommendations. There, we already have a content based (tf-idf) recommender build on Solr's More Like This. It seems to work well in areas where our collaborative filtering approaches does not. Would it be possible to build a deep learning NLP-model of similar performance? To achieve a measure of similarity, building a classifier of the previously mentioned categories seemed like a good choice, since we already had a lot of pre-existing data. The NLP team at Schibsted had already tokenized around six million ads as well as trained a word2vec model for us - we were ready to roll! Some preprocessing still had to be done. We ran through all ads, concatenated the title and description strings, and after a quick look at the data took the first 15 words of each ad. Model architecture proposed by the paper Our initial experiments were done with a simple "Bag of words" model included in the Keras repository, but we promptly switched over to "Convolutional Neural Networks for Sentence Classification" based architecture after hearing about it from our colleague, Tobias. By looking at the first 15 words of the ad, and using 200 dimensional embeddings for each word, our input is transformed into a 15x200 matrix. We apply three different convolutions on each document. The three convolutions looks at 2, 3 and 4 words (kernel sizes) in each convolution. It then max-pools each over the whole document, so that you end up with one value per document per convolution. For each kernel size you do 100 different filters. Finally you add a dense layer for clas