#TensorFlow

[feitgemel]

Discover how to build a CNN model for skin melanoma classification using over 20,000 images of skin lesions

We'll begin by diving into data preparation, where we will organize, clean, and prepare the data form the classification model.

Next, we will walk you through the process of build and train convolutional neural network (CNN) model. We'll explain how to build the layers, and optimize the model.

Finally, we will test the model on a new fresh image and challenge our model.

Check out our tutorial here : https://youtu.be/RDgDVdLrmcs

Enjoy

Eran

#Python #Cnn #TensorFlow #deeplearning #neuralnetworks #imageclassification #convolutionalneuralnetworks #SkinMelanoma #melonomaclassification

[rinumia-blog]

Gemini taking a hardball approach to diversity is a remarkable step. When one tech giant has concluded these tech hurdles in public sphere can be crushed , maybe we cut them some slack and allow the changes unfold.

[rabbirubiez]

💥 Meet Honey Drone ▶️ #Analytics #BigData #AI #IoT #MachineLearning #Serverless #TensorFlow #flutter #javascript #Robotics #CyberSecurity #IIoT #Robotic #Fintech #CES2023 #programming #Coding #100DaysOfCode Cc: @jblefevre60 (at Honey & Drone) https://www.instagram.com/p/CmdLGn5MQFC/?igshid=NGJjMDIxMWI=

[cecillian-hobbies]

FRAMEWORKS Y DEEP LEARNING

Un artículo que habla sobre las frameworks y deep learning, originado de mi investigación por la red para redes neurales artificiales

INTRODUCCIÓN Deep Learning es una rama de la inteligencia artificial que se basa en la utilización de redes neuronales profundas para el análisis de datos. Estas redes están compuestas por varias capas de nodos o neuronas, las cuales se encargan de procesar y analizar los datos de entrada. Una de las principales ventajas de las redes neuronales profundas es su capacidad para aprender y mejorar…

[fortunatelycoldengineer]

[govindhtech]

Agilex 3 FPGAs: Next-Gen Edge-To-Cloud Technology At Altera

Agilex 3 FPGA

Today, Altera, an Intel company, launched a line of FPGA hardware, software, and development tools to expand the market and use cases for its programmable solutions. Altera unveiled new development kits and software support for its Agilex 5 FPGAs at its annual developer’s conference, along with fresh information on its next-generation, cost-and power-optimized Agilex 3 FPGA.

Altera

Why It Matters

Altera is the sole independent provider of FPGAs, offering complete stack solutions designed for next-generation communications infrastructure, intelligent edge applications, and high-performance accelerated computing systems. Customers can get adaptable hardware from the company that quickly adjusts to shifting market demands brought about by the era of intelligent computing thanks to its extensive FPGA range. With Agilex FPGAs loaded with AI Tensor Blocks and the Altera FPGA AI Suite, which speeds up FPGA development for AI inference using well-liked frameworks like TensorFlow, PyTorch, and OpenVINO toolkit and tested FPGA development flows, Altera is leading the industry in the use of FPGAs in AI inference workload

Intel Agilex 3

What Agilex 3 FPGAs Offer

Designed to satisfy the power, performance, and size needs of embedded and intelligent edge applications, Altera today revealed additional product details for its Agilex 3 FPGA. Agilex 3 FPGAs, with densities ranging from 25K-135K logic elements, offer faster performance, improved security, and higher degrees of integration in a smaller box than its predecessors.

An on-chip twin Cortex A55 ARM hard processor subsystem with a programmable fabric enhanced with artificial intelligence capabilities is a feature of the FPGA family. Real-time computation for time-sensitive applications such as industrial Internet of Things (IoT) and driverless cars is made possible by the FPGA for intelligent edge applications. Agilex 3 FPGAs give sensors, drivers, actuators, and machine learning algorithms a smooth integration for smart factory automation technologies including robotics and machine vision.

Agilex 3 FPGAs provide numerous major security advancements over the previous generation, such as bitstream encryption, authentication, and physical anti-tamper detection, to fulfill the needs of both defense and commercial projects. Critical applications in industrial automation and other fields benefit from these capabilities, which guarantee dependable and secure performance.

Agilex 3 FPGAs offer a 1.9×1 boost in performance over the previous generation by utilizing Altera’s HyperFlex architecture. By extending the HyperFlex design to Agilex 3 FPGAs, high clock frequencies can be achieved in an FPGA that is optimized for both cost and power. Added support for LPDDR4X Memory and integrated high-speed transceivers capable of up to 12.5 Gbps allow for increased system performance.

Agilex 3 FPGA software support is scheduled to begin in Q1 2025, with development kits and production shipments following in the middle of the year.

How FPGA Software Tools Speed Market Entry

Quartus Prime Pro

The Latest Features of Altera’s Quartus Prime Pro software, which gives developers industry-leading compilation times, enhanced designer productivity, and expedited time-to-market, are another way that FPGA software tools accelerate time-to-market. With the impending Quartus Prime Pro 24.3 release, enhanced support for embedded applications and access to additional Agilex devices are made possible.

Agilex 5 FPGA D-series, which targets an even wider range of use cases than Agilex 5 FPGA E-series, which are optimized to enable efficient computing in edge applications, can be designed by customers using this forthcoming release. In order to help lower entry barriers for its mid-range FPGA family, Altera provides software support for its Agilex 5 FPGA E-series through a free license in the Quartus Prime Software.

Support for embedded applications that use Altera’s RISC-V solution, the Nios V soft-core processor that may be instantiated in the FPGA fabric, or an integrated hard-processor subsystem is also included in this software release. Agilex 5 FPGA design examples that highlight Nios V features like lockstep, complete ECC, and branch prediction are now available to customers. The most recent versions of Linux, VxWorks, and Zephyr provide new OS and RTOS support for the Agilex 5 SoC FPGA-based hard processor subsystem.

How to Begin for Developers

In addition to the extensive range of Agilex 5 and Agilex 7 FPGAs-based solutions available to assist developers in getting started, Altera and its ecosystem partners announced the release of 11 additional Agilex 5 FPGA-based development kits and system-on-modules (SoMs).

Developers may quickly transition to full-volume production, gain firsthand knowledge of the features and advantages Agilex FPGAs can offer, and easily and affordably access Altera hardware with FPGA development kits.

Kits are available for a wide range of application cases and all geographical locations. To find out how to buy, go to Altera’s Partner Showcase website.

Read more on govindhtech.com

[warrenwoodhouse]

Google TensorFlow

[mitsde123]

Top Machine Learning Frameworks to Watch in 2024: TensorFlow, PyTorch, and Beyond

As machine learning continues to revolutionize industries, choosing the right framework is crucial for building robust, scalable, and efficient models. In 2024, several machine learning frameworks are leading the pack, each with unique features and capabilities that cater to different needs. This blog explores the top frameworks, including TensorFlow, PyTorch, and others, and how they compare to one another. Additionally, we’ll discuss how the MIT School of Distance Education (MITSDE) can help you master these frameworks through their comprehensive courses.

[surajheroblog]

TensorFlow Mastery: Build Cutting-Edge AI Models

In the realm of artificial intelligence and machine learning, TensorFlow stands out as one of the most powerful and widely-used frameworks. Developed by Google, TensorFlow provides a comprehensive ecosystem for building and deploying machine learning models. For those looking to master this technology, a well-structured TensorFlow course for deep learning can be a game-changer. In this blog post, we will explore the benefits of mastering TensorFlow, the key components of a TensorFlow course for deep learning, and how it can help you build cutting-edge AI models. Whether you are a beginner or an experienced practitioner, this guide will provide valuable insights into the world of TensorFlow.

1. Understanding TensorFlow

1.1 What is TensorFlow?

TensorFlow is an open-source machine learning framework that allows developers to build and deploy machine learning models with ease. It provides a flexible and comprehensive ecosystem that includes tools, libraries, and community resources. TensorFlow supports a wide range of tasks, from simple linear regression to complex deep learning models. This versatility makes it an essential tool for anyone looking to delve into the world of AI.

1.2 Why Choose TensorFlow?

There are several reasons why TensorFlow is a popular choice among data scientists and AI practitioners. Firstly, it offers a high level of flexibility, allowing users to build custom models tailored to their specific needs. Secondly, TensorFlow’s extensive documentation and community support make it accessible to both beginners and experts. Lastly, TensorFlow’s integration with other Google products, such as TensorFlow Extended (TFX) and TensorFlow Lite, provides a seamless workflow for deploying models in production environments.

2. Key Components of a TensorFlow Course for Deep Learning

2.1 Introduction to Deep Learning

A comprehensive TensorFlow course for deep learning typically begins with an introduction to deep learning concepts. This includes understanding neural networks, activation functions, and the basics of forward and backward propagation. By grasping these foundational concepts, learners can build a solid base for more advanced topics.

2.2 Building Neural Networks with TensorFlow

The next step in a TensorFlow course for deep learning is learning how to build neural networks using TensorFlow. This involves understanding TensorFlow’s core components, such as tensors, operations, and computational graphs. Learners will also explore how to create and train neural networks using TensorFlow’s high-level APIs, such as Keras.

2.3 Advanced Deep Learning Techniques

As learners progress through the TensorFlow course for deep learning, they will encounter more advanced techniques. This includes topics such as convolutional neural networks (CNNs) for image recognition, recurrent neural networks (RNNs) for sequence data, and generative adversarial networks (GANs) for generating new data. These advanced techniques enable learners to tackle complex AI challenges and build cutting-edge models.

2.4 Model Optimization and Deployment

A crucial aspect of any TensorFlow course for deep learning is learning how to optimize and deploy models. This includes techniques for hyperparameter tuning, regularization, and model evaluation. Additionally, learners will explore how to deploy models using TensorFlow Serving, TensorFlow Lite, and TensorFlow.js. These deployment tools ensure that models can be efficiently integrated into real-world applications.

3. Practical Applications of TensorFlow

3.1 Computer Vision

One of the most popular applications of TensorFlow is in the field of computer vision. By leveraging TensorFlow’s powerful libraries, developers can build models for image classification, object detection, and image segmentation. A TensorFlow course for deep learning will typically include hands-on projects that allow learners to apply these techniques to real-world datasets.

3.2 Natural Language Processing

Another key application of TensorFlow is in natural language processing (NLP). TensorFlow provides tools for building models that can understand and generate human language. This includes tasks such as sentiment analysis, language translation, and text generation. By mastering TensorFlow, learners can develop sophisticated NLP models that can be used in various applications, from chatbots to language translation services.

3.3 Reinforcement Learning

Reinforcement learning is a branch of machine learning that focuses on training agents to make decisions by interacting with their environment. TensorFlow provides a robust framework for building and training reinforcement learning models. A TensorFlow course for deep learning will often cover the basics of reinforcement learning and provide practical examples of how to implement these models using TensorFlow.

4. Benefits of Mastering TensorFlow

4.1 Career Advancement

Mastering TensorFlow can significantly enhance your career prospects. As one of the most widely-used machine learning frameworks, TensorFlow skills are in high demand across various industries. By completing a TensorFlow course for deep learning, you can demonstrate your expertise and open up new career opportunities in AI and machine learning.

4.2 Personal Growth

Beyond career advancement, mastering TensorFlow offers personal growth and intellectual satisfaction. The ability to build and deploy cutting-edge AI models allows you to tackle complex problems and contribute to innovative solutions. Whether you are working on personal projects or collaborating with a team, TensorFlow provides the tools and resources needed to bring your ideas to life.

4.3 Community and Support

One of the key benefits of learning TensorFlow is the vibrant community and support network. TensorFlow’s extensive documentation, tutorials, and community forums provide valuable resources for learners at all levels. By engaging with the TensorFlow community, you can gain insights, share knowledge, and collaborate with other AI enthusiasts.

Conclusion

In conclusion, mastering TensorFlow through a well-structured TensorFlow course for deep learning can open up a world of possibilities in the field of artificial intelligence. From understanding the basics of neural networks to building and deploying advanced models, a comprehensive course provides the knowledge and skills needed to excel in AI. This deep dive into TensorFlow not only enhances your career prospects but also offers personal growth and intellectual satisfaction.

[feitgemel]

This tutorial provides a step-by-step guide on how to implement and train a Res-UNet model for skin Melanoma detection and segmentation using TensorFlow and Keras.

What You'll Learn :

- Building Res-Unet model : Learn how to construct the model using TensorFlow and Keras.

- Model Training: We'll guide you through the training process, optimizing your model to distinguish Melanoma from non-Melanoma skin lesions.

- Testing and Evaluation: Run the pre-trained model on a new fresh images .

Explore how to generate masks that highlight Melanoma regions within the images.

Visualizing Results: See the results in real-time as we compare predicted masks with actual ground truth masks.

You can find more tutorials, and join my newsletter here : https://eranfeit.net/

Check out our tutorial here : https://youtu.be/5inxPSZz7no&list=UULFTiWJJhaH6BviSWKLJUM9sg

Enjoy

Eran

#Python #openCV #TensorFlow #Deeplearning #DeepLearningTutorial #Unet #Resunet #MachineLearningProject #Segmentation

[swiftnliftnewsandarticle]

What skills are needed for machine learning jobs?

Machine learning jobs typically require a diverse set of skills, including:

1.Programming Languages: Working with data and implementing machine learning algorithms require proficiency in languages like Python, R, and occasionally Java or C++.

2. Mathematics and Statistics: To comprehend and create machine learning models, one must have a solid foundation in linear algebra, calculus, probability, and statistics.

3. Machine Learning Algorithms: Knowledge of various machine learning algorithms and techniques, including supervised and unsupervised learning, neural networks, and reinforcement learning.

4. Data Handling: To prepare data for analysis, one must be proficient in the preparation, cleaning, and manipulation of data using tools and libraries such as Pandas, NumPy, and SQL.

5. Data visualization: The capacity to analyze model performance and insights by visualizing data and results using tools and frameworks like Matplotlib, Seaborn, or Tableau.

6. Knowledge in ML Frameworks: Proficiency in building and training models using machine learning frameworks and libraries such as TensorFlow, PyTorch, Keras, or Scikit-learn.

7. Big Data Technologies: Proficiency in handling and processing massive datasets through the use of big data tools and platforms such as Hadoop, Spark, or cloud-based services (such AWS, Google Cloud, and Azure).

8. Software Engineering Skills: Competence in software development practices, including version control (e.g., Git), debugging, and writing clean, maintainable code. Strong problem-solving abilities and the capacity to approach challenging issues analytically in order to provide useful insights are required for analytical thinking and problem-solving.

9. Domain Knowledge: Developing appropriate models and comprehending context can be aided by having knowledge of the particular area or industry in which machine learning is being applied.

These skills combine to enable professionals to build, deploy, and refine machine learning models that drive data-driven decision-making and innovation.

[ingoampt]

Day 17 _ Hyperparameter Tuning with Keras Tuner

Hyperparameter Tuning with Keras Tuner A Comprehensive Guide to Hyperparameter Tuning with Keras Tuner Introduction In the world of machine learning, the performance of your model can heavily depend on the choice of hyperparameters. Hyperparameter tuning, the process of finding the optimal settings for these parameters, can be time-consuming and complex. Keras Tuner is a powerful library that…

[bullzeyemedia]

Unveiling Gemma 2: Google’s Breakthrough AI Model

The Google has released Gemma 2, AI model that will revolutionalize the field for researcher or developers. This new model is very powerful and efficient compared to the previous models; therefore, is a big leap to AI technology. Key Features: 1. Advanced Architecture: Perfected for performance With great design. 2. Exceptional Efficiency: Runs on a single NVIDIA H100 Tensor Core GPU host or a TPU host. 3. Cost-Effective: A high performing AI becomes more affordable when the deployment costs are low and goes on to benefit more consumers. Gemma 2 also works as a well-interpretable interface with the leading AI libraries such as Hugging Face, PyTorch, and TensorFlow to support the multiform compatibility. It is specifically optimized to run well on systems as diverse as pure desktop systems through to top-end cloud solutions. Thus, the firm ensures safe AI practices repeating detailed safety measures and shareable metrics in automating Gemma 2. This model is on Google AI Studio, Kaggle, and Hugging Face Models and has further assistance for new and academic users.

Read more: Google’s Breakthrough AI Model For Researchers And Developers

[govindhtech]

Federated Learning & AI Help In Hospital’s Cancer Detection

Medical Facilities Use Federated Learning and AI to Improve Cancer Detection. Using NVIDIA-powered Federated learning, a panel of experts from leading research institutions and medical facilities in the United States is assessing the effectiveness of federated learning and AI-assisted annotation in training AI models for tumor segmentation.

What Is Federated Learning?

A method for creating more precise, broadly applicable AI models that are trained on data from several data sources without compromising data security or privacy is called Federated learning. It enables cooperation between several enterprises on the creation of an AI model without allowing sensitive data to ever leave their systems.

“The only feasible way to stay ahead is to use Federated learning to create and test models at numerous locations simultaneously. It is a really useful tool.

The team, comprising collaborators from various universities such as Case Western, Georgetown, Mayo Clinic, University of California, San Diego, University of Florida, and Vanderbilt University, utilized NVIDIA FLARE (NVFlare), an open-source framework featuring strong security features, sophisticated privacy protection methods, and an adaptable system architecture, to assist with their most recent project.

The committee was given four NVIDIA RTX A5000 GPUs via the NVIDIA Academic Grant Program, and they were dispersed throughout the collaborating research institutions so that they could configure their workstations for Federated learning. Further collaborations demonstrated NVFLare’s adaptability by using NVIDIA GPUs in on-premises servers and cloud environments.

Federated Learning AI

Remote Multi-Party Cooperation

Federated learning reduces the danger of jeopardizing data security or privacy while enabling the development and validation of more precise and broadly applicable AI models from a variety of data sources. It makes it possible to create AI models using a group of data sources without the data ever leaving the specific location.

Features

Algorithms Preserving Privacy

With the help of privacy-preserving techniques from NVIDIA FLARE, every modification to the global model is kept secret and the server is unable to reverse-engineer the weights that users input or find any training data.

Workflows for Training and Evaluation

Learning algorithms for FedAvg, FedOpt, and FedProx are among the integrated workflow paradigms that leverage local and decentralized data to maintain the relevance of models at the edge.

Wide-ranging Management Instruments

Management tools provide orchestration via an admin portal, safe provisioning via SSL certificates, and visualization of Federated learning experiments using TensorBoard.

Accommodates Well-Known ML/DL Frameworks

Federated learning may be integrated into your present workflow with the help of the SDK, which has an adaptable architecture and works with PyTorch, Tensorflow, and even Numpy.

Wide-ranging API

Researchers may create novel federated workflow techniques, creative learning, and privacy-preserving algorithms thanks to its comprehensive and open-source API.

Reusable Construction Pieces

NVIDIA FLARE offers a reusable building element and example walkthrough that make it simple to conduct Federated learning experiments.

Breaking Federated Learning’s Code

For the initiative, which focused on renal cell carcinoma, a kind of kidney cancer, data from around fifty medical imaging investigations were submitted by each of the six collaborating medical institutes. An initial global model transmits model parameters to client servers in a Federated learning architecture. These parameters are used by each server to configure a localized version of the model that has been trained using the company’s confidential data.

Subsequently, the global model receives updated parameters from each of the local models, which are combined to create a new global model. Until the model’s predictions no longer become better with each training round, the cycle is repeated. In order to optimize for training speed, accuracy, and the quantity of imaging studies needed to train the model to the requisite degree of precision, the team experimented with model topologies and hyperparameters.

NVIDIA MONAI-Assisted AI-Assisted Annotation

The model’s training set was manually labeled during the project’s first phase. The team’s next step is using NVIDIA MONAI for AI-assisted annotation to assess the performance of the model with training data segmented using AI vs conventional annotation techniques.

“Federated learning activities are most difficult when data is not homogeneous across places. Individuals just label their data differently, utilize various imaging equipment, and follow different processes, according to Garrett. “It’s aim to determine whether adding MONAI to the Federated learning model during its second training improves overall annotation accuracy.”

The group is making use of MONAI Label, an image-labeling tool that cuts down on the time and effort required to produce new datasets by allowing users to design unique AI annotation applications. Prior to being utilized for model training, the segmentations produced by AI will be verified and improved by experts. Flywheel, a top medical imaging data and AI platform that has included NVIDIA MONAI into its services, hosts the data for both the human and AI-assisted annotation stages.

NVIDIA FLARE

The open-source, flexible, and domain-neutral NVIDIA Federated Learning Application Runtime Environment (NVIDIA FLARE) SDK is designed for Federated learning. Platform developers may use it to provide a safe, private solution for a dispersed multi-party cooperation, and academics and data scientists can modify current ML/DL process to a federated paradigm.

Maintaining Privacy in Multi-Party Collaboration

Create and verify more precise and broadly applicable AI models from a variety of data sources while reducing the possibility that data security and privacy may be jeopardized by including privacy-preserving algorithms and workflow techniques.

Quicken Research on AI

enables data scientists and researchers to modify the current ML/DL process (PyTorch, RAPIDS, Nemo, TensorFlow) to fit into a Federated learning model.

Open-Source Structure

A general-purpose, cross-domain Federated learning SDK with the goal of establishing a data science, research, and developer community.

Read more on govindhtech.com

[edutech-brijesh]

Python has numerous powerful data science libraries that enable efficient data analysis, visualization, and machine learning. Some popular ones include NumPy, Pandas, Matplotlib, Scikit-learn, and TensorFlow.