Duboko učenje Training in Srbija

Ova instruktorska, uživo obuka u Srbija (onlajn ili na lokaciji) namenjena je programerima, naučnicima podataka i AI praktičarima srednjeg nivoa znanja koji žele da iskoriste TensorFlow Lite za Edge AI aplikacije.

Na kraju ove obuke, učesnici će moći da:

• Razumeju osnove TensorFlow Litea i njegovu ulogu u Edge AI-u.
• Razviju i optimizuju AI modele pomoću TensorFlow Litea.
• Implementiraju TensorFlow Lite modele na različite edge uređaje.
• Korišćenje alata i tehnika za konverziju i optimizaciju modela.
• Implementiraju praktične Edge AI aplikacije koristeći TensorFlow Lite.

Ova obuka sa trenerom uživo na temu Srbija (online ili na lokaciji) namenjena je stručnjacima naprednog nivoa koji žele da prodube razumevanje računarske vizije i istraže mogućnosti Tenzora u razvoju sofisticiranih vizuelnih modela pomoću Google Colab.

Nakon završetka ove obuke, učesnici će moći da:

• Izgrade i obuku konvolucione neuronske mreže (CNN) koristeći TensorFlow.
• Izveste Google Colab za skalabilni i efikasan razvoj modela u oblaku.
• Implementiraju tehnike preobrade slika za zadatke iz računarske vizije.
• Implementiraju modele računarske vizije za primenu u stvarnom svetu.
• Koriste učenje prenosa (transfer learning) za poboljšavanje performansi CNN modela.
• Vizuelizuju i tumače rezultate modela za klasifikaciju slika.

Ovaj vodja-led trening uživo u Srbija (online ili uživo u učionici) namenjen je naučnicima za podatke i razvojima srednjeg nivoa znanja koji žele da razumeju i primenjuju tehnike dubokog učenja koristeći okruženje Google Colab.

Na kraju ovog treninga, učesnici će moći da:

• podesite i navigirate Google Colab-om za projekte dubokog učenja.
• razumeju temelje neuralnih mreža.
• implementiraju modele dubokog učenja koristeći TensorFlow.
• obuku i procene modele dubokog učenja.
• iskoriste napredne funkcionalnosti TensorFlow-a za duboko učenje.

This instructor-led, live training in Srbija (online or onsite) targets advanced-level professionals seeking to specialize in cutting-edge deep learning techniques for NLU.

By the end of this training, participants will be able to:

• Understand the key differences between NLU and NLP models.
• Apply advanced deep learning techniques to NLU tasks.
• Explore deep architectures such as transformers and attention mechanisms.
• Leverage future trends in NLU for building sophisticated AI systems.

Delivered as an instructor-led, live training in Srbija (online or onsite), this course is designed for advanced professionals who wish to explore cutting-edge XAI techniques for deep learning models, focusing on the development of interpretable AI systems.

By the end of this training, participants will be able to:

• Understand the challenges of explainability in deep learning.
• Implement advanced XAI techniques for neural networks.
• Interpret decisions made by deep learning models.
• Evaluate the trade-offs between performance and transparency.

This instructor-led, live training in Srbija (online or onsite) is designed for intermediate to advanced data scientists, machine learning engineers, deep learning researchers, and computer vision specialists aiming to broaden their knowledge and skills in applying deep learning to text-to-image generation.

By the conclusion of this training, participants will be able to:

• Analyze advanced deep learning architectures and techniques for text-to-image generation.
• Build complex models and apply optimizations for superior image synthesis.
• Improve performance and scalability for large datasets and complex models.
• Optimize hyperparameters to enhance model performance and generalization.
• Incorporate Stable Diffusion into other deep learning frameworks and tools

This instructor-led, live training in Srbija (online or onsite) is tailored for advanced professionals who aim to leverage AI techniques to revolutionize drug discovery and development processes.

By the end of this training, participants will be able to:

• Understand the role of AI in drug discovery and development.
• Apply machine learning techniques to predict molecular properties and interactions.
• Use deep learning models for virtual screening and lead optimization.
• Integrate AI-driven approaches into the clinical trial process.

Ovo instruktorsko, uživo obučavanje u Srbija (onlajn ili na lokaciji) namenjeno je biolozima koji žele da razumeju kako AlphaFold funkcioniše i da koriste AlphaFold modele kao smernice u svojim eksperimentalnim studijama.

Krajem ovog obučavanja, učesnici će moći da:

• Razumeju osnovna načela AlphaFold-a.
• Nauče kako AlphaFold funkcioniše.
• Nauče kako da interpretiraju predikcije i rezultate AlphaFold-a.

This instructor-led, live training in Srbija (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use Large Language Models for various natural language tasks.

By the end of this training, participants will be able to:

• Set up a development environment that includes a popular LLM.
• Create a basic LLM and fine-tune it on a custom dataset.
• Use LLMs for different natural language tasks such as text summarization, question answering, text generation, and more.
• Debug and evaluate LLMs using tools such as TensorBoard, PyTorch Lightning, and Hugging Face Datasets.

This instructor-led, live training (online or onsite) is aimed at data scientists, machine learning engineers, and computer vision researchers who wish to leverage Stable Diffusion to generate high-quality images for a variety of use cases.

By the end of this training, participants will be able to:

• Understand the principles of Stable Diffusion and how it works for image generation.
• Build and train Stable Diffusion models for image generation tasks.
• Apply Stable Diffusion to various image generation scenarios, such as inpainting, outpainting, and image-to-image translation.
• Optimize the performance and stability of Stable Diffusion models.

In this live, instructor-led training in Srbija, participants will explore the most current and advanced machine learning techniques in Python by developing a series of demonstration applications that utilize image, audio, text, and financial data.

Upon completion of this training, participants will be able to:

• Implement machine learning algorithms and techniques to address complex problems.
• Apply deep learning and semi-supervised learning methods to applications involving images, music, text, and financial data.
• Maximize the potential of Python algorithms.
• Utilize libraries and packages such as NumPy and Theano.

Ova obuka sa instruktorem uživo <lok> (online ili uživo) pruža uvid u oblast prepoznavanja obrazaca i mašinskog učenja. Bavi se praktičnim primenama u statistici, računarskoj nauci, obradi signala, računarskom videu, rudarenju podataka i bioinformatici.

Krajev ove obuke, učesnici će moći da:

• Primenjuju ključne statističke metode u prepoznavanju obrazaca.
• Koriste ključne modele, kao što su neuronetske mreže i kernel metode, za analizu podataka.
• Primjenjuju napredne tehnike za rešavanje složenih problema.
• Poboljšaju tačnost predviđanja kombinovanjem različitih modela.

This guided, live training in Srbija (online or onsite) is designed for software developers, data analysts, and technical professionals who wish to use TensorFlow 2.x and Keras to build, train, and deploy deep learning models for computer vision, natural language processing, and multimodal applications.

This instructor-led live training in Srbija (online or on-site) is designed for data scientists who want to use TensorFlow to analyze potential fraud data.

By the end of this training, participants will be able to:

• Create a fraud detection model in Python and TensorFlow.
• Build linear regressions and linear regression models to predict fraud.
• Develop an end-to-end AI application for analyzing fraud data.

U ovoj uživoj obuci sa instruktorom, učesnici će naučiti kako da koriste Matlab za dizajniranje, izgradnju i vizualizaciju konvolucione neuronske mreže za prepoznavanje slika.

Kojoj završetku ove obuke, učesnici će moći da:

• Izgrade model dubokog učenja
• Automatizuju označavanje podataka
• Rade sa modelima iz Caffe i TensorFlow-Keras okruženja
• Obučavaju podatke koristeći više GPU-ova, cloud okruženja ili klastera

Ciljna grupa

• Programeri
• Inženjeri
• Eksperti iz određenih domena

Format kursa

• Deo predavanja, deo diskusije, vežbe i intenzivna praktična vežba

Ova obuka uživo sa trenerom u Srbija (online ili na lokaciji) namenjena je programerima i naučnicima podataka koji žele da koriste TensorFlow 2.x za izgradnju prediktora, klasifikatora, generativnih modela, neuronskih mreža i slično.

Krajem ove obuke, učesnici će biti u mogućnosti da:

• Instaliraju i konfigurisuju TensorFlow 2.x.
• Razumeju prednosti TensorFlow 2.x u odnosu na prethodne verzije.
• Izgrade modele za duboko učenje.
• Implementiraju napredni klasifikator slika.
• Postave model za duboko učenje u oblaku, na mobilne uređaje i IoT uređaje.