{"id":238505,"date":"2026-04-08T15:04:47","date_gmt":"2026-04-08T13:04:47","guid":{"rendered":"https:\/\/www.itta.net\/?post_type=formations&#038;p=238505"},"modified":"2026-04-12T18:59:04","modified_gmt":"2026-04-12T16:59:04","slug":"deep-learning-tensorflow-neural-networks","status":"publish","type":"formations","link":"https:\/\/www.itta.net\/en\/trainings\/artificial-intelligence\/deep-learning-with-tensorflow-and-neural-networks\/","title":{"rendered":"Deep Learning with TensorFlow and Neural Networks"},"content":{"rendered":"\r\n<p class=\"wp-block-paragraph\">The Deep Learning with TensorFlow and Neural Networks training course enables participants to take their understanding of advanced artificial intelligence approaches to the next level. It helps them discover the fundamental concepts behind neural networks, deep learning and using a framework like TensorFlow for learning, modeling and implementation purposes.<\/p>\r\n\r\n\r\n\r\n<p><strong>An advanced training course for a deeper understanding of deep learning concepts<\/strong><\/p>\r\n\r\n\r\n\r\n<p class=\"wp-block-paragraph\">During this course, participants discover the key concepts of deep learning, the role of neuron layers, learning mechanisms, the main use cases and the conditions for implementation. The training provides a structured overview of the subject and prepares participants for further technical skill development on specialized environments and frameworks.<\/p>\r\n","protected":false},"featured_media":0,"parent":0,"menu_order":0,"template":"","editeurs":[1517],"domaine":[2982,4250],"class_list":["post-238505","formations","type-formations","status-publish","hentry","editeurs-tensorflow-open-source","domaine-artificial-intelligence","domaine-data-science-and-applied-ai"],"acf":{"reference":"AI-05-06","duree_nombre":1,"duree_unite":"jour","prix_virtuel":750,"prix_presentiel":800,"prix_blended":"","prix_elearning":"","views":83,"niveau":["Avanc\u00e9"],"role":[],"certifiant":"","pdus":"","lieux":["Gen\u00e8ve","Lausanne"],"formats_dapprentissage":["presentiel","virtuel"],"examen_inclus":"Non certifiant","garanti":[],"financement":["Temptraining"],"description_temptraining":"","description_caf":"","description_title":"Deep Learning with TensorFlow and Neural Networks Training","objectifs":"<ul>\r\n \t<li>Understand the principles of neural networks<\/li>\r\n \t<li>Discover the fundamentals of deep learning<\/li>\r\n \t<li>Identify the main use cases for deep learning<\/li>\r\n \t<li>Understand the role of TensorFlow in model implementation<\/li>\r\n \t<li>Connect theoretical concepts to practical applications<\/li>\r\n \t<li>Prepare for progression toward more technical or hands-on approaches<\/li>\r\n<\/ul>","connaissances":"<ul>\r\n \t<li>A solid understanding of machine learning is recommended<\/li>\r\n \t<li>A general background in data and AI is desirable<\/li>\r\n<\/ul>","profils_participants":"<ul>\r\n \t<li>IT professionals<\/li>\r\n \t<li>Technical project managers<\/li>\r\n \t<li>Data professionals<\/li>\r\n \t<li>Innovation managers<\/li>\r\n \t<li>Anyone with a strong data or machine learning background who wants to understand the fundamentals of deep learning with TensorFlow<\/li>\r\n<\/ul>","examen":"","documentation":"<ul>\n \t<li>Support de cours num\u00e9rique inclus<\/li>\n<\/ul>","lab__exercices":"<ul>\n \t<li>This course includes conceptual understanding workshops, case studies and analytical exercises to identify how deep learning and neural networks work, their use cases and key points of attention.<\/li>\n<\/ul>","contenu_cours":"<strong>Module 1: Introduction to deep learning and neural networks<\/strong>\r\n<ul>\r\n \t<li>Definition of deep learning<\/li>\r\n \t<li>Difference between traditional machine learning and deep learning<\/li>\r\n \t<li>Understanding the logic of neural networks<\/li>\r\n \t<li>Identifying typical use cases<\/li>\r\n<\/ul>\r\n<strong>Module 2: Understanding how a neural network works<\/strong>\r\n<ul>\r\n \t<li>Inputs<\/li>\r\n \t<li>Hidden layers<\/li>\r\n \t<li>Outputs<\/li>\r\n \t<li>Weights<\/li>\r\n \t<li>Learning<\/li>\r\n \t<li>Propagation<\/li>\r\n \t<li>Understanding simply how a network adjusts its parameters<\/li>\r\n<\/ul>\r\n<strong>Module 3: The main applications of deep learning<\/strong>\r\n<ul>\r\n \t<li>Computer vision<\/li>\r\n \t<li>Natural language processing<\/li>\r\n \t<li>Pattern recognition<\/li>\r\n \t<li>Forecasting<\/li>\r\n \t<li>Complex classification<\/li>\r\n \t<li>Identifying the contexts in which deep learning becomes relevant<\/li>\r\n<\/ul>\r\n<strong>Module 4: Discovering TensorFlow<\/strong>\r\n<ul>\r\n \t<li>TensorFlow's position in the AI ecosystem<\/li>\r\n \t<li>Role of a deep learning framework<\/li>\r\n \t<li>Understanding what it enables<\/li>\r\n \t<li>Connecting the framework to the modeling and experimentation workflow<\/li>\r\n<\/ul>\r\n<strong>Module 5: Limitations, constraints and caution<\/strong>\r\n<ul>\r\n \t<li>Data requirements<\/li>\r\n \t<li>Training time<\/li>\r\n \t<li>Implementation complexity<\/li>\r\n \t<li>Interpretability<\/li>\r\n \t<li>Computing cost<\/li>\r\n \t<li>The need for rigorous project scoping<\/li>\r\n<\/ul>\r\n<strong>Module 6: Preparing for further skill development<\/strong>\r\n<ul>\r\n \t<li>When to move toward deep learning<\/li>\r\n \t<li>How to choose a good use case<\/li>\r\n \t<li>What skills to mobilize<\/li>\r\n \t<li>How to progress toward more technical environments<\/li>\r\n<\/ul>","cours_recommandes":[238504,238503,238506,238511,238512],"infos_additionnelles":"<h3>TensorFlow, the reference framework for deep learning<\/h3>\r\n<p>TensorFlow, developed by Google, is one of the most widely used deep learning frameworks in the world. It offers a complete ecosystem for designing, training and deploying neural networks, from the simplest models to the most complex architectures. Its maturity and community make it a reliable choice for enterprise projects.<\/p>\r\n<p>Mastering TensorFlow opens up considerable career opportunities in a market where demand for deep learning skills far exceeds supply.<\/p>\r\n\r\n<h3>Understanding neural networks<\/h3>\r\n<p>Artificial neural networks draw inspiration from the human brain to process information. Each neuron receives inputs, applies weights and produces an output. Stacking layers of neurons enables the processing of increasingly complex problems, from simple classifiers to the most advanced language models.<\/p>\r\n<p>Network architectures vary according to the application. Convolutional networks excel at image processing, recurrent networks at sequence processing and transformers at natural language processing. Understanding these architectures helps choose the right approach for each problem.<\/p>\r\n\r\n<h3>Developing with TensorFlow and Keras<\/h3>\r\n<p>Keras, integrated into TensorFlow, provides a high-level API that simplifies the construction of neural networks. Defining a model, configuring training and evaluating results can be done in just a few lines of code. This accessibility allows developers to focus on model architecture rather than implementation details.<\/p>\r\n<p>TensorFlow also offers advanced features for experienced users, such as eager mode for debugging, distribution strategies for distributed training and TensorFlow Serving for production deployment.<\/p>\r\n\r\n<h3>Practical applications of deep learning<\/h3>\r\n<p>Deep learning has applications across many domains. Image classification, object detection, speech recognition, automatic translation, text generation and recommendation are common enterprise use cases.<\/p>\r\n<p>Pre-trained models and transfer learning deliver strong results even with limited datasets. This approach significantly reduces the cost and development time of deep learning solutions.<\/p>\r\n\r\n<h3>From prototype to production deployment<\/h3>\r\n<p>Moving from prototype to production is a major challenge in deep learning. TensorFlow provides tools for every stage: TensorFlow Lite for mobile deployment, TensorFlow.js for the browser and TensorFlow Serving for production APIs. MLOps pipelines with TFX automate the entire lifecycle.<\/p>\r\n<p>ITTA offers this advanced technical training in Geneva and Lausanne for developers and engineers in French-speaking Switzerland who want to master deep learning with TensorFlow and build production-quality AI solutions.<\/p>\r\n\r\n<h3>AI-augmented data analysis in the Swiss context<\/h3>\r\n<p>Switzerland is a country where data culture is particularly well developed, especially in the finance, healthcare and manufacturing sectors. Artificial intelligence makes it possible to extract even more value from this data by making it accessible to a wider audience within organizations. Augmented analytics tools democratize access to data insights and enable every team member to contribute to informed decision-making.<\/p>\r\n<p>Swiss requirements for data quality, analytical rigor and privacy protection create a demanding but beneficial framework for AI deployment. Organizations that train their teams in AI-driven data exploitation gain a lasting competitive advantage. The skills acquired are cross-functional and applicable regardless of the industry or company size.<\/p>\r\n\r\n<p><strong>Is Python knowledge required to learn TensorFlow?<\/strong><\/p>\r\n<p>Yes, Python is TensorFlow's primary language. A command of Python basics and scientific libraries such as NumPy is recommended. Participants without Python experience can take a preliminary Python training course.<\/p>\r\n\r\n<p><strong>TensorFlow or PyTorch: which one to choose?<\/strong><\/p>\r\n<p>Both frameworks are excellent. TensorFlow excels in production deployment and its ecosystem of tools. PyTorch is often preferred for research and rapid prototyping. The TensorFlow training provides foundations that are transferable to PyTorch.<\/p>\r\n\r\n<p><strong>Is a GPU required for this training?<\/strong><\/p>\r\n<p>Training exercises use cloud environments such as Google Colab, which provide free access to GPUs. No specific hardware is required from participants.<\/p>\r\n\r\n<p><strong>Is deep learning suitable for every problem?<\/strong><\/p>\r\n<p>No, deep learning excels with unstructured data and complex problems but can be outperformed by simpler approaches on structured tabular data. The training teaches when to use deep learning and when to prefer alternatives.<\/p>\r\n\r\n<p><strong>What career opportunities does TensorFlow proficiency offer?<\/strong><\/p>\r\n<p>Deep learning and TensorFlow skills are in high demand across the technology, finance, healthcare and manufacturing sectors. ML Engineer, Data Scientist and AI Developer positions are among the most sought-after roles on the Swiss job market.<\/p>","inclus_dans_ce_cours_virtuel":"<ul>\n \t<li>Training provided by a domain expert<\/li>\n \t<li>Digital documentation and support materials<\/li>\n \t<li>Achievement badge<\/li>\n<\/ul>","inclus_dans_ce_cours_presentiel":"<ul>\n \t<li>Training provided by a domain expert<\/li>\n \t<li>Digital documentation and support materials<\/li>\n \t<li>Achievement badge<\/li>\n<\/ul>","inclus_dans_ce_cours_blended":"","inclus_dans_ce_cours_elearning":"","inclus_dans_ce_cours_surmesure":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.5 (Yoast SEO v27.5) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Deep Learning TensorFlow Training | ITTA<\/title>\n<meta name=\"description\" content=\"Deep Learning TensorFlow training in Geneva and Lausanne. 1-day course on neural networks and deep learning with TensorFlow. 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