{"id":14845,"date":"2022-12-29T08:28:38","date_gmt":"2022-12-29T07:28:38","guid":{"rendered":"https:\/\/www.itta.net\/formations\/deep-learning-with-tensorflow-20\/"},"modified":"2025-09-15T14:52:27","modified_gmt":"2025-09-15T12:52:27","slug":"deep-learning-with-tensorflow-20","status":"publish","type":"formations","link":"https:\/\/www.itta.net\/en\/trainings\/development\/data-science\/deep-learning-with-tensorflow-20\/","title":{"rendered":"Deep Learning with TensorFlow 2.0"},"content":{"rendered":"<p data-start=\"0\" data-end=\"392\">TensorFlow is a complete open-source platform for machine learning. The course leverages the Keras API to write clean, fast code. You apply best practices from day one. You avoid common pitfalls in AI projects. You design, train, and evaluate robust models. You set up a reliable, reproducible environment. You validate your choices with meaningful metrics. You make better decisions, faster.<\/p>\n<h3 data-start=\"394\" data-end=\"417\">Skills and approach<\/h3>\n<p data-start=\"419\" data-end=\"844\" data-is-last-node=\"\" data-is-only-node=\"\">This training focuses on TensorFlow 2.0 and the Keras API. The goal is autonomy on real-world cases. You leave with a clear, reusable framework. You strengthen your Deep Learning foundations. By the end of the course, you can move from idea to model. You structure your experiments rigorously. You accelerate iteration and safeguard quality. The TensorFlow 2.0 course prepares you to deliver under real production conditions.<\/p>\n","protected":false},"featured_media":0,"parent":0,"menu_order":0,"template":"","editeurs":[1503,1517],"domaine":[2823,1262],"class_list":["post-14845","formations","type-formations","status-publish","hentry","editeurs-open-source","editeurs-tensorflow-open-source","domaine-data-science","domaine-development"],"acf":{"reference":"TFL-01","duree_nombre":2,"duree_unite":"jour","prix_virtuel":1700,"prix_presentiel":1800,"prix_blended":"","prix_elearning":"","views":1683,"niveau":["Fondamental"],"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 2.0 Training","objectifs":"<ul>\r\n \t<li>Gain a Strong Understanding of TensorFlow - Google\u2019s Cutting-Edge Deep Learning Framework<\/li>\r\n \t<li>Build Deep Learning Algorithms from Scratch in Python Using NumPy and TensorFlow<\/li>\r\n \t<li>Set Yourself Apart with Hands-on Deep and Machine Learning Experience<\/li>\r\n \t<li>Grasp the Mathematics Behind Deep Learning Algorithms<\/li>\r\n \t<li>Understand Backpropagation, Stochastic Gradient Descent, Batching, Momentum, and Learning Rate Schedules<\/li>\r\n \t<li>Know the Ins and Outs of Underfitting, Overfitting, Training, Validation, Testing, Early Stopping, and Initialization<\/li>\r\n \t<li>Competently Carry Out Pre-Processing, Standardization, Normalization, and One-Hot Encoding<\/li>\r\n<\/ul>","connaissances":"<ul>\r\n \t<li>Some basic Python programming skills<\/li>\r\n<\/ul>","profils_participants":"<ul> <li>Aspiring data scientists<\/li> <li>Python developers focused on AI<\/li> <li>Software engineers or entry-level MLOps<\/li> <li>Data analysts in career transition<\/li> <li>Computer science students<\/li> <\/ul>","examen":"","documentation":"<ul>\r\n \t<li>Digital courseware included<\/li>\r\n<\/ul>","lab__exercices":"<ul>\r\n<li>This course includes hands-on exercises designed to reinforce your knowledge and apply your skills in real-world professional scenarios.<\/li>\r\n<\/ul>","contenu_cours":"Module 1: Introduction to neural networks\r\n\r\nModule 2: Setting up the working environment\r\n\r\nModule 3: Minimal example - your first machine learning algorithm\r\n\r\nModule 4: TensorFlow - An introduction\r\n\r\nModule 5: Going deeper: Introduction to deep neural networks\r\n\r\nModule 6: Backpropagation. A peek into the Mathematics of Optimization\r\n\r\nModule 7: Overfitting\r\n\r\nModule 8: Initialization\r\n\r\nModule 9: Gradient descent and learning rates\r\n\r\nModule 10: Preprocessing","cours_recommandes":"","infos_additionnelles":"<h3>Why this approach makes the difference<\/h3>\r\nDeep Learning delivers real gains when the method follows. This course offers a clear and pragmatic approach. You start from a precise, measurable business need. You choose stable and relevant metrics. You then turn these goals into a simple, reproducible pipeline.\r\n\r\nWe connect theory to concrete decisions. You compare a linear baseline with a deep network. You justify the gap with numbers. You adopt an experimental mindset useful every day. This Deep Learning training helps you think like a practitioner.\r\n<h3>Frame the data properly from the start<\/h3>\r\nLabel quality often sets the performance ceiling. We cover data audits and simple checks. You detect leakage between training and test. You handle rare classes without distorting metrics. You adopt a clean and consistent split.\r\n\r\nYou standardize variables with rigor. You handle missing values without breaking distributions. You encode categories safely. You document every choice so it can be replayed. This discipline makes results comparable and credible.\r\n<h3>From notebook to reliable prototype with TensorFlow 2.0<\/h3>\r\nTensorFlow 2.0 simplifies the move from concept to prototype. You use eager execution to understand each step. You leverage the Keras API to structure your models. You set up useful, non-intrusive callbacks. You save at the right time and avoid losing progress.\r\n\r\nYou use tf.data to build a robust data flow. You separate preprocessing for training and inference. You prepare balanced batches to stabilize learning. You watch input latency that can throttle computation. You gain both time and stability.\r\n<h3>Optimize methodically, not randomly<\/h3>\r\nA well-tuned learning rate often beats a complex architecture. You learn to schedule rates without guesswork. You know when to decrease, freeze, or warm up. You measure real effects on loss and generalization. You avoid endless loops of random tweaks.\r\n\r\nWe also address unstable gradients. You recognize signs of explosion or vanishing. You adapt initialization to network depth. You combine normalization and regularization with care. You achieve a smoother, more readable descent.\r\n<h3>Build models that generalize<\/h3>\r\nOverfitting remains the main enemy. You set up clean, traceable validation. You use early stopping when it makes sense. You try simple, effective regularization strategies. You aim for robustness, not isolated peak scores.\r\n\r\nYou compare results against a strong baseline. You discuss gaps with honesty. You accept complexity only when it pays off. You document known model limits. This transparency eases adoption and maintenance.\r\n<h3>Industrialize without losing clarity<\/h3>\r\nMany prototypes fail at integration. We prepare deployment from day one. You export models in the recommended format. You anticipate version and dependency constraints. You think about inference compatibility during training.\r\n\r\nYou explore practical options by context. You plan conversion to lightweight targets when needed. You address security, quotas, and data governance. You design a realistic, documented release path. This rigor reduces late surprises.\r\n<h3>Traceability and collaboration in daily work<\/h3>\r\nReproducibility is not only academic. It saves weeks of work. You fix random seeds when possible. You log hyperparameters and versions. You store metrics and artifacts together. You simplify reviews and handovers between teams.\r\n\r\nYou also learn to write simple model cards. You state intended use and known limits. You flag potential biases and unsuitable contexts. You increase trust around the project. This habit becomes a team advantage.\r\n<h3>What you actually take away<\/h3>\r\nYou leave with a clear, tested method. You can frame a problem, prepare data, and train a model. You use TensorFlow 2.0 and the Keras API pragmatically. You read the signals of healthy training. You know when to stop, iterate, or simplify.\r\n\r\nThis TensorFlow 2.0 training targets responsible autonomy. You gain speed without sacrificing quality. You build useful, sustainable models. You improve decisions with solid measurements. You prepare the next steps on durable foundations.\r\n<h3>FAQ<\/h3>\r\n<strong>Can I take this course without advanced math?<\/strong>\r\nYes. Targeted refreshers are enough. Key points are explained with short examples.\r\n\r\n<strong>What does the industrialization part cover?<\/strong>\r\nExport, model formats, metric tracking, and practical deployment guidelines. All handled pragmatically.\r\n\r\n<strong>Which use cases are prioritized?<\/strong>\r\nSupervised classification and regression. You build reusable and scalable prototypes.\r\n\r\n<strong>How is this Deep Learning training different?<\/strong>\r\nIt links concrete decisions to measurable results. It favors a reproducible, transferable method.","inclus_dans_ce_cours_virtuel":"<ul>\r\n \t<li>Training provided by an industry expert<\/li>\r\n \t<li>Digital documentation and materials<\/li>\r\n \t<li>Achievement badge<\/li>\r\n<\/ul>","inclus_dans_ce_cours_presentiel":"<ul>\r\n \t<li>Training provided by an industry expert<\/li>\r\n \t<li>Digital documentation and materials<\/li>\r\n \t<li>Achievement badge<\/li>\r\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 with TensorFlow 2.0 - ITTA<\/title>\n<meta name=\"description\" content=\"Explore TensorFlow and deep learning with ITTA&#039;s AI training in Switzerland. 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