{"course":{"productid":33906,"modality":6,"active":true,"language":"fr","title":"Deep Learning in Theory and Practice","productcode":"HU0F0S","vendorcode":"HP","vendorname":"HP","fullproductcode":"HP-HU0F0S","courseware":{"has_ekit":false,"has_printkit":true,"language":""},"url":"https:\/\/portal.flane.ch\/course\/hewlettpackard-hu0f0s","objective":"<h5>At the end of this course, you will be able to: <\/h5><ul>\n<li>Design and develop neural network models in structured frameworks<\/li><li>Understand core buzzwords and terminology<\/li><li>Kick-start a deep learning project<\/li><li>Build a suitable deep learning model for a given problem statement<\/li><li>Export the deep learning model for consumption by other app developers<\/li><\/ul>","essentials":"<ul>\n<li>Basic understanding of any programming or scripting language<\/li><\/ul>","audience":"<ul>\n<li>This course is ideal for data engineers, data scientists, researchers, solution architects, software engineers, AI enthusiasts, statisticians and other IT professionals looking for a practical foundation in deep learning with neural networks.<\/li><\/ul>","outline":"<h5>Module 1 A Gentle Introduction to Deep Learning  <\/h5><ul>\n<li>History of deep learning &bull; Ethics in AI<\/li><li>Overview of deep learning<\/li><li>A single neuron<\/li><li>What is a transfer function?<\/li><\/ul><h5>Module 2 Introduction to TensorFlow <\/h5><ul>\n<li>Introduction to TensorFlow<\/li><li>The TensorFlow architecture<\/li><li>TensorFlow data<\/li><\/ul><h5>Module 3 Introduction to Keras <\/h5><ul>\n<li>Introduction to Keras<\/li><li>The Keras architecture<\/li><li>Keras models<\/li><li>Keras sequential vs functional API<\/li><li>Keras layers<\/li><li>Keras core modules<\/li><\/ul><h5>Module 4 Overfitting and Underfitting <\/h5><ul>\n<li>Overfitting and underfitting<\/li><li>How to avoid<\/li><\/ul>\n<h5>Module 5 Activation, Loss and Optimizer Functions <\/h5><ul>\n<li>Activation functions<\/li><li>Loss functions<\/li><li>Optimization functions<\/li><\/ul><h5>Module 6 Regularizing a Model &amp; Hyperparameter Optimization <\/h5><ul>\n<li>Why regularize?<\/li><li>Regularization types<\/li><li>Hyperparameters<\/li><li>Optimization techniques<\/li><\/ul><h5>Module 7 Pooling and Convolutions <\/h5><ul>\n<li>Convolutions<\/li><li>Pooling in neural networks<\/li><\/ul><h5>Module 8 Big Data Deep Learning <\/h5><ul>\n<li>The big data perspective<\/li><li>The big data deep learning team and roles<\/li><li>Apache Spark<\/li><li>Databricks<\/li><li>Determined AI<\/li><li>HPE Ezmeral<\/li><\/ul>","summary":"<p>This training covers industry-standard deep learning with applications in computer vision and natural language processing. With focus on hands-on labs using TensorFlow and Keras, the course includes a comprehensive and practical guide to deep learning, including optimization techniques.<\/p>","objective_plain":"At the end of this course, you will be able to: \n\n\n- Design and develop neural network models in structured frameworks\n- Understand core buzzwords and terminology\n- Kick-start a deep learning project\n- Build a suitable deep learning model for a given problem statement\n- Export the deep learning model for consumption by other app developers","essentials_plain":"- Basic understanding of any programming or scripting language","audience_plain":"- This course is ideal for data engineers, data scientists, researchers, solution architects, software engineers, AI enthusiasts, statisticians and other IT professionals looking for a practical foundation in deep learning with neural networks.","outline_plain":"Module 1 A Gentle Introduction to Deep Learning  \n\n\n- History of deep learning \u2022 Ethics in AI\n- Overview of deep learning\n- A single neuron\n- What is a transfer function?\nModule 2 Introduction to TensorFlow \n\n\n- Introduction to TensorFlow\n- The TensorFlow architecture\n- TensorFlow data\nModule 3 Introduction to Keras \n\n\n- Introduction to Keras\n- The Keras architecture\n- Keras models\n- Keras sequential vs functional API\n- Keras layers\n- Keras core modules\nModule 4 Overfitting and Underfitting \n\n\n- Overfitting and underfitting\n- How to avoid\n\nModule 5 Activation, Loss and Optimizer Functions \n\n\n- Activation functions\n- Loss functions\n- Optimization functions\nModule 6 Regularizing a Model & Hyperparameter Optimization \n\n\n- Why regularize?\n- Regularization types\n- Hyperparameters\n- Optimization techniques\nModule 7 Pooling and Convolutions \n\n\n- Convolutions\n- Pooling in neural networks\nModule 8 Big Data Deep Learning \n\n\n- The big data perspective\n- The big data deep learning team and roles\n- Apache Spark\n- Databricks\n- Determined AI\n- HPE Ezmeral","summary_plain":"This training covers industry-standard deep learning with applications in computer vision and natural language processing. With focus on hands-on labs using TensorFlow and Keras, the course includes a comprehensive and practical guide to deep learning, including optimization techniques.","skill_level":"Beginner","version":"1.0","duration":{"unit":"d","value":2,"formatted":"2 jours"},"pricelist":{"List Price":{"CH":{"country":"CH","currency":"CHF","taxrate":8.1,"price":1800},"DE":{"country":"DE","currency":"EUR","taxrate":19,"price":1500},"AT":{"country":"AT","currency":"EUR","taxrate":20,"price":1500}}},"lastchanged":"2025-07-29T12:18:23+02:00","parenturl":"https:\/\/portal.flane.ch\/swisscom\/fr\/json-courses","nexturl_course_schedule":"https:\/\/portal.flane.ch\/swisscom\/fr\/json-course-schedule\/33906","source_lang":"fr","source":"https:\/\/portal.flane.ch\/swisscom\/fr\/json-course\/hewlettpackard-hu0f0s"}}