Deep Learning Training in Turin

This instructor-led, live training in Turin (online or onsite) is aimed at intermediate-level developers, data scientists, and AI practitioners who wish to leverage TensorFlow Lite for Edge AI applications.

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

• Understand the fundamentals of TensorFlow Lite and its role in Edge AI.
• Develop and optimize AI models using TensorFlow Lite.
• Deploy TensorFlow Lite models on various edge devices.
• Utilize tools and techniques for model conversion and optimization.
• Implement practical Edge AI applications using TensorFlow Lite.

This instructor-led, live training in Turin (online or onsite) is aimed at advanced-level professionals who wish to deepen their understanding of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models using Google Colab.

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

• Build and train convolutional neural networks (CNNs) using TensorFlow.
• Leverage Google Colab for scalable and efficient cloud-based model development.
• Implement image preprocessing techniques for computer vision tasks.
• Deploy computer vision models for real-world applications.
• Use transfer learning to enhance the performance of CNN models.
• Visualize and interpret the results of image classification models.

This instructor-led, live training in Turin (online or onsite) is aimed at intermediate-level data scientists and developers who wish to understand and apply deep learning techniques using the Google Colab environment.

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

• Set up and navigate Google Colab for deep learning projects.
• Understand the fundamentals of neural networks.
• Implement deep learning models using TensorFlow.
• Train and evaluate deep learning models.
• Utilize advanced features of TensorFlow for deep learning.

This instructor-led, live training in Turin (online or on-site) is designed for advanced professionals wishing to specialize in cutting-edge deep learning techniques for NLU.

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

• Understand the key distinctions 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.

This instructor-led, live training in Turin (online or onsite) is aimed at advanced-level professionals who wish to explore state-of-the-art XAI techniques for deep learning models, with a focus on building 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 Turin (online or onsite) targets intermediate to advanced data scientists, machine learning engineers, deep learning researchers, and computer vision experts seeking to expand their knowledge and skills in deep learning for text-to-image generation.

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

• Comprehend advanced deep learning architectures and techniques for text-to-image generation.
• Implement complex models and optimizations for high-quality image synthesis.
• Optimize performance and scalability for large datasets and complex models.
• Tune hyperparameters to enhance model performance and generalization.
• Integrate Stable Diffusion with other deep learning frameworks and tools.

This instructor-led live training in Turin (online or onsite) is designed for advanced-level professionals aiming to use 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.

This instructor-led live training in Turin (available online or on-site) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies.

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

• Understand the basic principles of AlphaFold.
• Learn how AlphaFold works.
• Learn how to interpret AlphaFold predictions and results.

This instructor-led, live training in Turin (online or onsite) is aimed at beginner to intermediate-level data scientists and machine learning engineers who wish to improve the performance of their deep learning models.

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

• Understand the principles of distributed deep learning.
• Install and configure DeepSpeed.
• Scale deep learning models on distributed hardware using DeepSpeed.
• Implement and experiment with DeepSpeed features for optimization and memory efficiency.

This instructor-led, live training in Turin (online or onsite) is designed for beginner to intermediate-level developers who wish to apply Large Language Models to 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 guided, live training (available online or onsite) is designed for 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.

During this instructor-led live training in Turin, participants will master the most relevant and cutting-edge machine learning techniques in Python by developing a series of demo applications that handle image, music, text, and financial data.

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

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

This course on Applied AI from Scratch in Python provides programmers and data analysts with the essential skills needed to develop machine learning solutions entirely from the ground up using Python. It covers the fundamental concepts of supervised learning (including classification and regression) and unsupervised learning (such as clustering and anomaly detection), alongside an exploration of advanced neural network architectures. Participants will examine effective techniques for leveraging scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate hands-on AI development. The curriculum supports professionals in implementing practical machine learning models, assessing the constraints of various algorithms, and completing applied projects designed to solve real-world challenges.

Deep Reinforcement Learning (DRL) merges the principles of reinforcement learning with deep learning architectures, empowering agents to make informed decisions through interaction with their surroundings. This technology drives numerous contemporary AI innovations, including self-driving cars, robotic control systems, algorithmic trading, and adaptive recommendation engines. By leveraging reward-based learning, DRL enables artificial agents to master strategies, refine policies, and execute autonomous decisions through trial and error.

This instructor-led live training, available both online and onsite, targets intermediate developers and data scientists eager to master and apply Deep Reinforcement Learning techniques. The course focuses on building intelligent agents capable of autonomous decision-making within complex environments.

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

• Grasp the theoretical foundations and mathematical underpinnings of Reinforcement Learning.
• Implement core RL algorithms such as Q-Learning, Policy Gradients, and Actor-Critic methods.
• Construct and train Deep Reinforcement Learning agents using TensorFlow or PyTorch.
• Deploy DRL solutions in real-world scenarios, including gaming, robotics, and decision optimization.
• Utilize modern tools to troubleshoot, visualize, and optimize training performance.

Course Format

• Interactive lectures paired with guided discussions.
• Practical exercises and hands-on implementations.
• Live coding demonstrations and project-based applications.

Customization Options

• For customized versions of this course (e.g., utilizing PyTorch instead of TensorFlow), please reach out to us to arrange your specific requirements.

Delving into the fundamentals of artificial intelligence uncovers how intelligent technologies are transforming digital strategy, automation, and decision-making processes within enterprise operations. The course explores core concepts such as the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and machine learning paradigms, alongside topics like communication, perception, and autonomous action. It equips executives and architects with the insights needed to evaluate AI-driven transformation opportunities, assess emerging technology trends, and implement practical intelligent solutions to enhance business agility.

This program explores the application of AI—with a specific focus on Machine Learning and Deep Learning—within the automotive industry. It aims to identify technologies that can be (potentially) utilized across various automotive scenarios, ranging from basic automation and image recognition to autonomous decision-making processes.

An Artificial Neural Network serves as a computational data model utilized in the creation of Artificial Intelligence (AI) systems capable of executing "intelligent" tasks. Neural Networks are frequently employed in Machine Learning (ML) applications, which represent one implementation of AI. Deep Learning constitutes a subset of Machine Learning.

This instructor-led, live training in Turin (online or onsite) is aimed at researchers and developers who wish to use Chainer to build and train neural networks in Python while making the code easy to debug.

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

• Set up the necessary development environment to start developing neural network models.
• Define and implement neural network models using a comprehensible source code.
• Execute examples and modify existing algorithms to optimize deep learning training models while leveraging GPUs for high performance.

This instructor-led, live training in Turin (online or onsite) offers an introduction to the fields of pattern recognition and machine learning. It covers practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.

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

• Apply fundamental statistical methods to pattern recognition.
• Utilize essential models such as neural networks and kernel methods for data analysis.
• Implement advanced techniques to solve complex problems.
• Enhance prediction accuracy by integrating various models.

This instructor-led, live training in Turin (online or on-site) is designed for researchers and developers who wish to install, configure, customize, and utilize the DeepMind Lab platform to develop general artificial intelligence and machine learning solutions.

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

• Customize DeepMind Lab to create and run an environment tailored to specific learning and training requirements.
• Utilize DeepMind Lab's 3D simulation environment to train learning agents from a first-person perspective.
• Facilitate agent evaluation to foster intelligence development within a 3D, game-like world.

This instructor-led live training in Turin (online or onsite) is aimed at business analysts, data scientists, and developers who wish to build and implement deep learning models to accelerate revenue growth and solve problems in the business world.

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

• Understand the core concepts of machine learning and deep learning.
• Get insights on the future of business and industry with ML and DL.
• Define business strategies and solutions with deep learning.
• Learn how to apply data science and deep learning in solving business problems.
• Build deep learning models using Python, Pandas, TensorFlow, CNTK, Torch, Keras, etc.

This instructor-led, live training in Turin (online or onsite) is aimed at data scientists who wish to accelerate real-time machine learning applications and deploy them at scale.

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

• Install the OpenVINO toolkit.
• Accelerate a computer vision application using an FPGA.
• Execute different CNN layers on the FPGA.
• Scale the application across multiple nodes in a Kubernetes cluster.

This instructor-led, live training in Turin (online or onsite) is tailored 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.

This instructor-led, live training in Turin (online or onsite) is aimed at developers or data scientists who wish to use Horovod to run distributed deep learning trainings and scale them up to run across multiple GPUs in parallel.

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

• Set up the necessary development environment to start running deep learning trainings.
• Install and configure Horovod to train models with TensorFlow, Keras, PyTorch, and Apache MXNet.
• Scale deep learning training with Horovod to run on multiple GPUs.

In this guided, hands-on training session, participants will discover how to utilize Matlab to design, construct, and visualize convolutional neural networks for image recognition tasks.

Upon completing this training, participants will be capable of:

• Constructing deep learning models
• Automating the process of data labeling
• Utilizing models from Caffe and TensorFlow-Keras
• Training data across multiple GPUs, cloud environments, or clusters

Audience

• Developers
• Engineers
• Domain experts

Course Format

• A blend of lectures, discussions, exercises, and extensive practical application

This instructor-led, live training in Turin (online or onsite) is aimed at software engineers who wish to program in Python with OpenCV 4 for deep learning.

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

• View, load, and classify images and videos using OpenCV 4.
• Implement deep learning in OpenCV 4 with TensorFlow and Keras.
• Run deep learning models and generate impactful reports from images and videos.

During this instructor-led live training, participants will master advanced Machine Learning techniques using R by building a real-world application step by step.

Upon completion of this training, participants will be capable of:

• Understanding and implementing unsupervised learning techniques
• Utilizing clustering and classification to generate predictions from real-world data.
• Visualizing data to quickly gain insights, make informed decisions, and further refine analysis.
• Enhancing the performance of a machine learning model through hyper-parameter tuning.
• Deploying a model into production for integration into larger applications.
• Applying advanced machine learning techniques to address queries related to social network data, big data, and more.

This instructor-led live training in Turin (online or on-site) targets developers and data scientists who intend to use TensorFlow 2.x for building predictors, classifiers, generative models, neural networks, and related applications.

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

• Install and configure TensorFlow 2.x.
• Understand the benefits of TensorFlow 2.x over previous versions.
• Build deep learning models.
• Implement an advanced image classifier.
• Deploy a deep learning model to the cloud, mobile, and IoT devices.

This course provides a conceptual foundation in neural networks, machine learning algorithms, and deep learning (algorithms and applications).

Part-1 (40%) of this training focuses heavily on fundamentals, aiding you in selecting the appropriate technology: TensorFlow, Caffe, Theano, DeepDrive, Keras, etc.

Part-2 (20%) of this training introduces Theano - a Python library that simplifies the creation of deep learning models.

Part-3 (40%) of the training is heavily centered on Tensorflow - the API for Google's open-source software library for Deep Learning. All examples and hands-on exercises will be conducted in TensorFlow.

Audience

This course is designed for engineers looking to leverage TensorFlow for their Deep Learning projects.

Upon completion of this course, delegates will:

• possess a solid understanding of deep neural networks (DNN), CNN, and RNN.
• comprehend the structure and deployment mechanisms of TensorFlow.
• be capable of performing installation, production environment setup, architecture tasks, and configuration.
• be able to evaluate code quality, perform debugging, and monitoring.
• be able to implement advanced production-grade tasks such as model training, graph construction, and logging.