Online Deep Learning Training in Argentina

This instructor-led live training in Argentina (online or onsite) is intended for intermediate developers, data scientists, and AI practitioners aiming to utilize TensorFlow Lite for Edge AI applications.

By the conclusion of this training, attendees 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 Argentina (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 Argentina (online or onsite) targets intermediate-level data scientists and developers aiming to comprehend and implement deep learning methods using the Google Colab environment.

Upon completing this training, participants will be capable of:

• Configuring and navigating Google Colab for deep learning initiatives.
• Grasping the core concepts of neural networks.
• Developing deep learning models utilizing TensorFlow.
• Training and assessing deep learning models.
• Leveraging advanced capabilities of TensorFlow for deep learning.

This instructor-led, live training in Argentina (online or on-site) is designed for advanced professionals aiming 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.

This instructor-led, live training in Argentina (online or onsite) is designed for advanced professionals eager to explore cutting-edge XAI techniques for deep learning models, focusing on the creation 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, delivered online or on-site, targets intermediate to advanced-level data scientists, machine learning engineers, deep learning researchers, and computer vision experts who seek to expand their knowledge and proficiency in deep learning for text-to-image generation.

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

• Grasp 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 achieve better model performance and generalization.
• Integrate Stable Diffusion with other deep learning frameworks and tools.

This instructor-led live training in Argentina (online or onsite) is tailored for advanced professionals seeking to harness AI techniques to revolutionize drug discovery and development processes.

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

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

This instructor-led live training in Argentina (online or on-site) targets biologists who wish to understand the functioning of AlphaFold and utilize its models to guide 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 Argentina (online or onsite) is aimed at beginner to intermediate 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 Argentina (online or onsite) is designed for developers ranging from beginner to intermediate skill levels who aim to leverage Large Language Models for various natural language applications.

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

• Establish a development environment integrated with a widely used LLM.
• Construct a foundational LLM and fine-tune it using custom datasets.
• Apply LLMs to diverse natural language tasks, such as summarizing text, answering questions, and generating content.
• Debug and assess LLM performance utilizing tools like TensorBoard, PyTorch Lightning, and Hugging Face Datasets.

This instructor-led, live training (offered online or onsite) targets 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 its operational mechanics 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 instructor-led, live training in Argentina, participants will master the most relevant and cutting-edge machine learning techniques in Python while building a series of demo applications involving image, music, text, and financial data.

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

• Implement machine learning algorithms and techniques for solving complex problems.
• Apply deep learning and semi-supervised learning to applications involving image, music, text, and financial data.
• Push Python algorithms to their maximum potential.
• Use libraries and packages such as NumPy and Theano.

Applied AI from Scratch in Python provides programmers and data analysts with the foundational techniques required to build machine learning solutions from the ground up using Python. The course covers core principles of supervised learning, including classification and regression, as well as unsupervised learning methods such as clustering and anomaly detection, alongside advanced neural network architectures. It examines proven methodologies for leveraging scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate hands-on AI development. The program assists professionals in implementing practical ML models, evaluating the limitations of algorithms, and completing applied projects aimed at solving real-world problems.

Deep Reinforcement Learning (DRL) merges reinforcement learning principles with deep learning structures, empowering agents to make decisions through environmental interaction. This technology drives numerous modern AI innovations, including autonomous vehicles, robotics control, algorithmic trading, and adaptive recommendation systems. DRL enables artificial agents to acquire strategies, refine policies, and execute autonomous decisions via reward-based trial and error.

This instructor-led live training, available online or onsite, targets intermediate developers and data scientists eager to master and apply Deep Reinforcement Learning techniques for building intelligent agents capable of autonomous decision-making in complex settings.

Upon completing this training, participants will be equipped 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 in real-world scenarios including gaming, robotics, and decision optimization.
• Troubleshoot, visualize, and enhance training performance using contemporary tools.

Course Format

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

Customization Options

• For a customized course version (such as switching from TensorFlow to PyTorch), please contact us to make arrangements.

A deep dive into the fundamentals of artificial intelligence illustrates how smart technology is transforming digital strategies, automation processes, and decision-making capabilities within enterprise operations. This course covers core concepts including the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and machine learning paradigms, as well as communication, perception, and autonomous action. It equips executives and architects with the insights needed to evaluate opportunities for AI-driven transformation, assess emerging technology trends, and implement practical intelligent solutions to enhance business agility.

This course explores the application of AI—particularly Machine Learning and Deep Learning—within the automotive sector. It equips participants with the knowledge to identify technologies that can be applied across various scenarios in vehicles, ranging from basic automation and image recognition to advanced autonomous decision-making.

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

This instructor-led live training Argentina (available online or onsite) is designed for researchers and developers who want to use Chainer to build and train neural networks in Python, while keeping 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 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 Argentina (online or onsite) provides an introduction into the field of pattern recognition and machine learning. It touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.

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

• Apply core statistical methods to pattern recognition.
• Use key models like neural networks and kernel methods for data analysis.
• Implement advanced techniques for complex problem-solving.
• Improve prediction accuracy by combining different models.

This instructor-led live training in Argentina (online or onsite) targets researchers and developers who wish to install, set up, customize, and use the DeepMind Lab platform to develop general artificial intelligence and machine learning systems.

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

• Customize DeepMind Lab to build and run an environment that suits learning and training needs.
• Use DeepMind Lab's 3D simulation environment to train learning agents in a first-person viewpoint.
• Facilitate agent evaluation to develop intelligence in a 3D game-like world.

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

By the end 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 Argentina (online or onsite) is designed for data scientists who want to utilize 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 Argentina (online or onsite) targets developers or data scientists who wish to utilize Horovod for distributed deep learning training and scale it 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 begin running deep learning trainings.
• Install and configure Horovod to train models using TensorFlow, Keras, PyTorch, and Apache MXNet.
• Scale deep learning training with Horovod to execute on multiple GPUs.

In this instructor-led, live training, participants will learn how to use Matlab to design, build, and visualize a convolutional neural network for image recognition.

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

• Build a deep learning model
• Automate data labeling
• Work with models from Caffe and TensorFlow-Keras
• Train data using multiple GPUs, the cloud, or clusters

Audience

• Developers
• Engineers
• Domain experts

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

This instructor-led, live training in Argentina (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.

In this instructor-led, live training, participants will learn advanced techniques for Machine Learning with R as they step through the creation of a real-world application.

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

• Understand and implement unsupervised learning techniques
• Apply clustering and classification to make predictions based on real world data.
• Visualize data to quickly gain insights, make decisions and further refine analysis.
• Improve the performance of a machine learning model using hyper-parameter tuning.
• Put a model into production for use in a larger application.
• Apply advanced machine learning techniques to answer questions involving social network data, big data, and more.

This instructor-led live training in Argentina (online or onsite) targets developers and data scientists aiming to utilize TensorFlow 2.x for building predictors, classifiers, generative models, neural networks, and other related applications.

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

• Install and configure TensorFlow 2.x.
• Understand the advantages of TensorFlow 2.x over previous iterations.
• Construct deep learning models.
• Implement an advanced image classifier.
• Deploy deep learning models to cloud environments, mobile devices, and IoT systems.

This course provides a conceptual foundation in neural networks, with a broad overview of machine learning algorithms and deep learning techniques, including their algorithms and practical applications.

Part-1 (40%) of the training focuses heavily on fundamentals, helping you select the appropriate technology, such as TensorFlow, Caffe, Theano, DeepDrive, or Keras.

Part-2 (20%) introduces Theano, a Python library designed to simplify the creation of deep learning models.

Part-3 (40%) is extensively based on TensorFlow, the API for Google's open-source library for Deep Learning. All examples and hands-on exercises will be conducted within the TensorFlow environment.

Audience

This course is designed for engineers who wish to utilize TensorFlow for their Deep Learning projects.

Upon completion of this course, delegates will:

• possess a solid understanding of Deep Neural Networks (DNN), CNNs, and RNNs
• grasp TensorFlow’s structure and deployment mechanisms
• be capable of handling installation, production environment setup, and architecture configuration tasks
• be able to assess code quality, perform debugging, and implement monitoring
• be proficient in implementing advanced production tasks such as model training, graph construction, and logging