Deep Learning Training in New York

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

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

• Understand the fundamentals of TensorFlow Lite and its role in Edge AI for government.
• Develop and optimize AI models using TensorFlow Lite for deployment in government settings.
• Deploy TensorFlow Lite models on various edge devices used in public sector applications.
• Utilize tools and techniques for model conversion and optimization suitable for government workflows.
• Implement practical Edge AI applications using TensorFlow Lite to enhance public sector operations.

This instructor-led, live training (online or onsite) is designed for advanced-level professionals who aim 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 training is tailored to ensure that professionals can apply these skills effectively in their roles, particularly in projects for government.

This instructor-led, live training in [location] (online or onsite) is designed for intermediate-level data scientists and developers who aim 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 government 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 (online or onsite) is designed for government professionals at the advanced level who wish to specialize in cutting-edge deep learning techniques for Natural Language Understanding (NLU). By the end of this training, participants will be able to: - Understand the key differences between NLU and Natural Language Processing (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 for government use.

This instructor-led, live training (online or onsite) is designed for government professionals at an advanced level who wish to explore state-of-the-art XAI techniques for deep learning models, with a focus on building interpretable AI systems for government. 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 New York (online or onsite) is designed for intermediate to advanced-level data scientists, machine learning engineers, deep learning researchers, and computer vision experts who wish 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: - Understand advanced deep learning architectures and techniques for generating images from text. - Implement complex models and optimizations to produce high-quality image synthesis. - Optimize performance and scalability for large datasets and sophisticated models. - Tune hyperparameters to enhance model performance and generalization. - Integrate Stable Diffusion with other deep learning frameworks and tools, ensuring alignment with public sector workflows and governance requirements for government.

This instructor-led, live training (available online or onsite) is designed for advanced-level professionals who seek to harness artificial intelligence techniques to transform drug discovery and development processes for government and industry. By the end of this training, participants will be able to: - Comprehend the 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. - Incorporate AI-driven approaches into the clinical trial process.

The instructor-led, live training session, offered via New York (virtual or in-person), is designed for biological scientists seeking to comprehend the underlying mechanics of AlphaFold and apply its predictive models to support experimental workflows. This program is specifically developed for government professionals involved in biological research.

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

• Grasp the fundamental principles governing AlphaFold.
• Articulate the operational mechanisms of the AlphaFold system.
• Accurately interpret AlphaFold predictions and associated analytical results.

This instructor-led, live training (online or onsite) is designed for government developers at beginner to intermediate levels who wish to utilize Large Language Models for 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 and fine-tune a basic LLM on a custom dataset. - Apply LLMs to different natural language tasks such as text summarization, question answering, text generation, and more. - Debug and evaluate LLMs using tools like TensorBoard, PyTorch Lightning, and Hugging Face Datasets. This training is tailored to enhance the capabilities of government developers in leveraging advanced technologies for government applications.

This instructor-led, 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 applications, including those relevant to government operations.

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

• Comprehend the principles of Stable Diffusion and its application in image generation.
• Develop and train Stable Diffusion models for various image generation tasks.
• Utilize Stable Diffusion for diverse image generation scenarios, such as inpainting, outpainting, and image-to-image translation.
• Enhance the performance and stability of Stable Diffusion models to meet rigorous standards for government use cases.

In this instructor-led, live training for government participants in [location], attendees will gain proficiency in the most relevant and cutting-edge machine learning techniques using Python. Through hands-on exercises, they will develop 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 methods to applications involving image, music, text, and financial data. - Optimize Python algorithms to their maximum potential. - Utilize libraries and packages such as NumPy and Theano.

This resource provides program developers and data analysts with the essential methodologies required to construct machine learning frameworks from the ground up utilizing Python. It addresses the fundamental concepts governing supervised learning techniques, including classification and regression, as well as unsupervised methods such as clustering and anomaly detection, alongside sophisticated neural network designs. The material details established protocols for leveraging scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate direct AI development. This content is designed to assist professionals in deploying functional models, assessing algorithmic constraints, and executing applied projects aimed at addressing complex operational challenges for government entities.

Deep Reinforcement Learning (DRL) integrates the principles of reinforcement learning with deep learning architectures, enabling agents to make decisions through interaction with their environments. This technology is pivotal in driving many modern artificial intelligence advancements, including self-driving vehicles, robotics control, algorithmic trading, and adaptive recommendation systems. DRL allows an artificial agent to learn strategies, optimize policies, and make autonomous decisions based on trial-and-error learning using a reward system.

This instructor-led, live training (online or onsite) is designed for intermediate-level developers and data scientists who wish to gain proficiency in Deep Reinforcement Learning techniques. The goal is to build intelligent agents capable of autonomous decision-making in complex environments, which can be particularly useful for government applications.

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

• Understand the theoretical foundations and mathematical principles underlying Reinforcement Learning.
• Implement key RL algorithms such as Q-Learning, Policy Gradients, and Actor-Critic methods.
• Develop and train Deep Reinforcement Learning agents using TensorFlow or PyTorch.
• Apply DRL to real-world scenarios, including games, robotics, and decision optimization for government use cases.
• Troubleshoot, visualize, and optimize the training performance of DRL models using modern tools.

Format of the Course

• Interactive lectures and guided discussions.
• Hands-on exercises and practical implementations.
• Live coding demonstrations and project-based applications.

Course Customization Options

• To request a customized version of this course, such as using PyTorch instead of TensorFlow, please contact us to arrange.

This module examines the foundational principles of artificial intelligence and their impact on digital strategy, automation, and enterprise decision-making. It provides a comprehensive overview of core concepts, including the evolution of AI, problem-solving frameworks, knowledge representation, probabilistic reasoning, and machine learning methodologies, alongside capabilities related to communication, perception, and autonomous operations. Designed for government executives and technical architects, this content enables the evaluation of AI-driven transformation initiatives, the assessment of emerging technology trends, and the implementation of practical intelligent solutions to enhance operational agility.

This course provides an overview of artificial intelligence, with a focus on machine learning and deep learning, as applied to the automotive industry. It is designed to help participants identify which technologies can potentially be utilized in various scenarios within a vehicle, ranging from basic automation to image recognition and autonomous decision-making for government applications.

Artificial Neural Networks are computational models utilized in the creation of Artificial Intelligence (AI) systems designed to perform tasks that require "intelligent" capabilities. These networks are frequently employed in Machine Learning (ML) applications, which represent one approach to implementing AI. Deep Learning, a specialized subset of ML, further advances these techniques for government and other sectors.

This instructor-led, live training (online or onsite) provides an introduction to the field of pattern recognition and machine learning. It covers practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics for government use. By the end of this training, participants will be able to: - Apply core statistical methods to pattern recognition tasks. - Utilize key models such as neural networks and kernel methods for data analysis. - Implement advanced techniques to address complex problem-solving scenarios. - Enhance prediction accuracy by integrating different models.

This instructor-led, live training (available online or onsite) is designed for government data scientists who wish to utilize TensorFlow to analyze potential fraud data. By the end of this training, participants will be able to: - Develop a fraud detection model using Python and TensorFlow. - Construct linear regressions and linear regression models to predict fraudulent activities. - Create an end-to-end AI application for analyzing fraud data for government use.

In this instructor-led, live training, participants will learn how to utilize MATLAB to design, construct, and visualize a convolutional neural network for image recognition tasks.

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

• Develop a deep learning model
• Automate data labeling processes
• Integrate models from Caffe and TensorFlow-Keras
• Leverage multiple GPUs, cloud resources, or clusters for training data

Audience

• Developers
• Engineers
• Domain experts

Format of the Course

• Combination of lecture, discussion, practical exercises, and extensive hands-on practice to ensure proficiency in using MATLAB for government applications.

This instructor-led, live training (online or onsite) is aimed at developers and data scientists who wish to utilize TensorFlow 2.x for government to build predictors, classifiers, generative models, neural networks, and more. By the end of this training, participants will be able to: - Install and configure TensorFlow 2.x. - Understand the advantages of TensorFlow 2.x over previous versions. - Construct deep learning models. - Implement an advanced image classifier. - Deploy a deep learning model to the cloud, mobile devices, and IoT systems.

This course begins with an introduction to the conceptual knowledge of neural networks and machine learning algorithms, including deep learning (algorithms and applications). Part-1 (40%) of this training focuses on fundamental concepts, which will assist in selecting the appropriate technology: TensorFlow, Caffe, Theano, DeepDrive, Keras, etc. Part-2 (20%) introduces Theano, a Python library that simplifies the process of writing deep learning models. Part-3 (40%) is extensively based on TensorFlow, Google's open-source software library for deep learning. All examples and hands-on exercises will be conducted using TensorFlow. **Audience** This course is designed for engineers who intend to use TensorFlow for their deep learning projects for government. Upon completing this course, participants will: - Have a comprehensive understanding of deep neural networks (DNN), convolutional neural networks (CNN), and recurrent neural networks (RNN). - Understand the structure and deployment mechanisms of TensorFlow. - Be capable of performing installation, production environment setup, architecture tasks, and configuration. - Be able to assess code quality, perform debugging, and monitoring. - Be proficient in implementing advanced production tasks such as training models, building graphs, and logging.