Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to Deep Learning Frameworks
- Differentiating deep learning methodologies from conventional machine learning approaches
- Identifying applicable use cases within computer vision, natural language processing, and other public sector domains
- Surveying the deep learning software stack, including TensorFlow 2.x, Keras, and PyTorch
- Configuring GPU-accelerated development environments for government
Fundamentals of Neural Network Architecture
- Core components: artificial neurons, activation functions, and layered structures
- Forward propagation mechanics and prediction generation
- Selection of loss functions for classification and regression analyses
- Optimization via gradient descent and backpropagation algorithms
- Implementation of a baseline neural network using the MNIST dataset
Convolutional Neural Networks for Visual Data Analysis
- Principles of convolution, filter application, and feature map generation
- Utilization of pooling layers for dimensionality reduction
- Review of established CNN architectures, including LeNet, VGG, and ResNet frameworks
- Construction and training protocols for image classification models
- Visualization techniques for learned features and intermediate activations
Data Augmentation and Model Optimization Strategies
- Evaluating the impact of data augmentation on model generalization and overfitting mitigation
- Application of image transformations, including rotation, flipping, zooming, and cropping
- Deployment of augmentation pipelines using Keras preprocessing layers
- Implementation of regularization methods, such as dropout and batch normalization
- Model monitoring via validation metrics and early stopping criteria
Transfer Learning with Pre-Trained Architectures
- Conceptual foundation and operational benefits of transfer learning
- Integration of pre-trained models from the Keras Applications library, including ResNet, EfficientNet, and MobileNet
- Feature extraction techniques involving frozen base layers and new classifier training
- Fine-tuning strategies through selective layer unfreezing for domain adaptation
- Achieving high performance with constrained datasets
Recurrent Networks and Temporal Sequence Modeling
- Analyzing sequential data structures and temporal dependencies
- Overview of recurrent neural networks (RNNs) and the vanishing gradient challenge
- Utilization of LSTM and GRU cells for processing long-range dependencies
- Training character-level text generation models
- Application of word embeddings and the Embedding layer within Keras
Natural Language Processing Operations
- Text preprocessing workflows: tokenization, padding, and vocabulary construction
- Development of text classifiers using RNNs and LSTMs
- Architectural concepts for sequence-to-sequence models in machine translation
- Integration of attention mechanisms in contemporary NLP applications
- Practical implementation using TensorFlow 2.x text processing APIs for government
Capstone Project: Automated Image Captioning
- Integration of computer vision and NLP within multimodal architectures
- Feature extraction from images using pre-trained CNN encoders
- Development of LSTM-based decoders for automated caption generation
- Management of multi-input configurations via the Keras functional API
- End-to-end training and evaluation of the captioning pipeline
Deployment and Continued Learning Pathways
- Model deployment strategies using TensorFlow Serving
- Exploration of transformer architectures and large language models
- NVIDIA Deep Learning Institute advanced workshops and certification options
- Access to community resources, datasets, and potential project initiatives
Requirements
- Demonstrated foundational competency in Python programming, including proficiency with functions, iterative structures, dictionaries, and array operations
- Working knowledge of core programming principles, such as variable management, conditional logic, and data structure implementation
- Previous exposure to deep learning or machine learning frameworks is not a prerequisite for participation
Intended Audience
- Software developers and engineering personnel seeking to transition into artificial intelligence and machine learning disciplines
- Data analysts and scientists aiming to augment their expertise with deep learning capabilities
- Technical professionals interested in understanding and deploying neural network architectures for mission support
- Students and researchers initiating their studies in deep learning methodologies
This training curriculum is designed specifically for government personnel to enhance technical workforce readiness.
8 Hours
Testimonials (2)
The training was organized and well-planned out, and I come out of it with systematized knowledge and a good look at topics we looked at
Magdalena - Samsung Electronics Polska Sp. z o.o.
Course - Deep Learning with TensorFlow 2
I really liked the end where we took the time to play around with CHAT GPT. The room was not set up the best for this- instead of one large table a couple of small ones so we could get into small groups and brainstorm would have helped
