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Duration 8 hours
Course Outline
Introduction to Deep Learning
- Distinguishing deep learning from traditional machine learning approaches
- Real-world applications spanning computer vision, NLP, and other domains
- Survey of the deep learning landscape: TensorFlow 2.x, Keras, and PyTorch
- Establishing a GPU-accelerated development environment
The Mechanics of Deep Learning
- Artificial neurons, activation functions, and network layer structures
- Forward propagation processes for computing predictions
- Selecting appropriate loss functions for classification and regression
- Gradient descent optimization techniques and backpropagation algorithms
- Training an initial neural network using the MNIST dataset
Convolutional Neural Networks for Computer Vision
- Concepts of convolution, filters, and feature maps
- Pooling layers and methods for dimensionality reduction
- CNN architectures: Exploring LeNet, VGG, and ResNet principles
- Developing and training a CNN for image classification purposes
- Visualizing learned features and intermediate layer activations
Data Augmentation and Enhancing Model Accuracy
- The role of data augmentation in preventing overfitting and boosting generalization
- Image transformation techniques: rotation, flipping, zooming, and cropping
- Constructing augmentation pipelines using Keras preprocessing layers
- Regularization methods including dropout and batch normalization
- Tracking training progress via validation metrics and employing early stopping
Transfer Learning with Pre-Trained Models
- Concepts behind transfer learning and its effectiveness
- Loading pre-trained models from Keras Applications (such as ResNet, EfficientNet, and MobileNet)
- Feature extraction techniques: freezing base layers while training new classifiers
- Fine-tuning strategies: selectively unfreezing layers for domain adaptation
- Attaining high accuracy despite limited training data availability
Recurrent Networks and Sequence Modeling
- Introduction to sequential data and temporal dependencies
- Recurrent neural networks (RNNs) and addressing the vanishing gradient problem
- LSTM and GRU cells for managing long-range dependencies
- Training a character-level text generation model
- Word embeddings and utilizing the Embedding layer in Keras
Natural Language Processing Fundamentals
- Text preprocessing steps: tokenization, padding, and vocabulary construction
- Developing a text classifier using RNNs and LSTMs
- Concepts of sequence-to-sequence models for machine translation
- Attention mechanisms and their significance in contemporary NLP
- Practical NLP implementation using TensorFlow 2.x text processing APIs
Final Project: Image Captioning
- Integrating computer vision and NLP within a multimodal architecture
- Extracting image features via a pre-trained CNN encoder
- Designing an LSTM-based decoder for generating captions
- Managing multiple input layers using the Keras functional API
- Training and evaluating the complete end-to-end captioning pipeline
Next Steps and Resources
- Deploying trained models using TensorFlow Serving
- Exploring transformer architectures and large language models
- NVIDIA DLI advanced workshops and certification pathways
- Community resources, datasets, and project inspiration
Requirements
- Foundational proficiency in Python programming (including functions, loops, dictionaries, and arrays)
- Familiarity with fundamental programming concepts such as variables, conditionals, and data structures
- No previous experience in deep learning or machine learning is necessary
Target Audience
- Software developers and engineers transitioning into the fields of AI and machine learning
- Data analysts and data scientists looking to acquire deep learning expertise
- Technical professionals aiming to comprehend and apply neural network models
- Students and researchers initiating their exploration into deep learning
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 3200 € + VAT*
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Testimonials (2)
Getting people that never used AI some repetition in prompting and people that do use AI to consider different methods to using it.
Matthew Gay - Tarsus Pharmaceuticals
Course - Artificial Intelligence (AI) Overview
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
