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Duration 8 hours
Course Outline
Introduction to Deep Learning
- What constitutes deep learning and how it contrasts with traditional machine learning
- Real-world applications in computer vision, NLP, and other fields
- An overview of the deep learning landscape: TensorFlow 2.x, Keras, PyTorch
- Establishing a GPU-accelerated development environment
The Mechanics of Deep Learning
- Artificial neurons, activation functions, and network layers
- Forward propagation and calculating predictions
- Loss functions for classification and regression tasks
- Gradient descent optimisation and backpropagation
- Training your initial neural network on the MNIST dataset
Convolutional Neural Networks for Computer Vision
- Understanding convolution, filters, and feature maps
- Pooling layers and dimensionality reduction
- CNN architectures: concepts behind LeNet, VGG, and ResNet
- Constructing and training a CNN for image classification
- Visualising learned features and intermediate activations
Data Augmentation and Improving Model Accuracy
- Why data augmentation combats overfitting and enhances generalisation
- Image transformations: rotation, flipping, zooming, and cropping
- Implementing augmentation pipelines using Keras preprocessing layers
- Dropout, batch normalisation, and other regularisation techniques
- Monitoring training progress via validation metrics and early stopping
Transfer Learning with Pre-Trained Models
- Understanding transfer learning and why it is effective
- Loading pre-trained models from Keras Applications (ResNet, EfficientNet, MobileNet)
- Feature extraction: freezing base layers and training new classifiers
- Fine-tuning: selectively unfreezing layers for domain adaptation
- Achieving high accuracy with limited training data
Recurrent Networks and Sequence Modelling
- Introduction to sequential data and temporal dependencies
- Recurrent neural networks (RNNs) and the vanishing gradient problem
- LSTM and GRU cells for managing long-range dependencies
- Training a character-level text generation model
- Word embeddings and the Embedding layer in Keras
Natural Language Processing Fundamentals
- Text pre-processing: tokenisation, padding, and vocabulary building
- Constructing a text classifier using RNNs and LSTMs
- Sequence-to-sequence models for machine translation concepts
- Attention mechanisms and their role in modern NLP
- Practical NLP utilising 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
- Constructing an LSTM-based decoder for caption generation
- Managing multiple input layers in the Keras functional API
- Training and evaluating the 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 ideas
Requirements
- Basic competence in Python programming (functions, loops, dictionaries, arrays)
- Familiarity with programming concepts such as variables, conditionals, and data structures
- No previous experience in deep learning or machine learning is required
Audience
- Software developers and engineers pivoting towards AI and machine learning
- Data analysts and scientists looking to acquire deep learning skills
- Technical professionals aiming to comprehend and implement neural network models
- Students and researchers embarking on their deep learning journey
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
