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Duration 8 hours
Course Outline
Introduction to Deep Learning
- Defining deep learning and distinguishing it from traditional machine learning.
- Real-world applications in computer vision, NLP, and other fields.
- Overview of the deep learning ecosystem: TensorFlow 2.x, Keras, and PyTorch.
- Setting up 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 first neural network on the MNIST dataset.
Convolutional Neural Networks for Computer Vision
- Understanding convolution, filters, and feature maps.
- Pooling layers and dimensionality reduction techniques.
- CNN architectures: Concepts behind LeNet, VGG, and ResNet.
- Building and training a CNN for image classification.
- Visualising learned features and intermediate activations.
Data Augmentation and Improving Model Accuracy
- Understanding how data augmentation combats overfitting and improves generalisation.
- Image transformations: rotation, flipping, zooming, and cropping.
- Implementing augmentation pipelines using Keras preprocessing layers.
- Regularisation techniques such as dropout and batch normalization.
- Monitoring training progress with validation metrics and early stopping.
Transfer Learning with Pre-Trained Models
- Understanding the principles of 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 Modeling
- Introduction to sequential data and temporal dependencies.
- Recurrent neural networks (RNNs) and the vanishing gradient problem.
- LSTM and GRU cells for capturing long-range dependencies.
- Training a character-level text generation model.
- Word embeddings and the Embedding layer in Keras.
Natural Language Processing Fundamentals
- Text preprocessing: tokenization, padding, and vocabulary building.
- Building a text classifier using RNNs and LSTMs.
- Concepts of sequence-to-sequence models for machine translation.
- Attention mechanisms and their role in modern NLP.
- Practical NLP using TensorFlow 2.x text processing APIs.
Final Project: Image Captioning
- Integrating computer vision and NLP within a multimodal architecture.
- Extracting image features using a pre-trained CNN encoder.
- Building an LSTM-based decoder for caption generation.
- Managing multiple input layers with 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 proficiency in Python programming (including functions, loops, dictionaries, and arrays).
- Familiarity with core programming concepts such as variables, conditionals, and data structures.
- No prior experience in deep learning or machine learning is required.
Target Audience
- Software developers and engineers transitioning into AI and machine learning.
- Data analysts and data scientists aiming to acquire deep learning skills.
- Technical professionals keen on understanding and applying neural network models.
- Students and researchers beginning their journey in deep learning.
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
