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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
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
