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Duration 8 hours
Course Outline
Introduction to Deep Learning
- Defining deep learning and its distinction from traditional machine learning.
- Real-world applications in computer vision, NLP, and other domains.
- Overview of the deep learning ecosystem: 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 prediction calculation.
- Loss functions for classification and regression tasks.
- Gradient descent optimization and backpropagation.
- Training your initial neural network using the MNIST dataset.
Convolutional Neural Networks for Computer Vision
- Understanding convolution, filters, and feature maps.
- Pooling layers and dimensionality reduction techniques.
- CNN architectures: Concepts of LeNet, VGG, and ResNet.
- Building and training a CNN for image classification.
- Visualizing learned features and intermediate activations.
Data Augmentation and Improving Model Accuracy
- How data augmentation combats overfitting and boosts generalization.
- Image transformations: rotation, flipping, zooming, and cropping.
- Implementing augmentation pipelines using Keras preprocessing layers.
- Regularization techniques such as dropout and batch normalization.
- Monitoring training progress via validation metrics and early stopping.
Transfer Learning with Pre-Trained Models
- Understanding the principles and benefits of transfer learning.
- 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 restricted 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 managing 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 importance in modern NLP.
- Practical NLP implementation with TensorFlow 2.x text processing APIs.
Final Project: Image Captioning
- Integrating computer vision and NLP in a multimodal architecture.
- Extracting image features using a pre-trained CNN encoder.
- Constructing 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
- Fundamental proficiency in Python programming (functions, loops, dictionaries, arrays).
- Understanding of basic programming concepts such as variables, conditionals, and data structures.
- No previous experience in deep learning or machine learning is necessary.
Audience
- Software developers and engineers moving into the fields of AI and machine learning.
- Data analysts and data scientists aiming to acquire deep learning competencies.
- Technical professionals looking to comprehend and apply neural network models.
- Students and researchers starting their exploration of 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
