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Duration 8 hours
Course Outline
Introduction to Deep Learning
- Distinguishing deep learning from traditional machine learning
- Real-world applications in computer vision, NLP, and other domains
- Survey of the deep learning ecosystem: TensorFlow 2.x, Keras, and 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 optimization and backpropagation
- Training a initial neural network using the MNIST dataset
Convolutional Neural Networks for Computer Vision
- Comprehending convolution, filters, and feature maps
- Pooling layers and dimensionality reduction techniques
- CNN architectures: Understanding LeNet, VGG, and ResNet concepts
- Constructing and training a CNN for image classification
- Visualizing learned features and intermediate activations
Data Augmentation and Improving Model Accuracy
- How data augmentation mitigates overfitting and enhances 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 concept and effectiveness of transfer learning
- Loading pre-trained models from Keras Applications (e.g., 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 addressing the vanishing gradient problem
- Using LSTM and GRU cells for 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
- Developing 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 utilizing TensorFlow 2.x text processing APIs
Final Project: Image Captioning
- Merging computer vision and NLP within a multimodal architecture
- Extracting image features using 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 complete captioning pipeline
Next Steps and Resources
- Deploying trained models with 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 (including functions, loops, dictionaries, and arrays)
- Familiarity with core 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 moving into the fields of AI and machine learning
- Data analysts and data scientists aiming to acquire deep learning competencies
- Technical professionals interested in understanding and applying 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
