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Duration 8 hours
Course Outline
Introduction to Deep Learning
- Definition of deep learning and its distinctions from traditional machine learning
- Practical 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 computation
- Loss functions tailored for classification and regression tasks
- Gradient descent optimization and backpropagation algorithms
- Training your inaugural neural network using the MNIST dataset
Convolutional Neural Networks for Computer Vision
- Grasping convolution, filters, and feature maps
- Pooling layers and dimensionality reduction techniques
- CNN architectures: Concepts behind LeNet, VGG, and ResNet
- Constructing and training a CNN for image classification
- Visualizing learned features and intermediate activations
Data Augmentation and Improving Model Accuracy
- How data augmentation prevents overfitting and enhances generalization
- Image transformations: rotation, flipping, zooming, and cropping
- Implementing augmentation pipelines using Keras preprocessing layers
- Dropout, batch normalization, and additional regularization methods
- 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 while 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 addressing 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 construction
- Building a text classifier utilizing RNNs and LSTMs
- Sequence-to-sequence models for machine translation concepts
- Attention mechanisms and their significance in modern NLP
- Practical NLP implementation using 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
- Developing 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
- Fundamental proficiency in Python programming (functions, loops, dictionaries, arrays)
- Understanding of core programming concepts such as variables, conditionals, and data structures
- No previous experience with deep learning or machine learning is necessary
Audience
- Software developers and engineers moving into AI and machine learning fields
- Data analysts and scientists looking to acquire deep learning competencies
- Technical experts interested in comprehending 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
