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Duration 8 hours
Course Outline
Introduction to Deep Learning
- Differentiating deep learning from traditional machine learning approaches
- Practical 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
Core Mechanics of Deep Learning
- Artificial neurons, activation functions, and network layers
- Forward propagation and the process of computing predictions
- Loss functions utilized for classification and regression tasks
- Gradient descent optimization techniques and backpropagation
- Training your initial neural network using the MNIST dataset
Convolutional Neural Networks for Computer Vision
- Concepts of convolution, filters, and feature maps
- Pooling layers and techniques for dimensionality reduction
- CNN architectures: Understanding LeNet, VGG, and ResNet principles
- Constructing and training a CNN for image classification
- Visualizing learned features and intermediate activations
Data Augmentation and Enhancing Model Accuracy
- Understanding how data augmentation mitigates overfitting and boosts generalization
- Image transformation techniques: rotation, flipping, zooming, and cropping
- Implementing augmentation pipelines using Keras preprocessing layers
- Regularization methods such as dropout and batch normalization
- Monitoring training progress via validation metrics and early stopping strategies
Transfer Learning with Pre-Trained Models
- Grasping the concept of transfer learning and its effectiveness
- 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 restricted training data
Recurrent Networks and Sequence Modeling
- Introduction to sequential data and temporal dependencies
- Recurrent Neural Networks (RNNs) and the vanishing gradient challenge
- LSTM and GRU cells for capturing long-range dependencies
- Training a character-level text generation model
- Word embeddings and the Embedding layer within Keras
Fundamentals of Natural Language Processing
- Text preprocessing: tokenization, padding, and vocabulary construction
- Developing a text classifier using RNNs and LSTMs
- Concepts of sequence-to-sequence models for machine translation
- Attention mechanisms and their critical role in contemporary NLP
- Practical NLP implementation with 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
- Designing an LSTM-based decoder for caption generation
- Managing multiple input layers in the Keras functional API
- Training and evaluating the complete 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
- Foundational proficiency in Python programming (including functions, loops, dictionaries, and arrays)
- Understanding of core programming concepts such as variables, conditionals, and data structures
- No prior 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 scientists looking to acquire deep learning capabilities
- Technical professionals seeking to comprehend and apply neural network models
- Students and researchers starting their exploration into 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
