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Duration 8 hours
Course Outline
Introduction to Deep Learning
- Distinguishing deep learning from traditional machine learning methods
- Practical applications in computer vision, NLP, and other areas
- Survey of the deep learning ecosystem: TensorFlow 2.x, Keras, PyTorch
- Configuring a GPU-accelerated development workspace
The Mechanics of Deep Learning
- Artificial neurons, activation functions, and network layers
- Forward propagation and prediction computation
- 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
- Gaining insight into convolution, filters, and feature maps
- Pooling layers and reducing dimensionality
- CNN architectures: Concepts behind LeNet, VGG, and ResNet
- Developing and training a CNN for image classification
- Visualizing learned features and intermediate activations
Data Augmentation and Enhancing Model Accuracy
- How data augmentation mitigates overfitting and boosts generalization
- Image transformations: rotation, flipping, zooming, and cropping
- Creating augmentation pipelines with Keras preprocessing layers
- Dropout, batch normalization, and additional regularization methods
- Tracking training progress using validation metrics and early stopping
Transfer Learning with Pre-Trained Models
- Understanding the concept and benefits of transfer learning
- Accessing 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
- Attaining 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 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
- Developing a text classifier using RNNs and LSTMs
- Sequence-to-sequence models for machine translation concepts
- Attention mechanisms and their importance in modern 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 via a pre-trained CNN encoder
- Developing an LSTM-based decoder for caption generation
- Handling multiple input layers in the Keras functional API
- Training and assessing 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
- Foundational skills in Python programming (functions, loops, dictionaries, arrays)
- Understanding of programming fundamentals including variables, conditionals, and data structures
- No previous experience with deep learning or machine learning is necessary
Target Audience
- Software developers and engineers moving into AI and machine learning fields
- Data analysts and scientists looking to acquire deep learning competencies
- Technical professionals aiming to understand 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
