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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 fields
- Overview of the deep learning ecosystem: TensorFlow 2.x, Keras, and PyTorch
- Setting up a GPU-accelerated development environment
The Mechanics of Deep Learning
- Artificial neurons, activation functions, and network layers
- Forward propagation and computing predictions
- Loss functions for classification and regression tasks
- Gradient descent optimization and backpropagation
- Training your first neural network on the MNIST dataset
Convolutional Neural Networks for Computer Vision
- Understanding convolution, filters, and feature maps
- Pooling layers and dimensionality reduction
- CNN architectures: Concepts from LeNet, VGG, and ResNet
- Building and training a CNN for image classification
- Visualizing learned features and intermediate activations
Data Augmentation and Enhancing Model Accuracy
- Why data augmentation combats overfitting and improves generalization
- Image transformations: rotation, flipping, zooming, and cropping
- Implementing augmentation pipelines using Keras preprocessing layers
- Dropout, batch normalization, and other regularization techniques
- Monitoring training progress with validation metrics and early stopping
Transfer Learning with Pre-Trained Models
- Understanding transfer learning and its effectiveness
- 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 limited 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 handling 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 with RNNs and LSTMs
- Sequence-to-sequence models for machine translation concepts
- Attention mechanisms and their role in modern NLP
- Practical NLP using TensorFlow 2.x text processing APIs
Final Project: Image Captioning
- Combining computer vision and NLP within a multimodal architecture
- Extracting image features with a pre-trained CNN encoder
- Building 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 with 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 (including functions, loops, dictionaries, and arrays)
- Understanding of core programming concepts such as variables, conditionals, and data structures
- No previous experience with deep learning or machine learning is necessary
Target Audience
- Software developers and engineers transitioning into AI and machine learning
- Data analysts and data scientists looking to acquire deep learning skills
- Technical professionals wishing to understand and apply neural network models
- Students and researchers starting their journey in 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
