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Duration 8 hours
Course Outline
Introduction to Deep Learning
- Definition of deep learning and its distinction from traditional machine learning
- Real-world applications in computer vision, NLP, and other domains
- Survey of the deep learning landscape: 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 the computation of predictions
- Loss functions tailored for classification and regression tasks
- Gradient descent optimization and backpropagation techniques
- Training your initial neural network using the MNIST dataset
Convolutional Neural Networks for Computer Vision
- Comprehension of convolution, filters, and feature maps
- Pooling layers and dimensionality reduction strategies
- 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 Enhancing Model Accuracy
- Understanding how data augmentation mitigates overfitting and boosts generalization
- Image transformations: rotation, flipping, zooming, and cropping
- Implementing augmentation pipelines using Keras preprocessing layers
- Dropout, batch normalization, and other regularization methods
- Monitoring training progress via validation metrics and early stopping
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 despite limited 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 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 significance in contemporary NLP
- Practical NLP techniques 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 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)
- Familiarity with essential programming concepts like variables, conditionals, and data structures
- No previous experience in deep learning or machine learning is necessary
Target Audience
- Software developers and engineers moving into AI and machine learning fields
- Data analysts and scientists aiming to acquire deep learning competencies
- Technical professionals eager to comprehend and apply neural network models
- Students and researchers initiating 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
