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Course Outline
Introduction to Deep Learning
- Distinguishing deep learning from traditional machine learning
- Real-world use cases in computer vision, NLP, and other areas
- Survey of the deep learning ecosystem: TensorFlow 2.x, Keras, PyTorch
- Establishing a GPU-accelerated development environment
Deep Learning Mechanics
- Artificial neurons, activation functions, and network layers
- Forward propagation for making predictions
- Loss functions for classification and regression tasks
- Gradient descent optimization and backpropagation
- Training your initial neural network on the MNIST dataset
Convolutional Neural Networks for Computer Vision
- Concepts of convolution, filters, and feature maps
- Pooling layers and dimensionality reduction techniques
- CNN architectures: Understanding LeNet, VGG, and ResNet
- Constructing and training a CNN for image classification
- Analyzing learned features and intermediate activations
Data Augmentation and Enhancing Model Accuracy
- The role of data augmentation in preventing overfitting and boosting generalization
- Image transformations: rotation, flipping, zooming, and cropping
- Setting up augmentation pipelines using Keras preprocessing layers
- Regularization techniques such as Dropout and Batch Normalization
- Tracking training progress with validation metrics and early stopping
Transfer Learning with Pre-Trained Models
- Comprehending the principles behind 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 minimal 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 capturing long-range dependencies
- Training a character-level text generation model
- Word embeddings and utilizing 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 using TensorFlow 2.x text processing APIs
Final Project: Image Captioning
- Integrating 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
- Handling multiple input layers via the Keras functional API
- Training and evaluating the complete 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 knowledge of Python programming (functions, loops, dictionaries, arrays)
- Understanding of 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 looking to acquire deep learning expertise
- Technical professionals interested in comprehending and applying neural network models
- Students and researchers starting their exploration of deep learning
8 Hours
Testimonials (2)
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
Magdalena - Samsung Electronics Polska Sp. z o.o.
Course - Deep Learning with TensorFlow 2
I really liked the end where we took the time to play around with CHAT GPT. The room was not set up the best for this- instead of one large table a couple of small ones so we could get into small groups and brainstorm would have helped
