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Course Outline
Introduction to Deep Learning
- What is deep learning and how it differs from traditional machine learning
- Real-world applications in computer vision, NLP, and beyond
- Overview of the deep learning ecosystem: TensorFlow 2.x, Keras, 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: LeNet, VGG, and ResNet concepts
- Building and training a CNN for image classification
- Visualizing learned features and intermediate activations
Data Augmentation and Improving Model Accuracy
- Why data augmentation combats overfitting and improves generalization
- Image transformations: rotation, flipping, zooming, and cropping
- Implementing augmentation pipelines with Keras preprocessing layers
- Dropout, batch normalization, and other regularization techniques
- Monitoring training with validation metrics and early stopping
Transfer Learning with Pre-Trained Models
- Understanding transfer learning and why it works
- 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 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 with TensorFlow 2.x text processing APIs
Final Project: Image Captioning
- Combining computer vision and NLP in 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 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
- Basic proficiency in Python programming (functions, loops, dictionaries, arrays)
- Familiarity with programming concepts such as variables, conditionals, and data structures
- No prior deep learning or machine learning experience required
Audience
- Software developers and engineers transitioning into AI and machine learning
- Data analysts and data scientists seeking deep learning skills
- Technical professionals who want to understand and apply neural network models
- Students and researchers beginning their journey in deep learning
8 Hours
Testimonials (2)
Interactivity of the training. We experimented a lot.
Lidia Opuchlik - Orange Szkolenia
Course - Deep Reinforcement Learning with Python
Machine Translated
A wealth of expert knowledge, clear and specific answers from the trainer to questions. Content tailored to our needs. Well-organized knowledge.
Augustyn - Samsung Electronics Polska Sp. z o.o.
Course - Deep Learning with TensorFlow 2
Machine Translated
