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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
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
