Deep Learning with Keras (2 days)
Use python to build deep learning models for regression and classification. Learn how to create, train, evaluate and tune neural networks using Python and Keras.
Prerequisites
- Intermediate to advanced knowledge of Python programming, Jupyter notebooks, pandas and numpy.
Contents
Introduction to Machine Learning, Deep Learning, and Generative AI.
- Machine Learning vs Deep Learning vs Generative AI
- Supervised, Unsupervised and Reinfoircement Learning
- Strucutre of artificial neural networks
Overview of machine learning and deep learning concepts.
- Introduction to deep learning and its applications
- Weights and Bias
- Cost functions
- Back propagation
Building blocks of neural networks.
- Hands-on exercises using Keras for regression and classification.
- Perceptrons
- Activation Functions
- Weights and Bias
- Cost functions
- Back propagation
- Test, Train and Validation Data
Image processing.
- Convolutional Neural Networks
- CNN Hyperparameters
- LeNet-5 Architecture
- CNN Design
- Residual Networks
- Transfer Learning
Natural language processing.
- NLP Definitions
- Embeddings and Vectors
- Classification Metrics
- NLP Deep Learning Architectures
- CNN, RNN, LSTM Architectures
Advanced topics in deep learning.
- Guidance on further exploration and resources
- Where to source Data
- Hyperparameter tuning
- Popular Deep Learning Libraries
- Software 2.0
- Advances in Technology
