Deep Learning with Keras
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
