Uncover the Concepts and Techniques to Build and Train your own Artificial Intelligence Models
Gain a deep understanding of Supervised Learning techniques by studying the fundamentals and implementing them in NumPy.
Gain hands-on experience using popular Deep Learning frameworks such as Tensorflow 2 and Keras.
Section 1 - The Basics:
- Learn what Supervised Learning is, in the context of AI
- Learn the difference between Parametric and non-Parametric models
- Learn the fundamentals: Weights and biases, threshold functions and learning rates
- An introduction to the Vectorization technique to help speed up our self implemented code
- Learn to process real data: Feature Scaling, Splitting Data, One-hot Encoding and Handling missing data
- Classification vs Regression
Section 2 - Feedforward Networks:
- Learn about the Gradient Descent optimization algorithm.
- Implement the Logistic Regression model using NumPy
- Implement a Feedforward Network using NumPy
- Learn the difference between Multi-task and Multi-class Classification
- Understand the Vanishing Gradient Problem
- Overfitting
- Batching and various Optimizers (Momentum, RMSprop, Adam)
Section 3 - Convolutional Neural Networks:
- Fundamentals such as filters, padding, strides and reshaping
- Implement a Convolutional Neural Network using NumPy
- Introduction to Tensorfow 2 and Keras
- Data Augmentation to reduce overfitting
- Understand and implement Transfer Learning to require less data
- Analyse Object Classification models using Occlusion Sensitivity
- Generate Art using Style Transfer
- One-Shot Learning for Face Verification and Face Recognition
- Perform Object Detection for Blood Stream images
Section 4 - Sequential Data
- Understand Sequential Data and when data should be modeled as Sequential Data
- Implement a Recurrent Neural Network using NumPy
- Implement LSTM and GRUs in Tensorflow 2/Keras
- Sentiment Classification from the basics to the more advanced techniques
- Understand Word Embeddings
- Generate text similar to Romeo and Juliet
- Implement an Attention Model using Tensorflow 2/Keras
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