Supervised Learning for AI with Python and Tensorflow 2

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