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

– 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.