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
