Supervised Learning for AI with Python and Tensorflow 2

Uncover the Concepts and Techniques to Build and Train your own Artificial Intelligence Models

What you will learn

The basics of supervised learning: What are parameters, What is a bias node, Why do we use a learning rate

Techniques for dealing with data: How to Split Datasets, One-hot Encoding, Handling Missing Values

Vectors, matrices and creating faster code using Vectorization

Mathematical concepts such as Optimization, Derivatives and Gradient Descent

Gain a deep understanding behind the fundamentals of Feedforward, Convolutional and Recurrent Neural Networks

Build Feedforward, Convolutional and Recurrent Neural Networks using only the fundamentals

How to use Tensorflow 2.0 and Keras to build models, create TFRecords and save and load models

Practical project: Style Transfer – Use AI to draw an image in the style of your favorite artist

Practical project: Object Detection – Use AI to Detect the bounding box locations of objects inside of images

Practical project: Transfer Learning – Learn to leverage large pretrained AI models to work on new datasets

Practical project: One-Shot Learning – Learn to build AI models to perform tasks such as Face recognition

Practical project: Text Generation – Build an AI model to generate text similar to Romeo and Juliet

Practical project: Sentiment Classification – Build an AI model to determine whether text is overall negative or positive

Practical project: Attention Model – Build an attention model to build an interpretable AI model

Description

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:

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– 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

English

language

Content

Introduction

Syllabus

Setup Coding Environment Resources

Setup Coding Environment

The Basics

Artificial Intelligence Machine Learning Supervised Learning

Parameters and threshold function

Simple parametric model lab

Model Intuition and Lab

Learning rate and code clean up

A gentle introduction to vectors

Vectorization Lab

What is a Bias Node

Bias Node and Dynamic Decision Boundary Lab

The Perceptron Algorithm and Lab

Non-Binary Inputs and Feature Scaling

Working with Real Data

Working with Real Data Lab Part 1

Working with Real Data Lab Part 2

Saving and Loading Weights

Training Improvements

Classification vs Regression

019 – Limitations of Perceptrons

Feedforward Neural Networks

Introduction to Neural Networks

Logistic Regression Overview

A Gentle Introduction to Derivatives

Gradient Descent

Logistic Regression Equations

Logistic Regression Lab

Introduction to Matrices

Further Vectorization for Logistic Regression Lab

Notation for Neural Networks

Forward Propagation

Forward Propagation Lab

Backpropagation

Back Propagation Equation Derivations

Backpropagation Lab

Understanding Hidden Layers

Weight Intialization

Multi-Task and Multi-Class Classification

Derivatives of Softmax and Categorical Cross Entropy

Multi-Class Classification Lab

The Vanishing Gradient Problem and ReLu Activation Function

Relu Lab

Confusion Matrix Analysis

Overfitting

Batching Theory

Batching Lab

Code Cleanup

Optimizers – Momentum

Optimizers – Momentum Lab

Optimizers – RMS prop

RMSprop Lab

Optimizers – Adam

Optimizers – Adam Lab

Convolutional Neural Networks

CNN Section Overview

Image Data

Filters

Padding

Strides

Reshaping

Introducton to Convolutional Neural Networks

Convolutional Neural Networks Forward Propagation

CNN Forward Propagation Lab Part 1 – Parameter Initialization

CNN Forward Propagation Lab Part 2 – Forward Propagation Method

CNN Forward Propagation Lab Part 3 – Extract Patches and Test

Convolutional Neural Networks Backpropagation

Convolutional Neural Networks Backpropagation Lab

Pooling Layers

Pooling Lab Part 1 Forward Propagation (optional)

Pooling Lab Part 2 – Backpropagation (optional)

Introduction to Tensorflow Keras Part 1

Introduction to Tensorflow Keras Part 2

Creating a Custom Image Dataset – Part 1 Data Preparation

Creating a Custom Image Dataset – Part 2 Creating a Tensorflow Record

Using Tensorflow Records for Training

A Brief History of CNNs for Image Classifications

AlexNet Implementation Part 1 Data Preparation

AlexNet Implementation part 2 Model Definition

Transfer Learning

Occlusion Sensitivity

Style Transfer

Style Transfer Lab Part 1 – Setup

Style Transfer Lab Part 2 – Gram Matrix and Losses

Style Transfer Lab Part 3 – Training and Results

One Shot Learning Overview

Face Verification and Recognition Lab

Object Detection Architecture and Label Format

Object Detection Loss Function.mp4

Object Detection Lab Part 1 – Setup

Object Detection Lab Part 2 – Label Creation Loss Function and Training

Object Detection Making Predictions and Evaluating

Object Detection Lab Part 3 – Extracting Predictions

Object Detection Lab Part 4 – Non-max Suppression

Object Detection Lab Part 5 – F1 Score

CNN Section Summary

Sequential Data

Sequential Data Overview

Recurrent Neural Networks

Forward Propagation for RNNs

Data Prep and Forward Propagation Lab

Backpropagation for RNNs

Backpropagation and vanishing Gradient Lab

LSTM Theory

RNNs, LSTMS and GRUs in Tensorflow Lab

Character Based Text Generation

Word Embeddings

Exploring GloVe Word Embeddings Lab

Advanced Sentiment Classification with GloVe

Advanced Sentiment Classification with BERT

Attention Models Theory

Attention Models Lab Part 1 Model Definition and Training

Attention Models Lab Part 2 Visualizing Attention

Sequential Data Summary

Conclusion

Thank you, and where to from here?

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