Supervised Learning for AI with Python and Tensorflow 2

Supervised Learning for AI with Python and Tensorflow 2, available at $64.99, has an average rating of 4.5, with 113 lectures, based on 35 reviews, and has 3564 subscribers.

You will learn about 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 This course is ideal for individuals who are Beginner Python programmers curious about Artificial Intelligence or People looking for an AI course that teaches both the theoretical and practical aspects of Artificial Intelligence It is particularly useful for Beginner Python programmers curious about Artificial Intelligence or People looking for an AI course that teaches both the theoretical and practical aspects of Artificial Intelligence.

Enroll now: Supervised Learning for AI with Python and Tensorflow 2

Summary

Title: Supervised Learning for AI with Python and Tensorflow 2

Price: $64.99

Average Rating: 4.5

Number of Lectures: 113

Number of Published Lectures: 113

Number of Curriculum Items: 113

Number of Published Curriculum Objects: 113

Original Price: $199.99

Quality Status: approved

Status: Live

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

Who Should Attend

Beginner Python programmers curious about Artificial Intelligence
People looking for an AI course that teaches both the theoretical and practical aspects of Artificial Intelligence

Target Audiences

Beginner Python programmers curious about Artificial Intelligence
People looking for an AI course that teaches both the theoretical and practical aspects of Artificial Intelligence

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

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Syllabus

Lecture 3: Setup Coding Environment Resources

Lecture 4: Setup Coding Environment

Chapter 2: The Basics

Lecture 1: Artificial Intelligence Machine Learning Supervised Learning

Lecture 2: Parameters and threshold function

Lecture 3: Simple parametric model lab

Lecture 4: Model Intuition and Lab

Lecture 5: Learning rate and code clean up

Lecture 6: A gentle introduction to vectors

Lecture 7: Vectorization Lab

Lecture 8: What is a Bias Node

Lecture 9: Bias Node and Dynamic Decision Boundary Lab

Lecture 10: The Perceptron Algorithm and Lab

Lecture 11: Non-Binary Inputs and Feature Scaling

Lecture 12: Working with Real Data

Lecture 13: Working with Real Data Lab Part 1

Lecture 14: Working with Real Data Lab Part 2

Lecture 15: Saving and Loading Weights

Lecture 16: Training Improvements

Lecture 17: Classification vs Regression

Lecture 18: 019 – Limitations of Perceptrons

Chapter 3: Feedforward Neural Networks

Lecture 1: Introduction to Neural Networks

Lecture 2: Logistic Regression Overview

Lecture 3: A Gentle Introduction to Derivatives

Lecture 4: Gradient Descent

Lecture 5: Logistic Regression Equations

Lecture 6: Logistic Regression Lab

Lecture 7: Introduction to Matrices

Lecture 8: Further Vectorization for Logistic Regression Lab

Lecture 9: Notation for Neural Networks

Lecture 10: Forward Propagation

Lecture 11: Forward Propagation Lab

Lecture 12: Backpropagation

Lecture 13: Back Propagation Equation Derivations

Lecture 14: Backpropagation Lab

Lecture 15: Understanding Hidden Layers

Lecture 16: Weight Intialization

Lecture 17: Multi-Task and Multi-Class Classification

Lecture 18: Derivatives of Softmax and Categorical Cross Entropy

Lecture 19: Multi-Class Classification Lab

Lecture 20: The Vanishing Gradient Problem and ReLu Activation Function

Lecture 21: Relu Lab

Lecture 22: Confusion Matrix Analysis

Lecture 23: Overfitting

Lecture 24: Batching Theory

Lecture 25: Batching Lab

Lecture 26: Code Cleanup

Lecture 27: Optimizers – Momentum

Lecture 28: Optimizers – Momentum Lab

Lecture 29: Optimizers – RMS prop

Lecture 30: RMSprop Lab

Lecture 31: Optimizers – Adam

Lecture 32: Optimizers – Adam Lab

Chapter 4: Convolutional Neural Networks

Lecture 1: CNN Section Overview

Lecture 2: Image Data

Lecture 3: Filters

Lecture 4: Padding

Lecture 5: Strides

Lecture 6: Reshaping

Lecture 7: Introducton to Convolutional Neural Networks

Lecture 8: Convolutional Neural Networks Forward Propagation

Lecture 9: CNN Forward Propagation Lab Part 1 – Parameter Initialization

Lecture 10: CNN Forward Propagation Lab Part 2 – Forward Propagation Method

Lecture 11: CNN Forward Propagation Lab Part 3 – Extract Patches and Test

Lecture 12: Convolutional Neural Networks Backpropagation

Lecture 13: Convolutional Neural Networks Backpropagation Lab

Lecture 14: Pooling Layers

Lecture 15: Pooling Lab Part 1 Forward Propagation (optional)

Lecture 16: Pooling Lab Part 2 – Backpropagation (optional)

Lecture 17: Introduction to Tensorflow Keras Part 1

Lecture 18: Introduction to Tensorflow Keras Part 2

Lecture 19: Creating a Custom Image Dataset – Part 1 Data Preparation

Lecture 20: Creating a Custom Image Dataset – Part 2 Creating a Tensorflow Record

Lecture 21: Using Tensorflow Records for Training

Lecture 22: A Brief History of CNNs for Image Classifications

Lecture 23: AlexNet Implementation Part 1 Data Preparation

Lecture 24: AlexNet Implementation part 2 Model Definition

Lecture 25: Transfer Learning

Lecture 26: Occlusion Sensitivity

Lecture 27: Style Transfer

Lecture 28: Style Transfer Lab Part 1 – Setup

Lecture 29: Style Transfer Lab Part 2 – Gram Matrix and Losses

Lecture 30: Style Transfer Lab Part 3 – Training and Results

Lecture 31: One Shot Learning Overview

Lecture 32: Face Verification and Recognition Lab

Lecture 33: Object Detection Architecture and Label Format

Lecture 34: Object Detection Loss Function.mp4

Lecture 35: Object Detection Lab Part 1 – Setup

Lecture 36: Object Detection Lab Part 2 – Label Creation Loss Function and Training

Lecture 37: Object Detection Making Predictions and Evaluating

Lecture 38: Object Detection Lab Part 3 – Extracting Predictions

Lecture 39: Object Detection Lab Part 4 – Non-max Suppression

Lecture 40: Object Detection Lab Part 5 – F1 Score

Lecture 41: CNN Section Summary

Chapter 5: Sequential Data

Instructors

Jeremy Richard Lai Hong
Data Scientist and Software Engineer

Rating Distribution

1 stars: 0 votes
2 stars: 1 votes
3 stars: 0 votes
4 stars: 12 votes
5 stars: 22 votes

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