Supervised Learning for AI with Python and Tensorflow 2
What you'll 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
Requirements
- Secondary Level (High School) Mathematics
- Some basic programming experience in Python
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:
- 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
Who this course is for:
- Beginner Python programmers curious about Artificial Intelligence
- People looking for an AI course that teaches both the theoretical and practical aspects of Artificial Intelligence
Instructor
![Jeremy Richard Lai Hong](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 4.8 Instructor Rating
- 25 Reviews
- 3,430 Students
- 1 Course
Jeremy Richard Lai Hong was born and raised in South Africa and apologises in advance for his accent.
He has experience as both a Software Engineer and as a Data Scientist. He has practical working experience, having worked at a bank and for a brand management company.
He's worked on many projects including Optical Character Recognition, Topic Modelling, ATM cash forecasting, Object Tracking and Sentiment Analysis.