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Supervised Learning for AI with Python and Tensorflow 2


Supervised Learning for AI with Python and Tensorflow 2 - 
Uncover the Concepts and Techniques to Build and Train your own Artificial Intelligence Models
  • Hot & New
  • Created by Jeremy Richard Lai Hong
  • English [Auto]

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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


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
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