Machine Learning and Deep Learning Using TensorFlow
Artificial Intelligence (AI): Machine Learning, Deep Neural Networks (DNN), and Convolution Neural Networks (CNN)
Machine Learning and Deep Learning Using TensorFlow free download
Artificial Intelligence (AI): Machine Learning, Deep Neural Networks (DNN), and Convolution Neural Networks (CNN)
If you are interested in Machine Learning, Neural Networks, Deep Learning, Deep Neural Networks (DNN), and Convolution Neural Networks (CNN) with an in-depth and clear understanding, then this course is for you.
Topics are explained in detail. Concepts are developed progressively in a step by step manner. I sometimes spent more than 10 minutes discussing a single slide instead of rushing through it. This should help you to be in sync with the material presented and help you better understand it.
The hands-on examples are selected primarily to make you familiar with some aspects of TensorFlow 2 or other skills that may be very useful if you need to run a large and complex neural network job of your own in the future.
Hand-on examples are available for you to download.
Please watch the first two videos to have a better understanding of the course.
TOPICS COVERED
What is Machine Learning?
Linear Regression
Steps to Calculate the Parameters
Linear Regression-Gradient Descent using Mean Squared Error (MSE) Cost Function
Logistic Regression: Classification
Decision Boundary
Sigmoid Function
Non-Linear Decision Boundary
Logistic Regression: Gradient Descent
Gradient Descent using Mean Squared Error Cost Function
Problems with MSE Cost Function for Logistic Regression
In Search for an Alternative Cost-Function
Entropy and Cross-Entropy
Cross-Entropy: Cost Function for Logistic Regression
Gradient Descent with Cross Entropy Cost Function
Logistic Regression: Multiclass Classification
Introduction to Neural Network
Logical Operators
Modeling Logical Operators using Perceptron(s)
Logical Operators using Combination of Perceptron
Neural Network: More Complex Decision Making
Biological Neuron
What is Neuron? Why Is It Called the Neural Network?
What Is An Image?
My “Math” CAT. Anatomy of an Image
Neural Network: Multiclass Classification
Calculation of Weights of Multilayer Neural Network Using Backpropagation Technique
How to Update the Weights of Hidden Layers using Cross Entropy Cost Function
Hands On
Google Colab. Setup and Mounting Google Drive (Colab)
Deep Neural Network (DNN) Based Image Classification Using Google Colab. & TensorFlow (Colab)
Introduction to Convolution Neural Networks (CNN)
CNN Architecture
Feature Extraction, Filters, Pooling Layer
Hands On
CNN Based Image Classification Using Google Colab & TensorFlow (Colab)
Methods to Address Overfitting and Underfitting Problems
Regularization, Data Augmentation, Dropout, Early Stopping
Hands On
Diabetes prediction model development (Colab)
Fixing problems using Regularization, Dropout, and Early Stopping (Colab)
Hands On: Various Topics
Saving Weights and Loading the Saved Weights (Colab)
How To Split a Long Run Into Multiple Smaller Runs
Functional API and Transfer Learning (Colab)
How to Extract the Output From an Intermediate Layer of an Existing Model (Colab), and add additional layers to it to build a new model.
