MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 ChGenre: eLearning | Language: English + srt | Duration: 40 lectures (10h 6m) | Size: 4.94 GB

Machine Learning, Neural Networks, Deep Learning, Deep Neural Networks (DNN), and Convolution Neural Networks (CNN)

In depth understanding of Machine Learning.

In depth understanding of the Neural Network.
Detailed and step by step theoretical derivation and explanation of a majority of the topics to ensure clear understanding of the subject.
You will learn Linear Regression, Logistic Regression, Neural Network, Deep Neural Network (DNN), Convolution Neural Network etc.
Multiple hands-on projects using Tensorflow 2 and Python to expose you to some of the highly advanced topics of Tensorflow 2
Hands-on projects are selected to make you familiar with some of the expertise that may be very useful should you need to run a very long analysis in future.

For Theory Section: Some knowledge in algebra and calculus.
For Hands-On Section: Gmail account to use Google Colab and Google Drive. Basic programming knowledge using Python 3
If you have no programming background, you may still take the course. You can still benefit from the mathematical understanding of the subject. You can play with all the code that is provided.
All codes are provided for you to .
No software needed to install on your computer. We will use Google Colab with step-by-step set up instructions included in the course.

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 somes 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 .
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.

Who is this course for Almost for everyone. Machine Learning is not a topic for one single profession. Machine Learning (along with neural networks) is an immensely powerful tool that may help you to find solutions to some of the problems that one may not know how to solve otherwise. Try this course and see if it gives you better insight to address some of the problems you are working on.
People from a diverse range of professions may find this knowledge useful in their own profession.
Topics are explained in detail. Concepts are developed progressively in a step by step manner. I somes 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.
Please watch the first two videos to have a better understanding of the course.




DOWNLOAD
uploadgig.com