Updated on May, 2024
Language - English
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Deep Learning and Neural Networks Python Keras
Build deep learning models with Python and Keras.
Development,Python,Neural Networks
Lectures -88
Resources -4
Duration -11 hours
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Course Description
Deep Learning and Neural Networks Python Keras course is a basic to advanced crash course. The course teaches you deep learning neural networks and convolutional neural networks using Keras and Python. It is designed to skyrocket your current career prospects as this is the most in-demand skill today and for sure is the technology of the future.
Course Overview
The world has been revolving much around the terms "Machine Learning" and "Deep Learning" recently. With or without our knowledge every day we are using these technologies. There will be a day in the near future when deep learning models will outperform human intelligence.
Deep Learning and Machine Learning along with Data Science are a few of the most sought-after talents in the technology world nowadays. Its applications range from Google suggestions, translations, ads, movie recommendations, friend suggestions, sales and customer experience, and many more.
Learning these technologies involves a misconception that it needs prior knowledge of maths, statistics, complex algorithms, and formulas. The basic know-how is an added advantage but it is definitely not mandatory.
In this deep learning course, there is a perfect balance between learning the basic concepts along the implementation of the built-in Deep Learning Classes and functions from the Keras Library using the Python Programming Language.
These classes, functions, and APIs are just like the control pedals from the car engine, which we can use easily to build an efficient deep-learning model. Build deep learning models to solve a variety of problems, such as image classification, natural language processing, and speech recognition.
Goals
What will you learn in this course:
Understand the fundamentals of Neural Networks.
Master the basics of deep learning.
Understand the basics of machine learning.
Build and train deep learning models.
Use deep learning for problem-solving.
Prerequisites
What are the prerequisites for this course?
A medium-configuration computer.
Willingness to learn in order to indulge in the world of deep learning.
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
3 Lectures
-
Course Introduction and Table of Contents
11:29
11:29
-
Deep Learning Overview - Theory Session - Part 1
05:51
05:51
-
Deep Learning Overview - Theory Session - Part 2
06:30
06:30
Choosing Between ML or DL for the next AI project
1 Lectures
Preparing Your Computer
2 Lectures
Python Basics
4 Lectures
Environment Setup
8 Lectures
Explaining Multi-Layer Perceptron Concepts
1 Lectures
Explaining Neural Networks Steps and Terminology
1 Lectures
First Neural Network with Keras
1 Lectures
Explaining Training and Evaluation Concepts
1 Lectures
Pima Indian Model
8 Lectures
Iris Flower Multi-Class
4 Lectures
Sonar Returns Dataset
2 Lectures
Sonar Performance Improvement
3 Lectures
Boston Housing
2 Lectures
Boston Performance Improvement
3 Lectures
Pima Indian Dataset
4 Lectures
Load and Predict
3 Lectures
Checkpointing
4 Lectures
Plotting Model Behavior History
2 Lectures
Dropout Regularization
3 Lectures
Learning Rate Schedule using Ionosphere Dataset
1 Lectures
Time Based Learning Rate Schedule
2 Lectures
Drop Based Learning Rate Schedule
2 Lectures
Convolutional Neural Networks
2 Lectures
MNIST Handwritten Digit Recognition Dataset
2 Lectures
MNIST Multi-Layer Perceptron Model Development
2 Lectures
Convolutional Neural Network Model using MNIST
2 Lectures
Large CNN using MNIST
1 Lectures
Load and Predict using the MNIST CNN Model
1 Lectures
Augmentation
5 Lectures
CIFAR-10 Object Recognition Dataset
1 Lectures
Simple CNN using CIFAR-10 Dataset
3 Lectures
Train and Save CIFAR-10 Model
1 Lectures
Load and Predict using CIFAR-10 CNN Model
1 Lectures
SOURCE CODE ATTACHED
1 Lectures
Instructor Details
Abhilash Nelson
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