Deep Learning and Neural Networks using Python - Keras: The Complete Beginners Guide [Video]
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Course Intro and Table of Contents
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Deep Learning Overview
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Chosing ML or DL for your project
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Preparing Your Computer
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Python Basics
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Installing Theano Library and Sample Program to Test
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TensorFlow library Installation and Sample Program to Test
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Keras Installation and Switching Theano and TensorFlow Backends
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Multi-Layer Perceptron Concepts
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Training Neural Network - Steps and Terminology
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First Neural Network with Keras - Understanding Pima Indian Dataset
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Training and Evaluation Concepts Explained
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Pima Indian Model - Steps Explained
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Pima Indian Model - Performance Evaluation
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Understanding Iris Flower Dataset
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Developing the Iris Flower Model
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Understanding the Sonar Returns Dataset
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Developing the Sonar Returns Model
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Sonar Model Perfomance Improvement
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Understanding the Boston Housing Dataset
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Developing the Boston Housing Baseline Model
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Boston Performance Improvement
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Save the Trained Model as JSON File (Pima Indian Dataset)
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Save and Load Model as YAML File - Pima Indian Dataset
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Load and Predict using the Pima Indian Model
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Save Load and Predict using Iris Flower Dataset
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Save Load and Predict using Sonar Dataset
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Save Load and Predict using Boston Dataset
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Checkpointing Models
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Plotting Model Behaviour History
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Dropout Regularisation
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Learning Rate Schedule using Ionosphere Dataset
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Convolutional Neural Networks – Introduction
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Downloading the MNIST Handwritten Digit Dataset
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Multi-Layer Perceptron Model using MNIST
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Convolutional Neural Network Model using MNIST
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Convolutional Neural Network Model using MNIST - Part 2
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Large CNN using MNIST
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Load Save and Predict using MNIST
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Introduction to Image Augmentation using Keras
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Augmentation using Sample Wise Standardization
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Augmentation using Feature Wise Standardization and ZCA Whitening
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Augmentation using Rotation and Flipping
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Saving Augmentation for MNIST
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CIFAR-10 Object Recognition Dataset - Understanding and Loading
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Simple CNN using CIFAR-10 Dataset
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Simple CNN using CIFAR-10 Dataset - Part 2
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Simple CNN using CIFAR-10 Dataset – Coding
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Train and Save CIFAR-10 Model
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Load and Predict using CIFAR-10 CNN Model
About this video
The world has been obsessed with the terms machine learning and deep learning recently. We use these technologies every day with or without our knowledge through Google suggestions, translations, ads, movie recommendations, friend suggestions, and sales and customer experiences. There are tons of other applications too! No wonder that deep learning and machine learning specialists, along with data science practitioners, are the most sought-after talent in the technology world. However, it’s a common misconception that you need to study lots of mathematics, statistics, and complex algorithms for learning these technologies. It’s like believing that you must learn the working of a combustion engine before you learn how to drive a car. A basic know-how of the internal working of the engine is of course an added advantage, but it’s not mandatory.
Similarly, this course is a perfect balance between learning the basic deep learning concepts and implementing 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 of a car engine, which you can use to build an efficient deep-learning model. This is a basic-to-advanced crash course in deep learning, neural networks, and convolutional neural networks using Keras and Python. It’ll help your skill up to meet the demand of the tech world and skyrocket your career prospects.
All the code and supporting files for this course are available at https://github.com/PacktPublishing/Deep-Learning-and-Neural-Networks-using-Python---Keras-The-Complete-Beginners-Guide
- Publication date:
- May 2019
- Publisher
- Packt
- Duration
- 11 hours 0 minutes
- ISBN
- 9781838986476