Deep Learning & Neural Networks Python Training

StudyHub

The Deep Learning & Neural Networks Python Training course has been prepared by focusing largely on career readiness. It has been designed by our specialists in a manner that you will be likely to find yourself head and shoulders above the others. For better learning, one to one assistance will also be provided with Deep Learning & Neural Networks Python Training course, if it’s required by any learners.

Primary course of this bundle, Deep Learning & Neural Networks Python - Keras Course Curriculum:

• Course Introduction and Table of Contents
• Deep Learning Overview
• Choosing Between ML or DL for the next AI project - Quick Theory Session
• Preparing Your Computer
• Python Basics
• Theano Library Installation and Sample Program to Test
• TensorFlow library Installation and Sample Program to Test
• Keras Installation and Switching Theano and TensorFlow Backends
• Explaining Multi-Layer Perceptron Concepts
• Explaining Neural Networks Steps and Terminology
• First Neural Network with Keras - Understanding Pima Indian Diabetes Dataset
• Explaining Training and Evaluation Concepts
• Pima Indian Model - Steps Explained
• Coding the Pima Indian Model
• Pima Indian Model - Performance Evaluation
• Pima Indian Model - Performance Evaluation - k-fold Validation - Keras
• Pima Indian Model - Performance Evaluation - Hyper Parameters
• Understanding Iris Flower Multi-Class Dataset
• Developing the Iris Flower Multi-Class Model
• Understanding the Sonar Returns Dataset
• Developing the Sonar Returns Model
• Sonar Performance Improvement - Data Preparation - Standardization
• Sonar Performance Improvement - Layer Tuning for Smaller Network
• Sonar Performance Improvement - Layer Tuning for Larger Network
• Understanding the Boston Housing Regression Dataset
• Developing the Boston Housing Baseline Model
• Boston Performance Improvement by Standardization
• Boston Performance Improvement by Deeper Network Tuning
• Boston Performance Improvement by Wider Network Tuning
• Save & Load the Trained Model as JSON File (Pima Indian Dataset)
• Save and Load Model as YAML File - Pima Indian Dataset
• Load and Predict using the Pima Indian Diabetes Model
• Load and Predict using the Iris Flower Multi-Class Model
• Load and Predict using the Sonar Returns Model
• Load and Predict using the Boston Housing Regression Model
• An Introduction to Checkpointing
• Checkpoint Neural Network Model Improvements
• Checkpoint Neural Network Best Model
• Loading the Saved Checkpoint
• Plotting Model Behavior History
• Dropout Regularization - Visible Layer
• Dropout Regularization - Hidden Layer
• Learning Rate Schedule using Ionosphere Dataset - Intro
• Time Based Learning Rate Schedule
• Drop Based Learning Rate Schedule
• Convolutional Neural Networks - Introduction
• MNIST Handwritten Digit Recognition Dataset
• MNIST Multi-Layer Perceptron Model Development
• Convolutional Neural Network Model using MNIST
• Large CNN using MNIST
• Load and Predict using the MNIST CNN Model
• Introduction to Image Augmentation using Keras
• Augmentation using Sample Wise Standardization
• Augmentation using Feature Wise Standardization & ZCA Whitening
• Augmentation using Rotation and Flipping
• Saving Augmentation
• CIFAR-10 Object Recognition Dataset - Understanding and Loading
• Simple CNN using CIFAR-10 Dataset
• Train and Save CIFAR-10 Model
• Load and Predict using CIFAR-10 CNN Model

Assessment

After studying the complete training, you will be able to participate in the course assessment, which is included in the course. After completing the assessment you can claim all courses pdf certificates for free.

• Experienced programmers seeking to enhance their skills in deep learning and neural networks.
• Beginners seeking to enter the exciting and rapidly growing field of AI.
• Business leaders seeking to gain a competitive edge through artificial intelligence.
• Analysts seeking to enhance their analytical skills and advance their career prospects.
• Anyone seeking to unlock the power of artificial intelligence for themselves or their business.

There is no formal qualification needed for this course.

• AI Engineer: £45,000 - £120,000
• Machine Learning Engineer: £40,000 - £100,000
• Data Scientist: £35,000 - £100,000
• Software Developer: £25,000 - £70,000
• Research Scientist: £30,000 - £90,000

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