Deep Learning and Neural Networks using Python – Keras: The Complete Beginners Guide

Deep Learning and Neural Networks using Python – Keras: The Complete Beginners Guide
Deep Learning and Neural Networks using Python – Keras: The Complete Beginners Guide
English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 11h 07m | 4.22 GB

Deep learning and data science using a Python and Keras library – The complete guide from beginner to professional

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. Ranging from Google suggestions, to translations, ads, movie recommendations, friend suggestions, sales and customer experiences. There are tons of other applications too so there’s no wonder that deep learning and machine learning specialists, along with data science practitioners, are the most sought-after talent in the current technology world. But the problem is that, when you think about learning these technologies, there is a common misconception that it’s a prerequisite to study lots of maths, statistics, and complex algorithms. It’s almost like someone making you believe that you must learn the working of an internal combustion engine before you learn how to drive a car. The fact is that, to drive a car, we just only need to know how to use the user-friendly control pedals extending from the engine like the clutch, brake, accelerator, steering wheel, and so on. And with a bit of experience, you can easily drive a car. The basic know-how about the internal working of the engine is of course an added advantage while driving a car, but it’s not mandatory.

Similarly, in our deep learning course, we have a perfect balance between learning the basic concepts and 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 that we can use easily to build an efficient deep-learning model. Let’s see how this course is organized and an overview about the list of topics included. Overall, this is a basic to advanced crash course in deep learning neural networks and convolutional neural networks using Keras and Python. Once completed, it’s sure to sky-rocket your current career prospects as this in-demand skill is the technology of the future. There is a day in the near future itself, when deep learning models will out-perform human intelligence. So be ready and let’s dive into the world of thinking machines.

Exhaustive and packed with step-by-step instructions, working examples, and helpful advice, this course is divided into clear chunks so you can learn at your own pace and focus on your own area of interest.


  • Deep learning
  • Neural networks using Python
Table of Contents

1 Course Intro and Table of Contents
2 Code Password
3 Chosing ML or DL for your project
4 Preparing Your Computer
5 Python Basics
6 Installing Theano Library and Sample Program to Test
7 TensorFlow library Installation and Sample Program to Test
8 Keras Installation and Switching Theano and TensorFlow Backends
9 Multi-Layer Perceptron Concepts
10 Training Neural Network – Steps and Terminology
11 First Neural Network with Keras – Understanding Pima Indian Dataset
12 Training and Evaluation Concepts Explained
13 Pima Indian Model – Steps Explained
14 Pima Indian Model – Performance Evaluation
15 Understanding Iris Flower Dataset
16 Developing the Iris Flower Model
17 Understanding the Sonar Returns Dataset
18 Developing the Sonar Returns Model
19 Sonar Model Perfomance Improvement
20 Understanding the Boston Housing Dataset
21 Developing the Boston Housing Baseline Model
22 Boston Performance Improvement
23 Save the Trained Model as JSON File (Pima Indian Dataset)
24 Save and Load Model as YAML File – Pima Indian Dataset
25 Load and Predict using the Pima Indian Model
26 Save Load and Predict using Iris Flower Dataset
27 Save Load and Predict using Sonar Dataset
28 Save Load and Predict using Boston Dataset
29 Checkpointing Models
30 Plotting Model Behaviour History
31 Dropout Regularisation
32 Learning Rate Schedule using Ionosphere Dataset
33 Convolutional Neural Networks – Introduction
34 Downloading the MNIST Handwritten Digit Dataset
35 Multi-Layer Perceptron Model using MNIST
36 Convolutional Neural Network Model using MNIST
37 Convolutional Neural Network Model using MNIST – Part 2
38 Large CNN using MNIST
39 Load Save and Predict using MNIST
40 Introduction to Image Augmentation using Keras
41 Augmentation using Sample Wise Standardization
42 Augmentation using Feature Wise Standardization and ZCA Whitening
43 Augmentation using Rotation and Flipping
44 Saving Augmentation for MNIST
45 CIFAR-10 Object Recognition Dataset – Understanding and Loading
46 Simple CNN using CIFAR-10 Dataset
47 Simple CNN using CIFAR-10 Dataset – Part 2
48 Simple CNN using CIFAR-10 Dataset – Coding
49 Train and Save CIFAR-10 Model
50 Load and Predict using CIFAR-10 CNN Model