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Data Science

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Convolutional Neural Networks in Python: CNN Computer Vision

Python for Computer Vision & Image Recognition - Deep Learning Convolutional Neural Network (CNN) - Keras & TensorFlow 2

4.32 (692 reviews)

78692

Students

7 hours

Content

Nov 2020

Last Update
$19.99
Regular Price

What you will learn

Get a solid understanding of Convolutional Neural Networks (CNN) and Deep Learning

Build an end-to-end Image recognition project in Python

Learn usage of Keras and Tensorflow libraries

Use Artificial Neural Networks (ANN) to make predictions

Use Pandas DataFrames to manipulate data and make statistical computations.




Description

You're looking for a complete Convolutional Neural Network (CNN) course that teaches you everything you need to create a Image Recognition model in Python, right?

You've found the right Convolutional Neural Networks course!

After completing this course you will be able to:

  • Identify the Image Recognition problems which can be solved using CNN Models.

  • Create CNN models in Python using Keras and Tensorflow libraries and analyze their results.

  • Confidently practice, discuss and understand Deep Learning concepts

  • Have a clear understanding of Advanced Image Recognition models such as LeNet, GoogleNet, VGG16 etc.

How this course will help you?

A Verifiable Certificate of Completion is presented to all students who undertake this Convolutional Neural networks course.

If you are an Analyst or an ML scientist, or a student who wants to learn and apply Deep learning in Real world image recognition problems, this course will give you a solid base for that by teaching you some of the most advanced concepts of Deep Learning and their implementation in Python without getting too Mathematical.

Why should you choose this course?

This course covers all the steps that one should take to create an image recognition model using Convolutional Neural Networks.

Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business.

What makes us qualified to teach you?

The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Deep learning techniques and we have used our experience to include the practical aspects of data analysis in this course

We are also the creators of some of the most popular online courses - with over 300,000 enrollments and thousands of 5-star reviews like these ones:

This is very good, i love the fact the all explanation given can be understood by a layman - Joshua

Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy

Our Promise

Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.

Download Practice files, take Practice test, and complete Assignments

With each lecture, there are class notes attached for you to follow along. You can also take practice test to check your understanding of concepts. There is a final practical assignment for you to practically implement your learning.

What is covered in this course?

This course teaches you all the steps of creating a Neural network based model i.e. a Deep Learning model, to solve business problems.

Below are the course contents of this course on ANN:

  • Part 1 (Section 2)- Python basics

    This part gets you started with Python.

    This part will help you set up the python and Jupyter environment on your system and it'll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.

  • Part 2 (Section 3-6) - ANN Theoretical Concepts

    This part will give you a solid understanding of concepts involved in Neural Networks.

    In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model.

  • Part 3 (Section 7-11) - Creating ANN model in Python

    In this part you will learn how to create ANN models in Python.

    We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. Lastly we learn how to save and restore models.

    We also understand the importance of libraries such as Keras and TensorFlow in this part.

  • Part 4 (Section 12) - CNN Theoretical Concepts

    In this part you will learn about convolutional and pooling layers which are the building blocks of CNN models.

    In this section, we will start with the basic theory of convolutional layer, stride, filters and feature maps. We also explain how gray-scale images are different from colored images. Lastly we discuss pooling layer which bring computational efficiency in our model.

  • Part 5 (Section 13-14) - Creating CNN model in Python
    In this part you will learn how to create CNN models in Python.

    We will take the same problem of recognizing fashion objects and apply CNN model to it. We will compare the performance of our CNN model with our ANN model and notice that the accuracy increases by 9-10% when we use CNN. However, this is not the end of it. We can further improve accuracy by using certain techniques which we explore in the next part.

  • Part 6 (Section 15-18) - End-to-End Image Recognition project in Python
    In this section we build a complete image recognition project on colored images.

    We take a Kaggle image recognition competition and build CNN model to solve it. With a simple model we achieve nearly 70% accuracy on test set. Then we learn concepts like Data Augmentation and Transfer Learning which help us improve accuracy level from 70% to nearly 97% (as good as the winners of that competition).

By the end of this course, your confidence in creating a Convolutional Neural Network model in Python will soar. You'll have a thorough understanding of how to use CNN to create predictive models and solve image recognition problems.


Go ahead and click the enroll button, and I'll see you in lesson 1!


Cheers

Start-Tech Academy


------------

Below are some popular FAQs of students who want to start their Deep learning journey-


Why use Python for Deep Learning?

Understanding Python is one of the valuable skills needed for a career in Deep Learning.

Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history:

    In 2016, it overtook R on Kaggle, the premier platform for data science competitions.

    In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools.

    In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.

Deep Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well.

What is the difference between Data Mining, Machine Learning, and Deep Learning?

Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions.

Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.


Screenshots

Convolutional Neural Networks in Python: CNN Computer Vision
Convolutional Neural Networks in Python: CNN Computer Vision
Convolutional Neural Networks in Python: CNN Computer Vision
Convolutional Neural Networks in Python: CNN Computer Vision

Content

Introduction

Introduction

Course resources

Setting up Python and Jupyter Notebook

Installing Python and Anaconda

Opening Jupyter Notebook

Introduction to Jupyter

Arithmetic operators in Python: Python Basics

Strings in Python: Python Basics

Lists, Tuples and Directories: Python Basics

Working with Numpy Library of Python

Working with Pandas Library of Python

Working with Seaborn Library of Python

Single Cells - Perceptron and Sigmoid Neuron

Perceptron

Activation Functions

Python - Creating Perceptron model

Neural Networks - Stacking cells to create network

Basic Terminologies

Gradient Descent

Back Propagation

Important concepts: Common Interview questions

Some Important Concepts

Standard Model Parameters

Hyperparameters

Tensorflow and Keras

Keras and Tensorflow

Installing Tensorflow and Keras

Python - Dataset for classification problem

Dataset for classification

Normalization and Test-Train split

Python - Building and training the Model

Different ways to create ANN using Keras

Building the Neural Network using Keras

Compiling and Training the Neural Network model

Evaluating performance and Predicting using Keras

Saving and Restoring Models

Saving - Restoring Models and Using Callbacks

Hyperparameter Tuning

Hyperparameter Tuning

CNN - Basics

CNN Introduction

Stride

Padding

Filters and Feature maps

Channels

PoolingLayer

Creating CNN model in Python

CNN model in Python - Preprocessing

CNN model in Python - structure and Compile

CNN model in Python - Training and results

Analyzing impact of Pooling layer

Comparison - Pooling vs Without Pooling in Python

Project : Creating CNN model from scratch

Project - Introduction

Data for the project

Project - Data Preprocessing in Python

Project - Training CNN model in Python

Project in Python - model results

Project : Data Augmentation for avoiding overfitting

Project - Data Augmentation Preprocessing

Project - Data Augmentation Training and Results

Transfer Learning : Basics

ILSVRC

LeNET

VGG16NET

GoogLeNet

Transfer Learning

Transfer Learning in Python

Project - Transfer Learning - VGG16



Reviews

S
Swathi3 October 2020

Please tell us properly regarding the installation. There are tons of errors coming up while installing the modules. Whom should we ask? They have told in detail about the easy things like jup notebook and others whereas no proper talk about a bit complex things

S
Souvik20 September 2020

The course explainations and animations of videos are really good. Each step of code was properly explained.

J
Jendrejek18 September 2020

The topics are well divided. The individual steps are reproduced at an appropriate pace and explained in a clear and understandable way. One point of criticism: Only the subtitles are difficult to read, as the sentences are written in poor English. In addition, the sentences are wrongly divided, i.e. no coherent sentences, making them difficult to read and understand.

V
Vijay16 September 2020

Fantastic course, I attended Andrew NG course of deep learning on Coursera but this course is more simpler and you grasp concepts easier, highly recommended

J
Jyotsna15 September 2020

This is an elaborate and comprehensive course for a beginner . I recommend this to all those who have hands on experience with Machine Learning and want to go on with Deep Learning. It's a really good start.

K
Kevin8 September 2020

This was a good course. The horns beeping in the background was annoying, but the course was very helpful.

R
Rajas1 September 2020

The videos are really intuitive, as learning concepts along with the python code impact a lot. Also, the explanation of the python code is done in a better way.

S
Shubhang23 August 2020

Great Course, the explanations were amazing, everything was thoroughly explained. All over the course met my expectations.

F
Fábio20 August 2020

It shows what it promises. But there is a lack of commitment by the team in terms of answering the questions.

O
Oludele12 August 2020

A truly amazing intro to CNN and Computer Vision. The way you breakdown the building blocks of ANN and CNN is just spectacular. I enjoy the course

G
Govinda2 August 2020

It's a nice course to have a very first taste about ML (Image recognition) and associated software such as Anaconda and Jupyter.

N
Nada25 July 2020

I like it. it is simple and dives into machine learning basics simply and in depth. it is just perfect for a beginner and it was exactly what i was looking for. It would be better is some resources could be added at the end of each lecture for students who are curious about the field so they could get more in depth knowledge this would increase the excellency of the course. thanks so much for providing such a course i will recommend it to my friends and would try to help by adding more indepth articles if i found any to help better understand the details of the course.

L
Luis27 May 2020

A very well explained course, with numerous examples and very easy to follow. It is very recommendable if you want to know the state or art of CNNs : it introduces Keras over Tensorflow 2 to classify images.

G
George11 February 2020

This course was amazing. It takes you all the way from the basics all the way up to building your own model. Would highly recommend to anyone trying to learn how to implement CNNs to classification problems. Also, even after finishing the course the Start Tech team were more than happy to help me with any issues I was having with my own personal project and always replied promptly. Great course and teachers.

A
Ahire3 February 2020

nice course, actually first i thought this is not sufficient but now i can say the you can apply and definitely will learn something good. also the instructors are very helpful


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2737836

Udemy ID

1/4/2020

Course created date

2/7/2020

Course Indexed date
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