Convolutional Neural Networks in Python: CNN Computer Vision
- Description
- Curriculum
- FAQ
- Reviews
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 1,300,000 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.
-
1IntroductionVideo lesson
In Lecture 1 of Section 1, we will be covering an introduction to Convolutional Neural Networks (CNNs) in Python for computer vision applications. We will start by discussing the basics of CNNs, including what they are and how they are used in image recognition tasks. We will also explore the architecture of a typical CNN, including layers such as convolutional layers, pooling layers, and fully connected layers.
Additionally, we will delve into the importance of CNNs in computer vision, highlighting their ability to automatically learn features from raw data and their effectiveness in image classification tasks. Throughout the lecture, we will provide examples and practical demonstrations to help you understand how CNNs work and how they can be implemented using Python libraries such as TensorFlow and Keras. By the end of this lecture, you will have a foundational understanding of CNNs and be prepared to dive deeper into more advanced topics in the following sections. -
2Course ResourcesText lesson
-
3Installing Python and AnacondaVideo lesson
In Lecture 3 of Section 2, we will cover the installation process for Python and Anaconda, which are essential tools for setting up and running Convolutional Neural Networks in Python. We will walk through step-by-step instructions on how to download and install Python, a popular programming language for machine learning, as well as Anaconda, a data science platform that includes various tools and libraries for data analysis and visualization.
By the end of this lecture, you will have a better understanding of how to set up your Python environment and Jupyter Notebook, a web-based interactive computing environment, to begin working with CNNs for computer vision projects. We will discuss the importance of using Python and Anaconda for developing deep learning models, and how to effectively use these tools to build and train CNNs for image classification and object detection tasks. Overall, this lecture will provide you with the foundational knowledge needed to start exploring the world of Convolutional Neural Networks and computer vision using Python. -
4This is a milestone!Video lesson
-
5Opening Jupyter NotebookVideo lesson
In Lecture 5 of Section 2, we will be focusing on opening Jupyter Notebook to start working on our Convolutional Neural Networks in Python project. We will cover the steps to install Python and Jupyter Notebook on your system, ensuring that you have all the necessary tools to begin coding. We will also discuss how to launch Jupyter Notebook and create a new notebook to start writing and running Python code for our CNN project.
Additionally, we will provide a brief overview of the Jupyter Notebook interface, including how to navigate through the different sections and cells within a notebook. We will introduce you to the basics of using Markdown for text formatting and code execution within Jupyter Notebook. By the end of this lecture, you will have a solid understanding of how to set up Python and Jupyter Notebook for your CNN Computer Vision project and be ready to move on to the next steps in building and training your CNN model. -
6Introduction to JupyterVideo lesson
Welcome to Lecture 6 of Section 2: Setting up Python and Jupyter Notebook in our Convolutional Neural Networks in Python course. In this lecture, we will be diving into the basics of Jupyter Notebook, a popular tool used by data scientists and machine learning engineers for interactive computing and data visualization. We will discuss how to install Jupyter Notebook, launch the application, create new notebooks, and run code cells.
We will also cover the different types of cells in Jupyter Notebook, such as code cells, markdown cells, and raw cells. Additionally, we will explore various keyboard shortcuts and other useful features that will help streamline your workflow and enhance your coding experience. By the end of this lecture, you will have a solid understanding of Jupyter Notebook and be ready to start coding your own Convolutional Neural Networks in Python for computer vision tasks. -
7Arithmetic operators in Python: Python BasicsVideo lesson
In Lecture 7 of Section 2 on setting up Python and Jupyter Notebook, we will be covering the basics of arithmetic operators in Python. We will discuss how to perform mathematical operations such as addition, subtraction, multiplication, and division in Python. We will also learn about other operators such as modulus, exponential, and floor division. Understanding these arithmetic operators is essential for any programming task, including working with Convolutional Neural Networks in Python.
By the end of this lecture, you will have a solid understanding of how to use arithmetic operators in Python for basic mathematical calculations. This knowledge will serve as a foundation for more advanced concepts in the upcoming lectures on Convolutional Neural Networks. Make sure to follow along with the coding examples provided in Jupyter Notebook to practice applying these arithmetic operators in Python. -
8Quick coding exercise on arithmetic operatorsQuiz
-
9Strings in Python: Python BasicsVideo lesson
In this lecture, we will cover the basics of working with strings in Python. We will discuss how to create strings, manipulate them, and concatenate them together. Understanding how to work with strings is essential for data processing and analysis in Python, and we will explore various string methods and functions that can be used to manipulate and extract information from strings. Additionally, we will talk about the importance of string formatting and how it can be used to present data in a clear and visually appealing manner.
In the second part of the lecture, we will delve into the fundamentals of Python, including its syntax and basic programming concepts. We will explore variables, loops, and conditional statements, which are essential building blocks of any program in Python. By understanding these fundamental concepts, you will be better equipped to write efficient and effective code in Python, which will be critical for successfully implementing Convolutional Neural Networks in Python for computer vision tasks. -
10Quick coding exercise on String operationsQuiz
-
11Lists, Tuples and Directories: Python BasicsVideo lesson
In Lecture 9 of Section 2 of the course "Convolutional Neural Networks in Python: CNN Computer Vision," we will be covering the basics of Python including lists, tuples, and dictionaries. We will dive into how to create and manipulate lists, which are ordered collections of items that can be of different data types. We will also explore tuples, which are similar to lists but are immutable once created. Finally, we will discuss dictionaries, which are key-value pairs that allow for easy and efficient data retrieval and manipulation.
By the end of this lecture, you will have a solid understanding of how to work with lists, tuples, and dictionaries in Python. We will also discuss how these data structures can be useful for storing and organizing data in a variety of applications, making them essential tools for any Python programmer. Additionally, we will demonstrate how to effectively use these data structures in conjunction with Jupyter Notebook, the interactive coding environment that we will be using throughout this course. -
12QuizQuiz
-
13Quick coding exercise on TuplesQuiz
-
14Working with Numpy Library of PythonVideo lesson
In Lecture 10 of Section 2 on "Setting up Python and Jupyter Notebook" in the course "Convolutional Neural Networks in Python", we will be diving into the powerful Numpy library in Python. Numpy is a fundamental package for scientific computing with Python, particularly in the field of data manipulation and analysis. We will start by exploring how to install Numpy in Python and import it into our Jupyter Notebook environment.
Next, we will delve into the various functionalities and capabilities of Numpy, such as creating arrays, reshaping arrays, and performing basic arithmetic operations on arrays. We will also cover more advanced topics like broadcasting, array indexing, and slicing. By the end of this lecture, you will have a solid understanding of how to leverage the Numpy library in your CNN computer vision projects, enabling you to efficiently manipulate and process large datasets to train your neural networks. -
15Quick coding exercise on NumPy LibraryQuiz
-
16Working with Pandas Library of PythonVideo lesson
In this lecture, we will focus on setting up Python and Jupyter Notebook for working with Convolutional Neural Networks in Python. We will begin by discussing the importance of having a properly configured Python environment to run CNN models efficiently. We will also talk about the benefits of using Jupyter Notebook for coding and visualization of CNN algorithms.
Next, we will delve into the Pandas library of Python and how it can be used to handle data manipulation and analysis tasks in CNN projects. We will cover basic Pandas functions, such as reading and writing data files, filtering and sorting data, and performing descriptive statistics. Additionally, we will explore how Pandas can be integrated seamlessly with CNN models to preprocess and analyze image data effectively. By the end of this lecture, you will have a solid understanding of how to set up your Python environment and utilize the Pandas library for CNN computer vision projects. -
17Quick coding exercise on Pandas LibraryQuiz
-
18Working with Seaborn Library of PythonVideo lesson
In Lecture 12 of Section 2: Setting up Python and Jupyter Notebook, we will be focusing on working with the Seaborn library in Python. Seaborn is a popular data visualization library built on top of Matplotlib that provides a high-level interface for creating attractive and informative statistical graphics. We will explore how to install the Seaborn library using pip and how to import it into Jupyter Notebook for data visualization tasks.
We will learn how to use Seaborn to create different types of plots such as scatter plots, line plots, bar plots, and histograms. We will also discuss how to customize the appearance of these plots using various styling options provided by Seaborn. By the end of this lecture, students will have a better understanding of how to leverage the power of Seaborn to create visually appealing and insightful plots for their data analysis projects. -
19QuizzesQuiz
-
21PerceptronVideo lesson
In Lecture 14 of Section 4 on Single Cells in the course "Convolutional Neural Networks in Python: CNN Computer Vision," we will be delving into the concept of the Perceptron. The Perceptron is a fundamental building block in neural network models, and we will explore its role in classifying data by learning from example pairs of inputs and outputs. Through a series of examples and exercises, we will learn how the Perceptron can be trained to make accurate predictions based on input data.
Furthermore, we will also be covering the Sigmoid Neuron in this lecture. The Sigmoid Neuron is another type of artificial neuron commonly used in neural networks, particularly in the context of binary classification problems. We will examine how the Sigmoid Neuron computes its output based on a weighted sum of inputs, and how it applies a non-linear activation function to the result. By the end of this lecture, you will have a solid understanding of both the Perceptron and the Sigmoid Neuron, and how they are used in building more complex neural network models for solving computer vision tasks. -
22Activation FunctionsVideo lesson
In Lecture 15 of the Convolutional Neural Networks in Python course, we will dive deeper into the topic of activation functions, focusing specifically on the Perceptron and Sigmoid Neuron models. We will discuss how these functions are used in the context of single cell processing, and their significance in the realm of computer vision. We will explore the mathematical formulations of these functions, as well as their practical applications in training CNN models for image classification tasks.
Furthermore, we will examine the role of activation functions in the overall architecture of CNNs, and how they contribute to the non-linearity and feature extraction capabilities of these networks. Through hands-on examples and demonstrations, we will illustrate how the Perceptron and Sigmoid Neuron activation functions can be implemented in Python using popular deep learning libraries such as TensorFlow and Keras. By the end of this lecture, students will have a solid understanding of how activation functions shape the behavior of neural networks, and how they can be leveraged to enhance the performance of CNN models in computer vision applications. -
23Python - Creating Perceptron modelVideo lesson
In Lecture 16 of the "Convolutional Neural Networks in Python" course, we will be diving into the topic of creating a Perceptron model using Python. We will start by discussing the basics of a Perceptron and how it functions as a simple neural network model. We will cover the concept of input features, weights, and the activation function used in a Perceptron model. Additionally, we will go through the mathematical formula for updating weights during the training process.
Furthermore, in this lecture, we will be exploring the Sigmoid Neuron, which is a type of activation function commonly used in neural networks. We will discuss the properties of the Sigmoid function and how it helps in making predictions by mapping the output between 0 and 1. We will also cover the differences between the Sigmoid Neuron and the Perceptron model, and how they can be applied in various machine learning tasks. Finally, we will provide hands-on examples in Python to demonstrate how to implement a Perceptron model and incorporate the Sigmoid Neuron in a neural network. -
24QuizQuiz
-
25Basic TerminologiesVideo lesson
In Lecture 17 of the "Convolutional Neural Networks in Python: CNN Computer Vision" course, we will delve into the basic terminologies related to Neural Networks. This lecture will focus on explaining concepts such as neurons, layers, weights, biases, activations, and loss functions. We will also explore the role of activation functions like ReLU, Sigmoid, and Tanh in shaping the behavior of neural networks.
Furthermore, we will discuss the importance of stacking cells to create a network in neural networks. By understanding how to combine multiple layers of neurons, we can create complex models that can learn from data and make accurate predictions. This lecture will lay the foundation for building more advanced convolutional neural networks for computer vision tasks. -
26Gradient DescentVideo lesson
In Lecture 18 of our course on Convolutional Neural Networks in Python, we will delve into the concept of gradient descent. Gradient descent is a crucial optimization algorithm that is used in training neural networks. We will explore how gradient descent works, including the steps involved in updating the weights of a neural network to minimize the loss function.
Furthermore, we will discuss the importance of gradient descent in the context of training convolutional neural networks for computer vision tasks. Understanding how gradient descent can be applied to optimize the performance of CNNs is essential for achieving better results in image classification, object detection, and image segmentation. By the end of this lecture, you will have a solid understanding of how gradient descent can be used to improve the performance of neural networks in computer vision applications. -
27Back PropagationVideo lesson
In Lecture 19 of the course "Convolutional Neural Networks in Python: CNN Computer Vision," we will delve into the concept of back propagation in neural networks. Back propagation is a crucial algorithm that allows us to iteratively adjust the weights of our neural network to minimize the error between the predicted and actual outputs. Through this lecture, we will explore the mathematical foundations of back propagation and discuss how it helps optimize the performance of our convolutional neural network.
Moreover, we will also cover the technique of stacking cells to create a more complex neural network. By stacking multiple layers of neurons, we can create deep neural networks that can learn complex patterns and structures in our data. We will discuss the challenges and benefits of building deep neural networks, and how we can effectively train them using techniques such as back propagation. This lecture will provide a comprehensive understanding of how neural networks are constructed and trained, offering valuable insights into the world of computer vision and artificial intelligence. -
28QuizQuiz
-
29Some Important ConceptsVideo lesson
In Lecture 20 of Section 6, we will be discussing some important concepts related to Convolutional Neural Networks. Specifically, we will be focusing on common interview questions that may be asked in job interviews or technical discussions related to CNNs. Understanding these concepts is crucial for anyone looking to excel in the field of computer vision and machine learning.
Some of the key topics we will cover include the differences between CNNs and traditional neural networks, the importance of pooling layers in CNNs, the role of activation functions such as ReLU, and the concept of data augmentation. By gaining a thorough understanding of these concepts, students will be well-equipped to answer common interview questions and showcase their knowledge and expertise in the field of Convolutional Neural Networks. -
30QuizQuiz
-
31HyperparametersVideo lesson
In this lecture, we will be focusing on the standard model parameters for Convolutional Neural Networks in Python. Specifically, we will discuss how hyperparameters play a crucial role in the performance of CNN models. Hyperparameters such as learning rate, batch size, and momentum will be covered in detail, along with their impact on the training process and overall accuracy of the model.
We will also delve into the process of tuning hyperparameters to optimize the performance of a CNN model. By understanding how each hyperparameter affects the training process and model accuracy, we can make informed decisions when selecting values for these parameters. Through practical examples and demonstrations, we will explore best practices for choosing hyperparameters to ensure our CNN models are effectively trained and perform at their best. -
32QuizQuiz
-
33Keras and TensorflowVideo lesson
In Lecture 22 of Section 8 on Tensorflow and Keras, we will be diving into the relationship between Keras and Tensorflow in the context of convolutional neural networks. We will explore how Keras serves as a high-level neural networks API that can run on top of Tensorflow, making it easier for developers to build and train deep learning models. We will discuss the advantages of using Keras for its simplicity and user-friendly interface, as well as its compatibility with Tensorflow for optimized performance.
During this lecture, we will also cover how to leverage the power of Tensorflow within Keras to streamline the process of building CNN models for computer vision applications. We will walk through practical examples of using both libraries together to create efficient and effective neural networks. By the end of this lecture, students will have a solid understanding of how Keras and Tensorflow can be integrated to enhance the development of CNNs and improve their performance for computer vision tasks. -
34Installing Tensorflow and KerasVideo lesson
In this lecture, we will cover the process of installing Tensorflow and Keras, two popular deep learning libraries, on your system. We will walk through the steps required to set up these frameworks, including instructions for installing them via pip or conda. Additionally, we will discuss the importance of having the correct versions of Python and other dependencies installed to ensure compatibility with Tensorflow and Keras.
Furthermore, we will explore how to verify that Tensorflow and Keras have been successfully installed by running some sample code. We will demonstrate how to import the libraries into your Python scripts and execute basic operations, such as defining neural network architectures and training models. By the end of this lecture, you will have a solid understanding of how to install and begin using Tensorflow and Keras for your deep learning projects.
-
35Dataset for classificationVideo lesson
In Lecture 24 of the "Convolutional Neural Networks in Python" course, we will be diving into the topic of datasets for classification problems in Python. We will explore how to properly structure and prepare datasets for use in training convolutional neural networks for computer vision tasks. This lecture will cover the importance of high-quality datasets, data preprocessing techniques, and the common pitfalls to avoid when working with image classification datasets.
Furthermore, we will discuss the different types of datasets commonly used in classification problems, such as the CIFAR-10 and MNIST datasets. We will walk through the process of loading and exploring these datasets using Python libraries like NumPy and Pandas. By the end of this lecture, students will have a solid understanding of how to work with datasets for classification problems, setting them up for success in applying convolutional neural networks to real-world computer vision tasks. -
36Normalization and Test-Train splitVideo lesson
In Lecture 25 of Convolutional Neural Networks in Python, we will be covering the importance of normalization and test-train split in computer vision. Normalization helps standardize the input data, making it easier for the model to learn and improve its performance. We will discuss the different techniques for normalization such as min-max scaling, z-score normalization, and batch normalization. Additionally, we will explore the concept of test-train split, which involves dividing the dataset into a training set and a testing set to evaluate the performance of the model.
Furthermore, in this lecture, we will dive into the Python code implementation of normalization and test-train split using popular libraries such as NumPy and scikit-learn. We will demonstrate how to preprocess the dataset for a classification problem, ensuring that the data is appropriately normalized and split for training and testing. By the end of this lecture, students will have a clear understanding of the importance of normalization and test-train split in building effective convolutional neural networks for computer vision tasks. -
37More about test-train splitText lesson
In Lecture 26 of the "Convolutional Neural Networks in Python" course, we will delve deeper into the concept of test-train split when working with convolutional neural networks for computer vision tasks. We will discuss the importance of splitting your dataset into training and testing sets to evaluate the performance of your model accurately. We will also explore different techniques for splitting the data, including random sampling and stratified sampling, and learn how to implement these techniques in Python using popular libraries such as scikit-learn.
Additionally, we will cover the best practices for splitting your dataset for a classification problem and discuss the potential pitfalls to avoid when performing a test-train split. By the end of this lecture, you will have a clear understanding of how to properly split your data for training and evaluation purposes, ensuring that your convolutional neural network produces reliable and accurate results for computer vision applications.
-
38Different ways to create ANN using KerasVideo lesson
In Lecture 27 of Section 10 on Python - Building and training the Model, we will be focusing on different ways to create Artificial Neural Networks using Keras. We will explore how to build and train neural networks for image recognition using convolutional layers and pooling layers to extract features from images. This lecture will provide a comprehensive understanding of how to design and implement convolutional neural networks in Python using Keras for computer vision tasks.
Additionally, we will delve into the process of tuning hyperparameters, such as learning rates and batch sizes, to optimize the performance of our neural networks. By the end of this lecture, you will have the knowledge and skills to create and train convolutional neural networks using Keras for computer vision applications. This will equip you with the tools necessary to build powerful image recognition models that can classify and detect objects with high accuracy. -
39Building the Neural Network using KerasVideo lesson
In Lecture 28 of our course on Convolutional Neural Networks in Python, we will be diving into the process of building the neural network using Keras. Keras is a popular deep learning framework that provides a user-friendly and flexible way to build and train neural networks. We will start by discussing the structure of the neural network, including the number of layers, activation functions, and optimization algorithms that will be used.
Next, we will walk through the steps to build and train the model using Keras. This will include defining the layers of the network, compiling the model with an appropriate loss function and optimizer, and fitting the model to the training data. We will also cover techniques for monitoring the model's performance during training, such as using callbacks to save the best model and early stopping to prevent overfitting. By the end of this lecture, you will have a solid understanding of how to build and train a convolutional neural network using Keras for computer vision tasks. -
40Compiling and Training the Neural Network modelVideo lesson
In Lecture 29 of our course on Convolutional Neural Networks in Python, we will delve into the important process of compiling and training the neural network model. We will discuss the various parameters and options that are available when compiling a CNN model, such as choosing the optimizer, the loss function, and setting performance metrics for evaluation. We will also explore how to fine-tune these parameters to achieve optimal performance and efficiency in training our neural network.
Additionally, we will cover the process of training the neural network model using the compiled configuration. We will discuss the concept of epochs and batch size, as well as techniques for monitoring the training process and evaluating the performance of our model. By the end of this lecture, you will have a solid foundation in building and training CNN models in Python, equipping you with the skills needed to tackle real-world computer vision tasks. -
41Evaluating performance and Predicting using KerasVideo lesson
In Lecture 30 of the course "Convolutional Neural Networks in Python: CNN Computer Vision," we will be focusing on evaluating the performance of our trained model and making predictions using Keras. We will discuss various evaluation metrics such as accuracy, precision, recall, and F1 score that are commonly used to assess the performance of a convolutional neural network. Additionally, we will explore how to use these metrics to interpret the results of our model and fine-tune it for better performance.
Furthermore, we will delve into the process of making predictions using Keras and how to interpret the output of the model. We will demonstrate how to use our trained model to classify new images and analyze the predictions made by the network. By the end of this lecture, students will have a solid understanding of how to evaluate the performance of their convolutional neural network model and how to use Keras for making accurate predictions in computer vision tasks.
-
46CNN IntroductionVideo lesson
In Lecture 35 of our course on Convolutional Neural Networks in Python, we will be covering the basics of CNN. We will start by introducing what CNNs are and how they differ from traditional neural networks. We will discuss the key components of CNNs, including convolutional layers, pooling layers, and fully connected layers, and explain how they work together to extract features from images.
Additionally, we will explore the importance of data preprocessing in CNNs and how it can improve the performance of our models. We will also touch on topics such as activation functions, loss functions, and optimization algorithms that are commonly used in CNNs. By the end of this lecture, you will have a solid understanding of the fundamentals of CNNs and be equipped with the knowledge to start building your own computer vision applications. -
47StrideVideo lesson
In this lecture, we will be diving into the concept of stride in Convolutional Neural Networks (CNN). Stride refers to the number of pixels by which the filter slides over the input image during the convolution operation. We will explore how adjusting the stride value can impact the output size of the feature maps and affect the overall performance of the CNN model. By understanding the role of stride in CNNs, we can optimize our network for various computer vision tasks.
Furthermore, we will discuss the importance of choosing the right stride value based on the specific requirements of the problem at hand. We will examine how a larger stride can lead to a reduction in the spatial dimensions of the feature maps, potentially losing valuable information, while a smaller stride can result in overlapping receptive fields and increased computational complexity. Through practical examples and demonstrations, we will demonstrate how to implement different stride values in Python using popular deep learning libraries such as TensorFlow and Keras. -
48PaddingVideo lesson
In Lecture 37 of Section 15 on CNN Basics, we will be diving into the concept of padding in convolutional neural networks. We will discuss how padding can be used to add additional layers of zeros around the input image, which can help preserve information around the edges of the image during the convolutional process. We will explore the different types of padding techniques such as valid padding, same padding, and full padding, and how they impact the dimensions of the output feature maps.
Additionally, we will delve into the importance of padding in CNNs for maintaining spatial resolution and handling boundaries in images. We will learn how padding can prevent loss of information at the edges of the image, especially when using larger filter sizes in convolutional layers. By understanding the role of padding in CNNs, we can optimize our models for better performance in tasks such as image classification, object detection, and image segmentation. -
49Filters and Feature mapsVideo lesson
In Lecture 38 of Section 15 on CNN Basics, we will delve into the concepts of filters and feature maps in convolutional neural networks. Filters are essentially small matrices that are applied to input images to extract specific features, such as edges or textures. By sliding these filters across the entire image, we can create feature maps that highlight areas of interest and help in recognizing patterns within the data.
We will discuss the process of convolution, where filters are applied to different parts of the input image to detect patterns and extract relevant information. Through this process, we can create multiple feature maps that capture various features of the image, which are then used in subsequent layers for further processing and analysis. By understanding the role of filters and feature maps in CNNs, we can improve the performance of our models and achieve better results in computer vision tasks. -
50ChannelsVideo lesson
In this lecture, we will delve into the concept of channels in Convolutional Neural Networks (CNNs). Channels refer to the different feature maps produced by filters in CNNs. Each channel corresponds to a specific feature or aspect of the input image. Understanding the role of channels is crucial in interpreting the workings of CNNs and optimizing their performance.
We will explore how channels are utilized in CNNs to extract various features from input images, such as edges, textures, colors, and shapes. By manipulating the number and configuration of channels, we can enhance the network's ability to recognize and classify objects in images accurately. Through practical examples and demonstrations, we will illustrate how channels contribute to the overall effectiveness of CNNs in computer vision tasks. Additionally, we will discuss strategies for designing and fine-tuning channel configurations to improve the network's performance in image recognition and classification tasks. -
51PoolingLayerVideo lesson
In Lecture 40: Pooling Layer, we will dive into the concept of pooling in Convolutional Neural Networks. Pooling layers are essential in CNNs as they help reduce the spatial dimensions of the input volume and control overfitting by summarizing the features present in the sub-regions of the input. We will explore different types of pooling techniques such as Max Pooling, Average Pooling, and Global Average Pooling, and understand how they work to downsample the feature maps.
Furthermore, we will learn about the importance of pooling layers in feature extraction and how they help in creating translation-invariant representations of the input data. We will also discuss the role of pooling in making the network more robust to variations in input and improving the network's computational efficiency. By the end of this lecture, you will have a clear understanding of how pooling layers contribute to the overall performance of a CNN in image recognition tasks. -
52QuizQuiz
-
53CNN model in Python - PreprocessingVideo lesson
In Lecture 41 of our course on Convolutional Neural Networks in Python, we will be focusing on the preprocessing steps necessary before building a CNN model. We will cover topics such as resizing images to a standard size, normalizing pixel values, and splitting the data into training and testing sets. Preprocessing is a crucial step in preparing our data for training the CNN model, as it helps improve the model's accuracy and efficiency.
Additionally, we will discuss techniques such as data augmentation, which involves artificially increasing the size of our training dataset by applying transformations like rotation, flipping, and zooming to the images. Data augmentation is a powerful tool for improving the generalization capabilities of the CNN model and preventing overfitting. By the end of this lecture, students will have a solid understanding of the importance of preprocessing in building a successful CNN model in Python. -
54CNN model in Python - structure and CompileVideo lesson
In this lecture, we will delve into creating a Convolutional Neural Network (CNN) model in Python for computer vision applications. We will explore the structure of a CNN model, understanding the different layers involved such as convolutional, pooling, and fully connected layers. We will also discuss the importance of each layer in the overall architecture of the CNN model and how they work together to extract features from images.
Additionally, we will cover the process of compiling a CNN model in Python, including defining the loss function, optimizer, and metrics for model evaluation. We will explore various options for loss functions and optimizers that are commonly used in CNN models for computer vision tasks. By the end of this lecture, you will have a thorough understanding of how to create and compile a CNN model in Python for image recognition and classification tasks. -
55CNN model in Python - Training and resultsVideo lesson
In this lecture, we will delve into creating a Convolutional Neural Network (CNN) model in Python for computer vision tasks. We will discuss the key steps involved in setting up the CNN model, including defining the architecture of the network, specifying the number of layers and filters, and configuring the activation functions. Additionally, we will look at how to preprocess the input data for training and validation, as well as how to compile the model with a suitable optimizer and loss function.
Furthermore, we will explore the process of training the CNN model in Python and evaluating the results. We will cover topics such as splitting the dataset into training and testing sets, setting up the training parameters, and monitoring the training progress using metrics like accuracy and loss. Additionally, we will discuss how to interpret the results of the training process, including analyzing the confusion matrix and visualizing the performance of the model on test data. By the end of this lecture, you will have a solid understanding of how to create and train a CNN model in Python for computer vision applications.
-
57Project - IntroductionVideo lesson
In Lecture 45, we will delve into the exciting world of creating a Convolutional Neural Network (CNN) model from scratch. We will start by discussing the importance of CNNs in computer vision and how they are revolutionizing various industries such as healthcare, autonomous driving, and security. We will explore the key components of a CNN model, including convolutional layers, pooling layers, and fully connected layers, and how they work together to extract features from images.
Next, we will walk through the steps involved in building a CNN model from scratch using Python. We will cover topics such as data preprocessing, model architecture, and training process. By the end of this lecture, you will have a solid understanding of how to create a CNN model that can accurately classify images and perform tasks such as object detection and image segmentation. Join us as we unlock the potential of CNNs in computer vision and take your skills to the next level. -
58Data for the projectText lesson
In Lecture 46 of our course on Convolutional Neural Networks in Python, we will be diving into the essential topic of data preparation for our project. We will learn how to gather and preprocess the necessary data to train our CNN model from scratch. This step is crucial in ensuring the success of our computer vision project as the quality and quantity of training data directly impact the performance of our model.
We will cover techniques for data augmentation, dataset splitting, and loading the data into our model for training. By the end of this lecture, you will have a solid understanding of how to effectively prepare and utilize data for your CNN project. This foundational knowledge will be crucial for the subsequent lectures where we will delve into building and fine-tuning our CNN model for computer vision tasks. -
59Project - Data Preprocessing in PythonVideo lesson
In this lecture, we will explore the important step of data preprocessing in Python for our Convolutional Neural Networks project. Data preprocessing is crucial for ensuring that our model receives clean and well-organized data for training. We will cover various techniques such as data normalization, resizing images, and data augmentation to enhance the quality of our dataset and improve the performance of our CNN model.
Additionally, we will walk through the process of splitting our data into training and testing sets, as well as creating data generators to efficiently load and preprocess our data during training. By the end of this lecture, you will have a thorough understanding of the importance of data preprocessing in building a successful CNN model and the tools and techniques necessary to preprocess your data effectively in Python. -
60Project - Training CNN model in PythonVideo lesson
In this lecture, we will cover the project of creating a Convolutional Neural Network (CNN) model from scratch. We will go over the process of designing the architecture of the CNN model, including the number of convolutional layers, pooling layers, and fully connected layers. Additionally, we will discuss the implementation of activation functions, dropout regularization, and batch normalization to improve the performance of the model.
Furthermore, we will delve into the training process of the CNN model in Python. We will explain the steps involved in loading and preprocessing image data, splitting the data into training and testing sets, and compiling the model with appropriate loss function and optimizer. Additionally, we will discuss the process of training the model using the fit method and evaluating its performance using metrics such as accuracy and loss. By the end of this lecture, students will have a solid understanding of how to create and train a CNN model for computer vision tasks from scratch using Python. -
61Project in Python - model resultsVideo lesson
In this lecture, we will delve into the final project of our course where we will be creating a Convolutional Neural Network model from scratch using Python. We will walk through the entire process of building the model, training it on a dataset, and evaluating its performance. This project will allow us to apply all the concepts and techniques we have learned throughout the course in a practical and hands-on way.
Additionally, we will discuss how to interpret the results of our CNN model and make any necessary adjustments to improve its accuracy. We will explore various metrics such as accuracy, precision, recall, and F1 score to evaluate the performance of our model on a specific dataset. By the end of this lecture, you will have a better understanding of how to create and assess a CNN model for computer vision tasks using Python.
-
62Project - Data Augmentation PreprocessingVideo lesson
In this lecture, we will discuss the importance of data augmentation in avoiding overfitting when training Convolutional Neural Networks for computer vision tasks. We will explore various techniques such as flipping, rotating, scaling, and adding noise to the training data to create new variations of the original images. By increasing the diversity of the training data through data augmentation, we can improve the generalization capability of the CNN model and prevent it from memorizing the training data.
Furthermore, we will walk through a practical project on implementing data augmentation preprocessing in Python using libraries such as Keras and OpenCV. We will demonstrate how to apply different data augmentation techniques on a dataset, visualize the augmented images, and train a CNN model using the augmented data to observe the impact on the model's performance. By the end of this lecture, you will have a clear understanding of how data augmentation can help prevent overfitting and improve the robustness of your CNN models in computer vision applications. -
63Project - Data Augmentation Training and ResultsVideo lesson
In this lecture, we will delve into the concept of data augmentation as a technique to avoid overfitting in Convolutional Neural Networks. We will discuss how data augmentation can help to increase the size of our training dataset by applying various transformations such as flipping, rotating, scaling, and shifting the images. By creating new variations of our original images, we can prevent our CNN model from memorizing the training data and improve its generalization capabilities.
Furthermore, we will walk through a practical example of implementing data augmentation in Python using the Keras library. We will demonstrate how to apply different data augmentation techniques to our image dataset and train our model on the augmented data. We will then evaluate the performance of our CNN model on the augmented dataset and compare the results with the model trained on the original dataset. By the end of this lecture, you will have a solid understanding of how data augmentation can be used effectively to enhance the performance of your CNN models and avoid overfitting.
-
64ILSVRCVideo lesson
In this lecture, we will delve into the basics of transfer learning in convolutional neural networks. Transfer learning is a technique that allows us to use a pre-trained model on a large dataset, such as the ImageNet database, and fine-tune it for a specific task or dataset. By leveraging the knowledge and features learned from the pre-trained model, we can significantly improve the performance of our own network on a smaller dataset.
Specifically, we will be looking at the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), which is an annual competition that evaluates algorithms for object detection and image classification. We will explore how transfer learning using models trained on ILSVRC can be applied in our own computer vision tasks. Through real-world examples and practical demonstrations, we will gain a deeper understanding of how transfer learning can benefit our CNN models and improve their overall accuracy and efficiency. -
65LeNETVideo lesson
In Lecture 53 of Section 20 on Transfer Learning: Basics, we will delve into the LeNET architecture, a pioneering Convolutional Neural Network developed by Yann LeCun in the 1990s. We will explore the different layers of LeNET, including convolutional and pooling layers, and how they are structured to efficiently process and extract features from images. Additionally, we will discuss the significance of LeNET in the development of modern CNNs and its impact on computer vision tasks.
Furthermore, we will examine how transfer learning can be applied to LeNET, allowing us to leverage pre-trained models and fine-tune them for specific tasks. We will discuss the advantages of using transfer learning in reducing training time and improving model performance, as well as how to implement transfer learning techniques in Python using popular libraries such as TensorFlow and Keras. By the end of this lecture, students will have a deeper understanding of LeNET and transfer learning, and how they can be utilized to enhance their own CNN models for computer vision applications. -
66VGG16NETVideo lesson
In Lecture 54 of Section 20 on Transfer Learning Basics, we will be diving into the VGG16NET architecture. VGG16NET is a powerful convolutional neural network that has been pre-trained on the large ImageNet dataset. We will discuss how VGG16NET can be used for transfer learning, where we fine-tune the network on a new dataset to perform specific tasks such as image classification or object detection.
We will learn about the architecture of VGG16NET, which consists of 16 layers with a combination of convolutional and pooling layers, followed by fully connected layers. We will also explore how to load the pre-trained VGG16NET model using Python libraries such as Keras, and how to adapt the model for our own dataset by replacing the top layers with new layers suited for our task. By the end of this lecture, you will have a deeper understanding of how transfer learning with VGG16NET can be leveraged to achieve impressive results in computer vision tasks. -
67GoogLeNetVideo lesson
In this lecture, we will be diving into the concept of transfer learning and its basics within the context of convolutional neural networks. We will explore how transfer learning can be used to retrain pre-trained models for specific tasks, allowing for faster and more efficient training of CNNs. Specifically, we will focus on the GoogLeNet architecture, one of the most well-known deep learning models that has achieved great success in image recognition tasks.
Throughout the lecture, we will dissect the architecture of GoogLeNet and understand its intricacies. We will examine the various modules and layers that make up this complex CNN model and discuss how it differs from other traditional CNN architectures. Additionally, we will explore how GoogLeNet utilizes techniques such as inception modules and parallel pathways to improve the network's performance and accuracy in image classification tasks. By the end of this lecture, students will have a solid understanding of how GoogLeNet works and how it can be utilized for transfer learning in computer vision applications. -
68Transfer LearningVideo lesson
In Lecture 56 of Section 20 on Transfer Learning, we will be diving into the basics of transfer learning in Convolutional Neural Networks (CNNs). Transfer learning is a technique where a pre-trained model is used as a starting point and then fine-tuned for a specific task. This approach can save a significant amount of time and resources compared to training a CNN from scratch, especially when working with limited data.
During this lecture, we will explore how to implement transfer learning in Python using popular libraries such as Tensorflow and Keras. We will discuss the different layers in a pre-trained model that can be frozen or fine-tuned, and how to adapt the model to a new dataset. Additionally, we will cover best practices for selecting a pre-trained model, modifying the model architecture, and optimizing hyperparameters to achieve optimal performance in computer vision tasks.
-
70Facial Mask DetectionText lesson
In Lecture 58 of Section 22, we will be diving into the exciting topic of facial mask detection using Convolutional Neural Networks (CNN) in Python. We will discuss the importance of this technology in the current global context of the COVID-19 pandemic, as well as its potential applications in various industries such as healthcare, retail, and security. Through this lecture, we aim to demonstrate how CNNs can be trained to accurately detect whether a person is wearing a facial mask or not, based on input images.
During this lecture, we will cover the key concepts and techniques involved in building a facial mask detection system using CNNs. We will explore the process of data collection, data preprocessing, model training, and evaluation. Additionally, we will provide step-by-step guidance on implementing a CNN-based facial mask detection algorithm in Python, using popular deep learning libraries such as TensorFlow and Keras. By the end of this lecture, students will have gained valuable insights into how CNNs can be leveraged for real-world applications in computer vision, particularly in the context of facial recognition and safety precautions. -
71Comprehensive Interview Preparation QuestionsText lesson
External Links May Contain Affiliate Links read more