Convolutional Neural NetWorks With Tensorflow In Python

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Convolutional Neural Networks With Tensorflow In Python
Last updated 3/2021
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Advanced neural networks: Master Computer Vision with Convolutional Neural Networks (CNN) and Deep Learning

[b]What you'll learn[/b]

Learn the fundamentals of Convolutional Neural Networks

Perform Computer Vision and Machine Learning tasks

Master working with TensorFlow and Tensorboard

Understand kernels

Get the hang of convolution and its role in CNNs

Get familiar with L2 regularization and weight decay

Grasp the concept of dropout

Visualize networks and metrics using Tensorboard

Approach multilabel classification

Gain experience from a big real-world practical example

Convert Images into Tensors

Explore the concepts behind popular state-of-the-art CNN architectures

[b]Requirements[/b]

Python 3 and the Anaconda distribution

Basic to Intermediate Python knowledge

Understanding of Feed-forward neural networks

Basic familiarity with TensorFlow 2

Curiosity and enthusiasm to learn and practice

[b]Description[/b]

Are you a Deep Learning enthusiast who is now looking for their next challenge?Are you interested in the field of Computer Vision and the ability of machines to extract insightful information from visuals and images?Do you want to learn a valuable skill to put yourself ahead of the competition in this AI-driven world?If you answered with "yes" to any of these questions, you have come to the right place and at the right time!Here are 5 reasons this is the right course for you:We have 1,170,000 students on Udemy and we know how to teach a complex topic in an easy to understand wayIt contains numerous practical exercisesA real-life case study with 16,000 imagesSave time - our course will get you there faster than the average courses on the topicNotebook files, course notes, quiz questions, practice materials - all materials are inside the courseThis course is a fantastic training opportunity to help you gain insights into the rapidly expanding field of Machine Learning and Computer Vision through the use of Convolutional Neural Networks.Convolutional Neural Networks, or CNNs in short, are a subtype of deep neural networks that are extensively used in the field of Computer Vision. These networks specialize in inferring information from spatial-structure data to help computers gain high-level understanding from digital images and videos. That can be as simple a task as classifying an image to be a dog or a cat, but it can also explode in complexity as is the case with self-driving cars, for example.This is where most of the active Machine Learning research is concentrated right now, and CNNs are a crucial part of it. So, it is high time to up your game and master this piece of the Deep Learning puzzle.To do just that, we have devised this wonderful and engaging course for you. Although a general understanding of TensorFlow and the main deep learning concepts is required, we will start from the CNNs basics and build our way to proficiency. Moreover, we are firm believers that practice makes perfect, that's why this course offers a comprehensive practical example of a real-world project. What's more, it contains plenty of exercises, homework, downloadable files and notebooks, as well as quiz questions and course notes.We'll start this course by taking a look at Kernels in the context of image processing. Kernels are an essential tool for working with and understanding Convolutional Neural Networks. We'll explore how to achieve different image transformations and help you understand the role of the mathematical operation of convolution in this process. This will be the basis for our next topic - convolutional layers.Armed with all that knowledge, we will introduce the main subject of the course: Convolutional Neural Networks. Here, we'll discuss intriguing concepts such as feature maps and pooling. In addition, we'll inspect how such a network transforms the dimensions of the tensors.Then, what follows is a short and optional neural networks revision. CNNs are simply a subtype of deep neural networks, so a general knowledge of NNs is required. That's why we'll revise the basics: activation functions, early stopping, and optimizers.Once we've covered all that, you will have the minimum required knowledge to start putting all this theory to practice - by building your first Convolutional Neural Network.Working on the MNIST dataset, we'll help you grasp the general workflow of creating a CNN architecture and build one from scratch. You are going to train it to recognize handwritten digits - a very useful tool in the real world. At this point, you will get the hands-on opportunity to tinker and change the network and see the results for yourself.And we won't stop at creating the CNNs. We will also spend a good amount of time exploring them through TensorBoard - the go-to visualization and logging tool when working with TensorFlow. This will make your journey and experimentation in the field more straightforward and definitely more memorable. Neural networks are notorious for their difficult interpretation, so we will examine the Confusion Matrix as a tool to help you understand and interpret the results of your networks. Finally, we'll show you how to easily tune the hyperparameters of your networks.But there's more.We will show you how to master 3 common techniques to improve the performance of your models. In fact, you will have the opportunity to apply those techniques to the networks we create for the next practical section.You heard that right! The idea of this course is to give you the real CNN experience. We will have an enormous practical exercise so you can work on a real-world project.To do that, we've created our very own custom data set that comes from the fashion industry. It consists of more than 16,000 images of trousers, jeans, shoes, glasses, and sunglasses. And we will be using these for numerous practical examples and problems. We've devised a task to classify the different items with a corresponding label. Not only that, but we will also determine other characteristics, such as the items' subtype and gender. Given the nature of these, we will be able to try out different techniques to achieve our goal and compare how these approaches fare against each other. You'll get a taste of the real-world challenges of solving such a task, and gain experience with a real project that you can later add to your portfolio.Finally, to cap it all off, we end this course with a review of the timeline of Convolutional Neural Networks professional research. We will dive into the workings of some popular CNN architectures, and all-stars like AlexNet, GoogLeNet, as well as ResNet will all make an appearance.By the end of this course, you will be completely equipped with all the tools you need to confidently work on CNN projects!We, at the 365 Data Science Team are committed to providing only the highest quality content to you - our students. That's why we have teamed up with a true industry expert - Iskren Vankov. Iskren is a very capable Software developer and Computer Scientist with a Bachelor's degree in Computer Science and Physics from The University of Edinburgh, and a Master's degree in Computer Science from The University of Oxford. Iskren has also been engaged in Deep Learning programming for more than 5 years with a focus on Recurrent Neural Networks.As with all of our courses, you have a 30-day money-back guarantee, if at some point you decide that the training isn't the best fit for you.What's more, the course comes with plenty of exercises, homework, downloadable files, quiz questions, and course notes. Everything you need for a perfect learning experience.So, what are you waiting for?Click the 'Buy now' button and let's explore CNNs together!

Overview

Section 1: Introduction to the course

Lecture 1 What does the course cover?

Lecture 2 Why CNNs?

Section 2: Kernels

Lecture 3 Introduction to image kernels

Lecture 4 How do image transformations work?

Lecture 5 Kernels as matrices

Lecture 6 Convolution - applying kernels

Lecture 7 Edge handling

Section 3: CNN Introduction

Lecture 8 CNNs motivation

Lecture 9 Feature maps

Lecture 10 Pooling and Stride

Lecture 11 Dimensions

Section 4: Neural networks techniques (revision)

Lecture 12 Activation functions

Lecture 13 Overfitting and early stopping

Lecture 14 Optimizers

Section 5: Setting up the environment

Lecture 15 Setting up the environment - Do not skip, please!

Lecture 16 Why Python and why Jupyter?

Lecture 17 Installing Anaconda

Lecture 18 Jupyter Dashboard - Part 1

Lecture 19 Jupyter Dashboard - Part 2

Lecture 20 Installing the packages

Section 6: CNN assembling - MNIST

Lecture 21 Road plan

Lecture 22 A simple CNN architecture

Lecture 23 Preprocessing the data

Lecture 24 Building and training the CNN

Lecture 25 Testing the trained CNN

Section 7: Tensorboard: Visualization tool for TensorFlow

Lecture 26 Tensorboard on the MNIST example

Lecture 27 Confusion matrix and visualizing it with Tensorboard

Lecture 28 Using Tensorboard to tune hyperparameters

Section 8: Common techniques for better performance of NN

Lecture 29 Introduction

Lecture 30 Regularization

Lecture 31 L2 Regularization and Weight Decay

Lecture 32 Dropout

Lecture 33 Data augmentation

Section 9: A practical project: Labelling fashion items

Lecture 34 Introduction to the problem

Lecture 35 The objective and the images

Lecture 36 Converting images to arrays

Lecture 37 Getting started with the code concepts

Lecture 38 Primary classification task - Part 1

Lecture 39 Primary classification task - Part 2

Lecture 40 Primary classification task - Part 3

Lecture 41 Trousers and Jeans - discussion of approaches

Lecture 42 Trousers and Jeans - All

Lecture 43 Trousers and Jeans - Gender + Type

Lecture 44 Trousers and Jeans - comparing the methods

Lecture 45 L2 regularization and Dropout

Lecture 46 Data augmentation - Shoes All

Section 10: Understanding CNNs

Lecture 47 Unexpected failures

Section 11: Popular CNN architectures

Lecture 48 Introduction - the ILSVRC challenge

Lecture 49 AlexNet - CNN success

Lecture 50 VGG - more layers

Lecture 51 GoogleNet - computational efficiency

Lecture 52 ResNet - revolution of depth

Anyone seeking to advance their skills in Machine Learning and Computer Vision,This course is for you if you want to learn how Convolutional Neural Networks work,Anyone who wants to make a career in Deep Learning,Individuals who are curious and passionate about AI

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