by Rudy Lai | Released November 2018 | ISBN: 9781788629942

Apply deep learning concepts and use Python to solve challenging tasksAbout This VideoA no-math beginner-friendly introduction to deep learning suitable for all backgroundsDevelop an intuitive understanding of deep learning: with its advantages and limitationsGain the ability to build truly impressive and immersive intelligent applications with PythonIn DetailWe avoid complex math equations, which can often be a barrier to entry for newcomers.This course will teach you to apply deep learning concepts using Python to solve challenging tasks. You'll build a Python deep learning-based image recognition system and deploy and integrate images into web apps or phone apps.You will start out with an intuitive understanding of neural networks in general. We will guide you through the building blocks of deep learning networks to tackle complex neural networks.So, take this course and learn the skills and temperament need to enter the AI marketplace today.All the code and this course's supporting files of are available on GitHub at - https://github.com/PacktPublishing/Python-Deep-Learning-for-Beginners- Show and hide more

1. Chapter 1 : Understanding Deep Learning
   • The Course Overview 00:02:06
   • A Brief History of Deep Learning 00:03:22
   • Deep Learning Today 00:06:46
   • Tools, Requirements, and Setup 00:05:09
2. Chapter 2 : Building the Basic Blocks of Machine Learning
   • Exploring Supervised Learning 00:02:25
   • Representational Learning and Feature Engineering 00:05:23
   • Linear Regression 00:09:54
   • The Perceptron 00:07:08
3. Chapter 3 : Diving into Deep Neural Networks
   • Feedforward Networks 00:05:05
   • Backpropagation 00:06:09
   • Neural Networks from Scratch 00:05:08
   • Overfitting and Regularization 00:04:54
4. Chapter 4 : Discovering Convolutional Neural Networks (CNNs)
   • Understanding CNNs 00:07:54
   • Implementing a CNN 00:07:01
   • Deep CNNs 00:08:17
5. Chapter 5 : Using CNNs to Solve Increasingly Complex Tasks
   • Very Deep CNNs 00:05:04
   • Batch Normalization 00:04:48
   • Fine-Tuning 00:07:54
6. Chapter 6 : Learning about Detection and Segmentation
   • Semantic Segmentation 00:05:10
   • Fully Convolutional Networks 00:06:02
7. Chapter 7 : Exploring Recurrent Neural Networks
   • Recurrent Neural Networks 00:08:44
   • LSTM and Advancements 00:06:31
8. Chapter 8 : Object Detection Using CNNs
   • Building a CNN to Detect General Images 00:07:12
   • Training and Deploying on a Cluster 00:06:01
9. Chapter 9 : Moving Forward with Deep Learning and AI
   • Comparison of DL Frameworks 00:09:09
   • Exciting Areas for Upcoming Research 00:05:53
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