English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 2h 39m | 309 MB
Apply deep learning concepts and use Python to solve challenging tasks
We 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.
A direct, practical, and very hands-on approach where we deal less with theory and adopt a more hands-on style of learning.
What You Will Learn
- The history of neural networks, and where they are now
- Get a brief understanding of trade-offs and practical implementation aspects
- Get hands-on experience building basic neural network models (no math!) using Python
- Ramp up productivity in model building by leveraging popular frameworks
- Implement some state-of-the-art computer vision algorithms using deep learning and Python
- Build and learn to deploy a practical deep learning application with Python
- Get an overview of current skills, frameworks, tools, and techniques for the AI market
- Build a deep learning-based image recognition system using Python and learn how to deploy and integrate it into web apps or phone apps
Table of Contents
Understanding Deep Learning
1 The Course Overview
2 A Brief History of Deep Learning
3 Deep Learning Today
4 Tools, Requirements, and Setup
Building the Basic Blocks of Machine Learning
5 Exploring Supervised Learning
6 Representational Learning and Feature Engineering
7 Linear Regression
8 The Perceptron
Diving into Deep Neural Networks
9 Feedforward Networks
10 Backpropagation
11 Neural Networks from Scratch
12 Overfitting and Regularization
Discovering Convolutional Neural Networks (CNNs)
13 Understanding CNNs
14 Implementing a CNN
15 Deep CNNs
Using CNNs to Solve Increasingly Complex Tasks
16 Very Deep CNNs
17 Batch Normalization
18 Fine-Tuning
Learning about Detection and Segmentation
19 Semantic Segmentation
20 Fully Convolutional Networks
Exploring Recurrent Neural Networks
21 Recurrent Neural Networks
22 LSTM and Advancements
Object Detection Using CNNs
23 Building a CNN to Detect General Images
24 Training and Deploying on a Cluster
Moving Forward with Deep Learning and AI
25 Comparison of DL Frameworks
26 Exciting Areas for Upcoming Research
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