Deep Learning: Convolutional Neural Networks in Python

Deep Learning: Convolutional Neural Networks in Python

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 10.5 Hours | 2.72 GB

Use CNNs for Image Recognition, Natural Language Processing (NLP) +More! For Data Science, Machine Learning, and AI

NOW IN TENSORFLOW 2 and PYTHON 3

Learn about one of the most powerful Deep Learning architectures yet!

The Convolutional Neural Network (CNN) has been used to obtain state-of-the-art results in computer vision tasks such as object detection, image segmentation, and generating photo-realistic images of people and things that don’t exist in the real world!

This course will teach you the fundamentals of convolution and why it’s useful for deep learning and even NLP (natural language processing).

You will learn about modern techniques such as data augmentation and batch normalization, and build modern architectures such as VGG yourself.

This course will teach you:

  • The basics of machine learning and neurons (just a review to get you warmed up!)
  • Neural networks for classification and regression (just a review to get you warmed up!)
  • How to model image data in code
  • How to model text data for NLP (including preprocessing steps for text)
  • How to build an CNN using Tensorflow 2
  • How to use batch normalization and dropout regularization in Tensorflow 2
  • How to do image classification in Tensorflow 2
  • How to do data preprocessing for your own custom image dataset
  • How to use Embeddings in Tensorflow 2 for NLP
  • How to build a Text Classification CNN for NLP (examples: spam detection, sentiment analysis, parts-of-speech tagging, named entity recognition)

All of the materials required for this course can be downloaded and installed for FREE. We will do most of our work in Numpy, Matplotlib, and Tensorflow. I am always available to answer your questions and help you along your data science journey.

This course focuses on “how to build and understand”, not just “how to use”. Anyone can learn to use an API in 15 minutes after reading some documentation. It’s not about “remembering facts”, it’s about “seeing for yourself” via experimentation. It will teach you how to visualize what’s happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

What you’ll learn

  • Understand convolution and why it’s useful for Deep Learning
  • Understand and explain the architecture of a convolutional neural network (CNN)
  • Implement a CNN in TensorFlow 2
  • Apply CNNs to challenging Image Recognition tasks
  • Apply CNNs to Natural Language Processing (NLP) for Text Classification (e.g. Spam Detection, Sentiment Analysis)
Table of Contents

Welcome
1 Introduction and Outline
2 Where to get the code
3 How to Succeed in this Course

Google Colab
4 Intro to Google Colab, how to use a GPU or TPU for free
5 Uploading your own data to Google Colab
6 Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn

Machine Learning and Neurons
7 Review Section Introduction
8 Making Predictions
9 Saving and Loading a Model
10 Suggestion Box
11 What is Machine Learning
12 Code Preparation (Classification Theory)
13 Beginner’s Code Preamble
14 Classification Notebook
15 Code Preparation (Regression Theory)
16 Regression Notebook
17 The Neuron
18 How does a model learn

Feedforward Artificial Neural Networks
19 Artificial Neural Networks Section Introduction
20 Forward Propagation
21 The Geometrical Picture
22 Activation Functions
23 Multiclass Classification
24 How to Represent Images
25 Code Preparation (ANN)
26 ANN for Image Classification
27 ANN for Regression

Convolutional Neural Networks
28 What is Convolution (part 1)
29 Batch Normalization
30 Improving CIFAR-10 Results
31 What is Convolution (part 2)
32 What is Convolution (part 3)
33 Convolution on Color Images
34 CNN Architecture
35 CNN Code Preparation
36 CNN for Fashion MNIST
37 CNN for CIFAR-10
38 Data Augmentation

Natural Language Processing (NLP)
39 Embeddings
40 Code Preparation (NLP)
41 Text Preprocessing
42 CNNs for Text
43 Text Classification with CNNs

Convolution In-Depth
44 Real-Life Examples of Convolution
45 Beginner’s Guide to Convolution
46 Alternative Views on Convolution

Convolutional Neural Network Description
47 Convolution on 3-D Images
48 Tracking Shapes in a CNN

Practical Tips
49 Advanced CNNs and how to Design your Own

Extras
50 Colab Notebooks

Setting Up Your Environment
51 Windows-Focused Environment Setup 2018
52 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

Extra Help With Python Coding for Beginners
53 How to Code by Yourself (part 1)
54 How to Code by Yourself (part 2)
55 How to Uncompress a .tar.gz file
56 Proof that using Jupyter Notebook is the same as not using it
57 Python 2 vs Python 3
58 Is Theano Dead

Effective Learning Strategies for Machine Learning
59 How to Succeed in this Course (Long Version)
60 Is this for Beginners or Experts Academic or Practical Fast or slow-paced
61 What order should I take your courses in (part 1)
62 What order should I take your courses in (part 2)

Appendix FAQ
63 What is the Appendix
64 BONUS Where to get discount coupons and FREE deep learning material