English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 84 lectures (8h 38m) | 4.26 GB
Your Complete Guide to Implementing PyTorch, Keras, Tensorflow Algorithms: Neural Networks and Deep Learning in Python
THIS IS A COMPLETE NEURAL NETWORKS & DEEP LEARNING TRAINING WITH PYTORCH, H2O, KERAS & TENSORFLOW IN PYTHON!
It is a full 5-Hour+ Deep Learning Boot Camp that will help you learn basic machine learning, neural networks and deep learning using one of the most important Python Deep Learning frameworks- PyTorch, H2O, Keras & Tensorflow.
HERE IS WHY YOU SHOULD ENROLL IN THIS COURSE:
This course is your complete guide to practical machine & deep learning using the PyTorch, H2O, Keras and Tensorflow framework in Python.
This means, this course covers the important aspects of these architectures and if you take this course, you can do away with taking other courses or buying books on the different Python-based- deep learning architectures.
In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal and advent of frameworks such as PyTorch, Keras, H2o, Tensorflow is revolutionizing Deep Learning…
By gaining proficiency in PyTorch, H2O, Keras and Tensorflow, you can give your company a competitive edge and boost your career to the next level.
THIS IS MY PROMISE TO YOU: COMPLETE THIS ONE COURSE & BECOME A PRO IN PRACTICAL PYTHON BASED DATA SCIENCE!
But first things first. My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment), graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation).
I have several years of experience in analyzing real-life data from different sources using data science-related techniques and producing publications for international peer-reviewed journals.
Over the course of my research, I realized almost all the Python data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably with machine learning.
This gives students an incomplete knowledge of the subject. My course, on the other hand, will give you a robust grounding in all aspects of data science within the PyTorch, H2O, Tensorflow and Keras framework.
Unlike other Python courses and books, you will actually learn to use PyTorch, H20, Tensorflow and Keras on real data! Most of the other resources I encountered showed how to use PyTorch on in-built datasets which have limited use.
DISCOVER 7 COMPLETE SECTIONS ADDRESSING EVERY ASPECT OF IMPORTANT DEEP LEARNING FRAMEWORKS:
- A full introduction to Python Data Science and powerful Python driven framework for data science, Anaconda
- Getting started with Jupyter notebooks for implementing data science techniques in Python
- A comprehensive presentation about PyTorch, H2o, Tensorflow and Keras installation and a brief introduction to the other Python data science packages
- A brief introduction to the working of important data science packages such as Pandas and Numpy
- The basics of the PyTorch, H2o, Tensorflow and Keras syntax
- The basics of working with imagery data in Python
- The theory behind neural network concepts such as artificial neural networks, deep neural networks and convolutional neural networks (CNN)
- You’ll even discover how to create artificial neural networks and deep learning structures with PyTorch, Keras and Tensorflow (on real data)
BUT, WAIT! THIS ISN’T JUST ANY OTHER DATA SCIENCE COURSE:
You’ll start by absorbing the most valuable PyTorch, Tensorflow and Keras basics and techniques.
I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts.
My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement Python-based data science in real -life.
After taking this course, you’ll easily use packages like Numpy, Pandas, and PIL to work with real data in Python along with gaining fluency in the most important of deep learning architectures. I will even introduce you to deep learning models such as Convolution Neural network (CNN) !!
The underlying motivation for the course is to ensure you can apply Python-based data science on real data into practice today, start analyzing data for your own projects whatever your skill level, and impress your potential employers with actual examples of your data science abilities.
It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to data science. However, the majority of the course will focus on implementing different techniques on real data and interpret the results. Some of the problems we will solve include identifying credit card fraud and classifying the images of different fruits.
After each video, you will learn a new concept or technique which you may apply to your own projects!
What you’ll learn
- Harness The Power Of Anaconda/iPython For Practical Data Science (Including AI Applications)
- Learn How To Install & Use Important Deep Learning Packages Within Anaconda (Including Keras, H20, Tensorflow and PyTorch)
- Implement Statistical & Machine Learning Techniques With Tensorflow
- Implement Neural Network Modelling With Deep learning Packages Including Keras
Table of Contents
Introduction to the Course
2 Data and Scripts
3 Why Artificial Intelligence and Deep Learning
4 Get Started With the Python Data Science Environment Anaconda
5 Anaconda for Mac Users
6 The iPython Environment
Introduction to Common Python Data Science Packages
7 Python Packages for Data Science
8 NUMPY Introduction to Numpy
9 Create Numpy Arrays
10 Numpy Operations
11 Numpy for Basic Vector Arithmetric
12 Numpy for Basic Matrix Arithmetic
13 PANDAS What are Pandas
14 Read in CSV data
15 Read in Excel data
16 Basic Data Exploration With Pandas
Theoretical Foundations of Artificial Neural Networks (ANN) & Deep Learning (DL)
17 Theory Behind ANN (Artificial Neural Network) and DNN (Deep Neural Networks)
18 Perceptrons for Binary Classification
19 ANN For Binary Classification
20 What Are Activation Functions Theory
21 More on Backpropagation
22 Multi-label classification with MLP
23 Regression with MLP
24 Other Accuracy Metrics
Introduction to Artificial Intelligence Python Packages PyTorch
25 Start With H20
26 Welcome to Tensorflow
27 Install Tensorflow
28 What are Tensors
29 Introduction to Computational Graphs
30 Common Tensorflow Operations
31 Welcome to Keras
32 Keras Installation on Windows 10
33 Keras Installation on Mac OS
34 Written Instructions
35 Why PyTorch
36 Install PyTorch
37 PyTorch Basics What Is a Tensor
38 Explore PyTorch Tensors and Numpy Arrays
39 Some Basic PyTorch Tensor Operations
Implementing ANN With Python
40 Implement Multi Layer Perceptron (MLP) with Tensorflow
41 Multi Layer Perceptron (MLP) With Keras
42 Keras MLP For Binary Classification
43 Keras MLP for Multiclass Classification
44 Keras MLP for Regression
45 Implement ANN With H2O
46 PyTorch ANN Syntax
47 Setting Up ANN Analysis With PyTorch
48 How the Different Components of Neural Networks Come Together PyTorch Example
Implementing DNNs With Python
49 Deep Neural Network (DNN) Classifier With Tensorflow
50 Deep Neural Network (DNN) Classifier With Mixed Predictors
51 Deep Neural Network (DNN) Regression With Tensorflow
52 Wide & Deep Learning (Tensorflow)
53 DNN Classifier With Keras
54 DNN Classifier With Keras-Example 2
55 DNN Classifier With H2O
56 DNN Analysis with PyTorch
57 More DNNs
58 DNNs For Identifying Credit Card Fraud
Unsupervised Learning with Deep Learning
59 What is Unsupervised Learning
60 Autoencoders for Unsupervised Classification
61 Autoencoders in Tensorflow (Binary Class Problem)
62 Autoencoders in Tensorflow (Multiple Classes)
63 Autoencoders in Keras (Sparsity Constraints)
64 Autoencoders in Keras (Simple)
65 Deep Autoencoder With Keras
Working With Imagery Data and Computer Vision
67 What Are Images
68 Read in Images in Python
69 Some Basic Image Conversions
70 Basic Image Resizing
Convolution Neural Networks (CNN)
71 What are CNNs
72 Implement a CNN for Multi-Class Supervised Classification
73 What Are Activation Functions
74 More on CNN
75 Pre-Requisite For Working With Imagery Data
76 CNN on Image Data-Part 1
77 CNN on Image Data-Part 2
78 Implement CNN With TFLearn
79 CNN Workflow for Keras
80 CNN With Keras
81 CNN on Image Data with Keras-Part 2
82 Theory Behind Transer Learning
83 Implement an InceptionV3 model on Real Images
84 Github Intro