English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 17 Hours | 6.27 GB

Learn how to create state of the art neural networks for deep learning with Facebook’s PyTorch Deep Learning library!

Welcome to the best online course for learning about Deep Learning with Python and PyTorch!

PyTorch is an open source deep learning platform that provides a seamless path from research prototyping to production deployment. It is rapidly becoming one of the most popular deep learning frameworks for Python. Deep integration into Python allows popular libraries and packages to be used for easily writing neural network layers in Python. A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more.

This course focuses on balancing important theory concepts with practical hands-on exercises and projects that let you learn how to apply the concepts in the course to your own data sets! When you enroll in this course you will get access to carefully laid out notebooks that explain concepts in an easy to understand manner, including both code and explanations side by side. You will also get access to our slides that explain theory through easy to understand visualizations.

In this course we will teach you everything you need to know to get started with Deep Learning with Pytorch, including:

- NumPy
- Pandas
- Machine Learning Theory
- Test/Train/Validation Data Splits
- Model Evaluation – Regression and Classification Tasks
- Unsupervised Learning Tasks
- Tensors with PyTorch
- Neural Network Theory
- Perceptrons
- Networks
- Activation Functions
- Cost/Loss Functions
- Backpropagation
- Gradients
- Artificial Neural Networks
- Convolutional Neural Networks
- Recurrent Neural Networks
- and much more!

By the end of this course you will be able to create a wide variety of deep learning models to solve your own problems with your own data sets.

So what are you waiting for? Enroll today and experience the true capabilities of Deep Learning with PyTorch! I’ll see you inside the course!

What you’ll learn

- Learn how to use NumPy to format data into arrays
- Use pandas for data manipulation and cleaning
- Learn classic machine learning theory principals
- Use PyTorch Deep Learning Library for image classification
- Use PyTorch with Recurrent Neural Networks for Sequence Time Series Data
- Create state of the art Deep Learning models to work with tabular data

## Table of Contents

**Course Overview, Installs, and Setup**

1 COURSE OVERVIEW LECTURE – PLEASE DO NOT SKIP!

2 Installation and Environment Setup

**COURSE OVERVIEW CONFIRMATION CHECK**

DID YOU WATCH THE COURSE OVERVIEW LECTURE

**Crash Course NumPy**

3 Introduction to NumPy

4 NumPy Arrays

5 NumPy Arrays Part Two

6 Numpy Index Selection

7 NumPy Operations

8 Numpy Exercises

9 Numpy Exercises – Solutions

**Crash Course Pandas**

10 Pandas Overview

11 Pandas Series

12 Pandas DataFrames – Part One

13 Pandas DataFrames – Part Two

14 GroupBy Operations

15 Pandas Operations

16 Data Input and Output

17 Pandas Exercises

18 Pandas Exercises – Solutions

**PyTorch Basics**

19 PyTorch Basics Introduction

20 Tensor Basics

21 Tensor Basics – Part Two

22 Tensor Operations

23 Tensor Operations – Part Two

24 PyTorch Basics – Exercise

25 PyTorch Basics – Exercise Solutions

**Machine Learning Concepts Overview**

26 What is Machine Learning

27 Supervised Learning

28 Overfitting

29 Evaluating Performance – Classification Error Metrics

30 Evaluating Performance – Regression Error Metrics

31 Unsupervised Learning

**ANN – Artificial Neural Networks**

32 Introduction to ANN Section

33 Linear Regression with PyTorch – Part Two

34 DataSets with PyTorch

35 Basic Pytorch ANN – Part One

36 Basic PyTorch ANN – Part Two

37 Basic PyTorch ANN – Part Three

38 Introduction to Full ANN with PyTorch

39 Full ANN Code Along – Regression – Part One – Feature Engineering

40 Full ANN Code Along – Regression – Part 2 – Categorical and Continuous Features

41 Full ANN Code Along – Regression – Part Three – Tabular Model

42 Full ANN Code Along – Regression – Part Four – Training and Evaluation

43 Theory – Perceptron Model

44 Full ANN Code Along – Classification Example

45 ANN – Exercise Overview

46 ANN – Exercise Solutions

47 Theory – Neural Network

48 Theory – Activation Functions

49 Multi-Class Classification

50 Theory – Cost Functions and Gradient Descent

51 Theory – BackPropagation

52 PyTorch Gradients

53 Linear Regression with PyTorch

**CNN – Convolutional Neural Networks**

54 Introduction to CNNs

55 MNIST Data Revisited

56 MNIST with CNN – Code Along – Part One

57 MNIST with CNN – Code Along – Part Two

58 MNIST with CNN – Code Along – Part Three

59 CIFAR-10 DataSet with CNN – Code Along – Part One

60 CIFAR-10 DataSet with CNN – Code Along – Part Two

61 Loading Real Image Data – Part One

62 Loading Real Image Data – Part Two

63 CNN on Custom Images – Part One – Loading Data

64 CNN on Custom Images – Part Two – Training and Evaluating Model

65 Understanding the MNIST data set

66 CNN on Custom Images – Part Three – PreTrained Networks

67 CNN Exercise

68 CNN Exercise Solutions

69 ANN with MNIST – Part One – Data

70 ANN with MNIST – Part Two – Creating the Network

71 ANN with MNIST – Part Three – Training

72 ANN with MNIST – Part Four – Evaluation

73 Image Filters and Kernels

74 Convolutional Layers

75 Pooling Layers

**Recurrent Neural Networks**

76 Introduction to Recurrent Neural Networks

77 RNN on a Time Series – Part Two

78 RNN Exercise

79 RNN Exercise – Solutions

80 RNN Basic Theory

81 Vanishing Gradients

82 LSTMS and GRU

83 RNN Batches Theory

84 RNN – Creating Batches with Data

85 Basic RNN – Creating the LSTM Model

86 Basic RNN – Training and Forecasting

87 RNN on a Time Series – Part One

**Using a GPU with PyTorch and CUDA**

88 Why do we need GPUs

89 Using GPU for PyTorch

**NLP with PyTorch**

90 Introduction to NLP with PyTorch

91 Encoding Text Data

92 Generating Training Batches

93 Creating the LSTM Model

94 Training the LSTM Model

95 OUR MODEL FOR DOWNLOAD

96 Generating Predictions

**BONUS SECTION THANK YOU!**

97 BONUS LECTURE

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