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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