PyTorch: Deep Learning and Artificial Intelligence

سرفصل های دوره

Neural Networks for Computer Vision, Time Series Forecasting, NLP, GANs, Reinforcement Learning, and More!

1. Introduction

1. Welcome

2. Overview and Outline

2. Getting Set Up

1.1 Data Links.html

1.2 Github Link.html

1. Get Your Hands Dirty, Practical Coding Experience, Data Links

2. How to use Github & Extra Coding Tips (Optional)

3.1 Code Link.html

3.2 Data Links.html

3.3 Github Link.html

3. Where to get the code, notebooks, and data

4. How to Succeed in This Course

5. Temporary 403 Errors

3. Google Colab

1. Intro to Google Colab, how to use a GPU or TPU for free

2. Uploading your own data to Google Colab

3. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn

4. Machine Learning and Neurons

1. What is Machine Learning

2. Regression Basics

3. Regression Code Preparation

4. Regression Notebook

5. Moores Law

6. Moores Law Notebook

7. Linear Classification Basics

8. Classification Code Preparation

9. Classification Notebook

10. Saving and Loading a Model

11. A Short Neuroscience Primer

12. How does a model learn

13. Model With Logits

14. Train Sets vs. Validation Sets vs. Test Sets

15. Suggestion Box

5. Feedforward Artificial Neural Networks

1. Artificial Neural Networks Section Introduction

2. Forward Propagation

3. The Geometrical Picture

4. Activation Functions

5. Multiclass Classification

6. How to Represent Images

7. Color Mixing Clarification

8. Code Preparation (ANN)

9. ANN for Image Classification

10. ANN for Regression

11. How to Choose Hyperparameters

6. Convolutional Neural Networks

1. What is Convolution (part 1)

2. What is Convolution (part 2)

3. What is Convolution (part 3)

4. Convolution on Color Images

5. CNN Architecture

6. CNN Code Preparation (part 1)

7. CNN Code Preparation (part 2)

8. CNN Code Preparation (part 3)

9. CNN for Fashion MNIST

10. CNN for CIFAR-10

11. Data Augmentation

12. Batch Normalization

13. Improving CIFAR-10 Results

7. Recurrent Neural Networks, Time Series, and Sequence Data

1. Sequence Data

2. Forecasting

3. Autoregressive Linear Model for Time Series Prediction

4. Proof that the Linear Model Works

5. Recurrent Neural Networks

6. RNN Code Preparation

7. RNN for Time Series Prediction

8. Paying Attention to Shapes

9. GRU and LSTM (pt 1)

10. GRU and LSTM (pt 2)

11. A More Challenging Sequence

12. RNN for Image Classification (Theory)

13. RNN for Image Classification (Code)

14. Stock Return Predictions using LSTMs (pt 1)

15. Stock Return Predictions using LSTMs (pt 2)

16. Stock Return Predictions using LSTMs (pt 3)

17. Other Ways to Forecast

8. Natural Language Processing (NLP)

1. Embeddings

2. Neural Networks with Embeddings

3. Text Preprocessing Concepts

4.1 Why bad programmers always need the latest version.html

4. Beginner Blues - PyTorch NLP Version

5. (Legacy) Text Preprocessing Code Preparation

6. (Legacy) Text Preprocessing Code Example

7. Text Classification with LSTMs (V2)

8. CNNs for Text

9. Text Classification with CNNs (V2)

10. (Legacy) VIP Making Predictions with a Trained NLP Model

11. VIP Making Predictions with a Trained NLP Model (V2)

9. Recommender Systems

1. Recommender Systems with Deep Learning Theory

2. Recommender Systems with Deep Learning Code Preparation

3. Recommender Systems with Deep Learning Code (pt 1)

4. Recommender Systems with Deep Learning Code (pt 2)

5. VIP Making Predictions with a Trained Recommender Model

10. Transfer Learning for Computer Vision

1. Transfer Learning Theory

2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet)

3. Large Datasets

4. 2 Approaches to Transfer Learning

5. Transfer Learning Code (pt 1)

6. Transfer Learning Code (pt 2)

11. GANs (Generative Adversarial Networks)

1. GAN Theory

2. GAN Code Preparation

3. GAN Code

12. Deep Reinforcement Learning (Theory)

1. Deep Reinforcement Learning Section Introduction

2. Elements of a Reinforcement Learning Problem

3. States, Actions, Rewards, Policies

4. Markov Decision Processes (MDPs)

5. The Return

6. Value Functions and the Bellman Equation

7. What does it mean to learn

8. Solving the Bellman Equation with Reinforcement Learning (pt 1)

9. Solving the Bellman Equation with Reinforcement Learning (pt 2)

10. Epsilon-Greedy

11. Q-Learning

12. Deep Q-Learning DQN (pt 1)

13. Deep Q-Learning DQN (pt 2)

14. How to Learn Reinforcement Learning

13. Stock Trading Project with Deep Reinforcement Learning

1. Reinforcement Learning Stock Trader Introduction

2. Data and Environment

3. Replay Buffer

4. Program Design and Layout

5. Code pt 1

6. Code pt 2

7. Code pt 3

8. Code pt 4

9. Reinforcement Learning Stock Trader Discussion

14. VIP Uncertainty Estimation

1. Custom Loss and Estimating Prediction Uncertainty

2. Estimating Prediction Uncertainty Code

15. VIP Facial Recognition

1. Facial Recognition Section Introduction

2. Siamese Networks

3. Code Outline

4. Loading in the data

5. Splitting the data into train and test

6. Converting the data into pairs

7. Generating Generators

8. Creating the model and loss

9. Accuracy and imbalanced classes

10. Facial Recognition Section Summary

16. In-Depth Loss Functions

1. Mean Squared Error

2. Binary Cross Entropy

3. Categorical Cross Entropy

17. In-Depth Gradient Descent

1. Gradient Descent

2. Stochastic Gradient Descent

3. Momentum

4. Variable and Adaptive Learning Rates

5. Adam (pt 1)

6. Adam (pt 2)

18. Extras

1. Where Are The Exercises

19. Setting up your Environment (FAQ by Student Request)

1. Pre-Installation Check

2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

3. Anaconda Environment Setup

4. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer

20. Extra Help With Python Coding for Beginners (FAQ by Student Request)

1. Beginners Coding Tips

2. How to Code Yourself (part 1)

3. How to Code Yourself (part 2)

4. Proof that using Jupyter Notebook is the same as not using it

21. Effective Learning Strategies for Machine Learning (FAQ by Student Request)

1. How to Succeed in this Course (Long Version)

2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced

3. Machine Learning and AI Prerequisite Roadmap (pt 1)

4. Machine Learning and AI Prerequisite Roadmap (pt 2)

22. Appendix FAQ Finale

1. What is the Appendix

2. BONUS

45,900 تومان

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تولید کننده: Udemy