Tensorflow 2.0: Deep Learning and Artificial Intelligence

سرفصل های دوره

Machine Learning & Neural Networks for Computer Vision, Time Series Analysis, NLP, GANs, Reinforcement Learning, +More!

1. Welcome

1. Introduction

2. Outline

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

2. Google Colab

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

2. Tensorflow 2.0 in Google Colab

3. Uploading your own data to Google Colab

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

5. How to Succeed in This Course

6. Temporary 403 Errors

3. Machine Learning and Neurons

1. What is Machine Learning

2. Code Preparation (Classification Theory)

3. Classification Notebook

4. Code Preparation (Regression Theory)

5. Regression Notebook

6. The Neuron

7. How does a model learn

8. Making Predictions

9. Saving and Loading a Model

10. Why Keras

11. Suggestion Box

4. Feedforward Artificial Neural Networks

1. Artificial Neural Networks Section Introduction

2. Beginners Rejoice The Math in This Course is Optional

3. Forward Propagation

4. The Geometrical Picture

5. Activation Functions

6. Multiclass Classification

7. How to Represent Images

8. Color Mixing Clarification

9. Code Preparation (ANN)

10. ANN for Image Classification

11. ANN for Regression

5. 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

7. CNN for Fashion MNIST

8. CNN for CIFAR-10

9. Data Augmentation

10. Batch Normalization

11. Improving CIFAR-10 Results

6. 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. Demo of the Long Distance Problem

13. RNN for Image Classification (Theory)

14. RNN for Image Classification (Code)

15. Stock Return Predictions using LSTMs (pt 1)

16. Stock Return Predictions using LSTMs (pt 2)

17. Stock Return Predictions using LSTMs (pt 3)

18. Other Ways to Forecast

7. Natural Language Processing (NLP)

1. Embeddings

2. Code Preparation (NLP)

3. Text Preprocessing

4. Text Classification with LSTMs

5. CNNs for Text

6. Text Classification with CNNs

8. Recommender Systems

1. Recommender Systems with Deep Learning Theory

2. Recommender Systems with Deep Learning Code

9. Transfer Learning for Computer Vision

1. Transfer Learning Theory

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

3. Large Datasets and Data Generators

4. 2 Approaches to Transfer Learning

5. Transfer Learning Code (pt 1)

6. Transfer Learning Code (pt 2)

10. GANs (Generative Adversarial Networks)

1. GAN Theory

2. GAN Code

11. 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

12. 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

10. Help! Why is the code slower on my machine

13. Advanced Tensorflow Usage

1. What is a Web Service (Tensorflow Serving pt 1)

2. Tensorflow Serving pt 2

3. Tensorflow Lite (TFLite)

4. Why is Google the King of Distributed Computing

5. Training with Distributed Strategies

6. Using the TPU

14. Low-Level Tensorflow

1. Differences Between Tensorflow 1.x and Tensorflow 2.x

2. Constants and Basic Computation

3. Variables and Gradient Tape

4. Build Your Own Custom Model

15. In-Depth Loss Functions

1. Mean Squared Error

2. Binary Cross Entropy

3. Categorical Cross Entropy

16. 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)

17. Course Conclusion

1. How to get the Tensorflow Developer Certificate

2. What to Learn Next

18. Extras

1. How to Choose Hyperparameters

2. Get the Exercise Pack for This Course

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)

1.1 Data Links.html

1.2 Github Links.html

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

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

3. Beginners Coding Tips

4. How to Code Yourself (part 1)

5. How to Code Yourself (part 2)

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

7. Is Theano Dead

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)

5. Common Beginner Questions What if Im advanced

22. Appendix FAQ Finale

1. What is the Appendix

2. BONUS

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تولید کننده: Udemy