Python for Deep Learning and Artificial Intelligence

سرفصل های دوره

Neural Networks, TensorFlow, ANN, CNN, RNN, LSTM, Transfer Learning and Much More

1. Course Setup

1.1 python-for-deep-learning-and-ai.zip

1. Jupyter Notebook Introduction

2. Python for Deep Learning

1. Python Introduction Part 1

2. Python Introduction Part 2

3. Python Introduction Part 3

4. Numpy Introduction Part 1

5. Numpy Introduction Part 2

6. Pandas Introduction

7. Matplotlib Introduction Part 1

8. Matplotlib Introduction Part 2

9. Seaborn Introduction Part 1

10. Seaborn Introduction Part 2

3. Introduction to Machine Learning

1. Classical Machine Learning Introduction

2. Logistic Regression

3. Support Vector Machine - SVM

4. Decision Tree

5. Random Forest

6. L2 Regularization

7. L1 Regularization

8. Model Evaluation

9. ROC-AUC Curve

10. Code Along in Python Part 1

11. Code Along in Python Part 2

12. Code Along in Python Part 3

13. Code Along in Python Part 4

4. Introduction to Deep Learning and TensorFlow

1. Machine Learning Process Introduction

2. Types of Machine Learning

3. Supervised Learning

4. Unsupervised Learning

5. Reinforcement Learning

6. What is Deep Learning and ML

7. What is Neural Network

8. How Deep Learning Process Works

9. Application of Deep Learning

10. Deep Learning Tools

11. MLops with AWS

5. End to End Deep Learning Project

1. What is Neuron

2. Multi-Layer Perceptron

3. Shallow vs Deep Neural Networks

4. Activation Function

5. What is Back Propagation

6. Optimizers in Deep Learning

7. Steps to Build Neural Network

8. Customer Churn Dataset Loading

9. Data Visualization Part 1

10. Data Visualization Part 2

11. Data Preprocessing

12. Import Neural Networks APIs

13. How to Get Input Shape and Class Weights

14. Neural Network Model Building

15. Model Summary Explanation

16. Model Training

17. Model Evaluation

18. Model Save and Load

19. Prediction on Real-Life Data

6. Introduction to Computer Vision with Deep Learning

1. Introduction to Computer Vision with Deep Learning

2. 5 Steps of Computer Vision Model Building

3. Fashion MNIST Dataset Download

4. Fashion MNIST Dataset Analysis

5. Train Test Split for Data

6. Deep Neural Network Model Building

7. Model Summary and Training

8. Discovering Overfitting - Early Stopping

9. Model Save and Load for Prediction

7. Introduction to Convolutional Neural Networks [Theory and Intuitions]

1. What is Convolutional Neural Network

2. Working Principle of CNN

3. Convolutional Filters

4. Feature Maps

5. Padding and Strides

6. Pooling Layers

7. Activation Function

8. Dropout

9. CNN Architectures Comparison

10. LeNet-5 Architecture Explained

11. AlexNet Architecture Explained

12. GoogLeNet (Inception V1) Architecture Explained

13. RestNet Architecture Explained

14. MobileNet Architecture Explained

15. EfficientNet Architecture Explained

8. Horses vs Humans Classification with Simple CNN

1. Overview of Image Classification using CNNs

2. Introduction to TensorFlow Datasets (TFDS)

3. Download Humans or Horses Dataset Part 1

4. Download Humans or Horses Dataset Part 2

5. Use of Image Data Generator

6. Data Display in Subplots Matrix

7. CNN Introduction

8. Building CNN Model

9. CNN Parameter Calculation

10. CNN Parameter Calculations Part 2

11. CNN Parameter Calculations Part 3

12. Model Training

13. Model Load and Save

14. Image Class Prediction

9. Building Cats and Dogs Classifier with Regularized CNN

1. What is Overfitting

2. L1, L2 and Early Stopping Regularization

3. How Dropout and Batch Normalization Prevents Overfitting

4. What is Data Augmentation [Theory]

5. Sample Data Load with ImageDataGenerator for Augmentation

6. Random Rotation Augmentation

7. Random Shift Augmentation

8. Other Types of Data Augmentation

9. All Types of Augmentation at Once

10. TensorFlow TFDS and Cats vs Dogs Data Download

11. Store Data in Local Directory

12. Load Dataset for Baseline Classifier

13. Building Baseline CNN Classifier

14. How to Calculate Size of Output Layers of CNN and MaxPool

15. How to Calculate Number of Parameters in CNN and FCN

16. Model Training and Layers Analysis

17. Model Training and Validation Accuracy Plot

18. Building Dataset for Regularized CNN

19. Regularized CNN Model Building and Training

20. Training Log Analysis

21. Load Model and Do the Prediction

22. CNN Model Visualization

10. Flowers Classification with Transfer Learning and CNN

1. Transfer Learning Introduction

2. Load Flowers Dataset for Classification

3. Download Flowers Data

4. Flowers Data Visualization

5. Preparing Data with Image Data Generator

6. Baseline CNN Model Building

7. How to Calculate Number of Parameters in CNN

8. Baseline CNN Model Training

9. Train Model with TFDS Data Without Saving Locally Part 1

10. Train Model with TFDS Data Without Saving Locally Part 2

11. import VGG16 from Keras

12. Data Augmentation for Training

13. Make CNN Model with VGG16 Transfer Learning

14. Model Training for Better Accuracy

15. Train Any Model for Transfer Learning

16. Save and Load Model with Class Names

17. Online Prediction of Flowers Classes

11. Introduction to NLP

1. Introduction to NLP

2. What are Key NLP Techniques

3. Overview of NLP Tools

4. Common Challenges in NLP

5. Bag of Words - The Simples Word Embedding Technique

6. Term Frequency - Inverse Document Frequency (TF-IDF)

7. Load Spam Dataset

8. Text Preprocessing

9. Feature Engineering

10. Pair Plot

11. Train Test Split

12. TF-IDF Vectorization

13. Model Evaluation and Prediction on Real Data

14. Model Load and Store

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