Python for Deep Learning and Artificial Intelligence

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Laxmi Kant KGP Talkie

17:04:48

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1. Course Setup

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

1. Jupyter Notebook Introduction.mp4

10:07

2. Python for Deep Learning

1. Python Introduction Part 1.mp4

08:21

2. Python Introduction Part 2.mp4

08:41

3. Python Introduction Part 3.mp4

08:40

4. Numpy Introduction Part 1.mp4

09:03

5. Numpy Introduction Part 2.mp4

07:46

6. Pandas Introduction.mp4

07:58

7. Matplotlib Introduction Part 1.mp4

09:12

8. Matplotlib Introduction Part 2.mp4

11:36

9. Seaborn Introduction Part 1.mp4

06:17

10. Seaborn Introduction Part 2.mp4

08:02

3. Introduction to Machine Learning

1. Classical Machine Learning Introduction.mp4

08:29

2. Logistic Regression.mp4

08:19

3. Support Vector Machine - SVM.mp4

07:01

4. Decision Tree.mp4

05:25

5. Random Forest.mp4

03:33

6. L2 Regularization.mp4

08:07

7. L1 Regularization.mp4

04:38

8. Model Evaluation.mp4

08:05

9. ROC-AUC Curve.mp4

03:26

10. Code Along in Python Part 1.mp4

06:36

11. Code Along in Python Part 2.mp4

07:02

12. Code Along in Python Part 3.mp4

06:32

13. Code Along in Python Part 4.mp4

11:07

4. Introduction to Deep Learning and TensorFlow

1. Machine Learning Process Introduction.mp4

06:43

2. Types of Machine Learning.mp4

02:37

3. Supervised Learning.mp4

03:43

4. Unsupervised Learning.mp4

06:00

5. Reinforcement Learning.mp4

02:25

6. What is Deep Learning and ML.mp4

04:00

7. What is Neural Network.mp4

04:51

8. How Deep Learning Process Works.mp4

04:18

9. Application of Deep Learning.mp4

04:51

10. Deep Learning Tools.mp4

04:46

11. MLops with AWS.mp4

03:31

5. End to End Deep Learning Project

1. What is Neuron.mp4

03:29

2. Multi-Layer Perceptron.mp4

07:47

3. Shallow vs Deep Neural Networks.mp4

01:59

4. Activation Function.mp4

06:56

5. What is Back Propagation.mp4

08:17

6. Optimizers in Deep Learning.mp4

06:32

7. Steps to Build Neural Network.mp4

07:03

8. Customer Churn Dataset Loading.mp4

04:21

9. Data Visualization Part 1.mp4

07:54

10. Data Visualization Part 2.mp4

09:14

11. Data Preprocessing.mp4

06:34

12. Import Neural Networks APIs.mp4

05:00

13. How to Get Input Shape and Class Weights.mp4

04:10

14. Neural Network Model Building.mp4

07:35

15. Model Summary Explanation.mp4

06:03

16. Model Training.mp4

08:02

17. Model Evaluation.mp4

03:20

18. Model Save and Load.mp4

05:36

19. Prediction on Real-Life Data.mp4

08:10

6. Introduction to Computer Vision with Deep Learning

1. Introduction to Computer Vision with Deep Learning.mp4

05:05

2. 5 Steps of Computer Vision Model Building.mp4

03:31

3. Fashion MNIST Dataset Download.mp4

07:28

4. Fashion MNIST Dataset Analysis.mp4

13:22

5. Train Test Split for Data.mp4

04:10

6. Deep Neural Network Model Building.mp4

05:54

7. Model Summary and Training.mp4

10:26

8. Discovering Overfitting - Early Stopping.mp4

11:04

9. Model Save and Load for Prediction.mp4

07:48

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

1. What is Convolutional Neural Network.mp4

08:54

2. Working Principle of CNN.mp4

09:07

3. Convolutional Filters.mp4

13:34

4. Feature Maps.mp4

07:43

5. Padding and Strides.mp4

10:02

6. Pooling Layers.mp4

10:06

7. Activation Function.mp4

09:13

8. Dropout.mp4

04:03

9. CNN Architectures Comparison.mp4

07:09

10. LeNet-5 Architecture Explained.mp4

08:39

11. AlexNet Architecture Explained.mp4

10:17

12. GoogLeNet (Inception V1) Architecture Explained.mp4

07:01

13. RestNet Architecture Explained.mp4

07:47

14. MobileNet Architecture Explained.mp4

15:52

15. EfficientNet Architecture Explained.mp4

15:11

8. Horses vs Humans Classification with Simple CNN

1. Overview of Image Classification using CNNs.mp4

06:41

2. Introduction to TensorFlow Datasets (TFDS).mp4

07:02

3. Download Humans or Horses Dataset Part 1.mp4

06:46

4. Download Humans or Horses Dataset Part 2.mp4

11:11

5. Use of Image Data Generator.mp4

09:52

6. Data Display in Subplots Matrix.mp4

12:33

7. CNN Introduction.mp4

06:21

8. Building CNN Model.mp4

08:52

9. CNN Parameter Calculation.mp4

07:32

10. CNN Parameter Calculations Part 2.mp4

06:33

11. CNN Parameter Calculations Part 3.mp4

09:23

12. Model Training.mp4

08:49

13. Model Load and Save.mp4

06:33

14. Image Class Prediction.mp4

11:23

9. Building Cats and Dogs Classifier with Regularized CNN

1. What is Overfitting.mp4

04:44

2. L1, L2 and Early Stopping Regularization.mp4

05:40

3. How Dropout and Batch Normalization Prevents Overfitting.mp4

06:00

4. What is Data Augmentation [Theory].mp4

05:47

5. Sample Data Load with ImageDataGenerator for Augmentation.mp4

08:58

6. Random Rotation Augmentation.mp4

07:43

7. Random Shift Augmentation.mp4

04:33

8. Other Types of Data Augmentation.mp4

07:47

9. All Types of Augmentation at Once.mp4

03:26

10. TensorFlow TFDS and Cats vs Dogs Data Download.mp4

05:41

11. Store Data in Local Directory.mp4

08:11

12. Load Dataset for Baseline Classifier.mp4

11:02

13. Building Baseline CNN Classifier.mp4

07:40

14. How to Calculate Size of Output Layers of CNN and MaxPool.mp4

10:43

15. How to Calculate Number of Parameters in CNN and FCN.mp4

10:04

16. Model Training and Layers Analysis.mp4

06:08

17. Model Training and Validation Accuracy Plot.mp4

03:57

18. Building Dataset for Regularized CNN.mp4

03:59

19. Regularized CNN Model Building and Training.mp4

05:54

20. Training Log Analysis.mp4

03:06

21. Load Model and Do the Prediction.mp4

13:17

22. CNN Model Visualization.mp4

03:11

10. Flowers Classification with Transfer Learning and CNN

1. Transfer Learning Introduction.mp4

07:35

2. Load Flowers Dataset for Classification.mp4

08:23

3. Download Flowers Data.mp4

07:11

4. Flowers Data Visualization.mp4

07:45

5. Preparing Data with Image Data Generator.mp4

09:55

6. Baseline CNN Model Building.mp4

06:40

7. How to Calculate Number of Parameters in CNN.mp4

11:09

8. Baseline CNN Model Training.mp4

06:40

9. Train Model with TFDS Data Without Saving Locally Part 1.mp4

07:10

10. Train Model with TFDS Data Without Saving Locally Part 2.mp4

06:03

11. import VGG16 from Keras.mp4

06:19

12. Data Augmentation for Training.mp4

04:37

13. Make CNN Model with VGG16 Transfer Learning.mp4

07:56

14. Model Training for Better Accuracy.mp4

03:23

15. Train Any Model for Transfer Learning.mp4

08:51

16. Save and Load Model with Class Names.mp4

07:23

17. Online Prediction of Flowers Classes.mp4

13:26

11. Introduction to NLP

1. Introduction to NLP.mp4

05:00

2. What are Key NLP Techniques.mp4

04:40

3. Overview of NLP Tools.mp4

07:44

4. Common Challenges in NLP.mp4

02:37

5. Bag of Words - The Simples Word Embedding Technique.mp4

04:16

6. Term Frequency - Inverse Document Frequency (TF-IDF).mp4

02:59

7. Load Spam Dataset.mp4

03:30

8. Text Preprocessing.mp4

05:36

9. Feature Engineering.mp4

04:39

10. Pair Plot.mp4

05:25

11. Train Test Split.mp4

02:27

12. TF-IDF Vectorization.mp4

05:39

13. Model Evaluation and Prediction on Real Data.mp4

05:08

14. Model Load and Store.mp4

04:24

Description

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

What You'll Learn?

The basics of Python programming language
Foundational concepts of deep learning and neural networks
How to build a neural network from scratch using Python
Advanced techniques in deep learning using TensorFlow 2.0
Convolutional neural networks (CNNs) for image classification and object detection
Recurrent neural networks (RNNs) for sequential data such as time series and natural language processing
Generative adversarial networks (GANs) for generating new data samples
Transfer learning in deep learning
Reinforcement learning and its applications in AI
Deployment options for deep learning models
Applications of deep learning in AI, such as computer vision, natural language processing, and speech recognition
The current and future trends in deep learning and AI, as well as ethical and societal implications.

Who is this for?

Data scientists, analysts, and engineers who want to expand their knowledge and skills in machine learning.

Developers and programmers who want to learn how to build and deploy machine learning models in a production environment.

Researchers and academics who want to understand the latest developments and applications of machine learning.

Business professionals and managers who want to learn how to apply machine learning to solve real-world problems in their organizations.

Students and recent graduates who want to gain a solid foundation in machine learning and pursue a career in data science or artificial intelligence.

Anyone who is curious about machine learning and wants to learn more about its applications and how it is used in the industry.

What You Need to Know?

Basic understanding of programming concepts and mathematics

A laptop or a computer with an internet connection

A willingness to learn and explore the exciting field of deep learning and artificial intelligence

More details

Description
This comprehensive course covers the latest advancements in deep learning and artificial intelligence using Python. Designed for both beginner and advanced students, this course teaches you the foundational concepts and practical skills necessary to build and deploy deep learning models.
Module 1: Introduction to Python and Deep Learning
Overview of Python programming language
Introduction to deep learning and neural networks
Module 2: Neural Network Fundamentals
Understanding activation functions, loss functions, and optimization techniques
Overview of supervised and unsupervised learning
Module 3: Building a Neural Network from Scratch
Hands-on coding exercise to build a simple neural network from scratch using Python
Module 4: TensorFlow 2.0 for Deep Learning
Overview of TensorFlow 2.0 and its features for deep learning
Hands-on coding exercises to implement deep learning models using TensorFlow
Module 5: Advanced Neural Network Architectures
Study of different neural network architectures such as feedforward, recurrent, and convolutional networks
Hands-on coding exercises to implement advanced neural network models
Module 6: Convolutional Neural Networks (CNNs)
Overview of convolutional neural networks and their applications
Hands-on coding exercises to implement CNNs for image classification and object detection tasks
Module 7: Recurrent Neural Networks (RNNs)
Overview of recurrent neural networks and their applications
Hands-on coding exercises to implement RNNs for sequential data such as time series and natural language processing

By the end of this course, you will have a strong understanding of deep learning and its applications in AI, and the ability to build and deploy deep learning models using Python and TensorFlow 2.0. This course will be a valuable asset for anyone looking to pursue a career in AI or simply expand their knowledge in this exciting field.
Who this course is for:
Data scientists, analysts, and engineers who want to expand their knowledge and skills in machine learning.
Developers and programmers who want to learn how to build and deploy machine learning models in a production environment.
Researchers and academics who want to understand the latest developments and applications of machine learning.
Business professionals and managers who want to learn how to apply machine learning to solve real-world problems in their organizations.
Students and recent graduates who want to gain a solid foundation in machine learning and pursue a career in data science or artificial intelligence.
Anyone who is curious about machine learning and wants to learn more about its applications and how it is used in the industry.

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I am AVP, Data Science at Join Ventures, and have been Ph.D. Scholar at the Indian Institute of Technology (IIT), Kharagpur. I also co-founded a company, mBreath Technologies. I have 8+ years of experience in data science, team management, business development, and customer profiling. I have worked with startups and MNCs. You can join me at my YouTube channel KGP Talkie.

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