Deep Learning and Neural Networks in Python (Data Science)

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Chaitanya attaluri

5:44:10

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1. Introduction To Deep Learning and Course Outline

1.1 course outline.pptx

1. Course Outline.mp4

06:57

2.1 1.Introduction to Deep Learning.docx

2. Introduction to Deep Learning.mp4

05:55

2. Introduction to Artificial Neural Networks(ANN)

1.1 2.Introduction to Artificial Neural Networks.docx

1. Introduction to ANN.mp4

07:50

3. Activation Functions

1.1 3.Activation Functions.docx

1. Activation Functions.mp4

13:04

4. Creating Neural Networks in Python

1.1 4.1 Creating Neural Networks in Python-Single Layered Neural Network.docx

1. Creating Neural Networks in Python-Single Layered Neural Network.mp4

16:41

2.1 4.2 Creating a Neural Network with 2 layers.docx

2. Creating a Neural Network with 2 layers.mp4

07:15

3.1 4.3 Creating a Neural Network with Activation Functions.docx

3. Creating a Neural Network with Activation Functions.mp4

08:26

5. TensarFlow and Keras

1.1 5.1Introduction to TensarFlow and Keras.docx

1. Introduction to TensarFlow and Keras.mp4

08:46

2.1 5.2 Types of Tensors.docx

2. Types of Tensors.mp4

14:53

3.1 5.3 Common operations in TensorFlow.docx

3. Common operations in TensorFlow.mp4

14:05

4.1 5.4 Introduction to Keras.docx

4. Introduction to Keras.mp4

07:26

6. Creating (Artificial Neural Network) ANN Using TensorFlow and Keras

1.1 1.Creating ANN using Tensorflow and Keras asiignment solution.docx

1.2 6.Creating ANN Using TensorFlow and Keras.docx

1.3 housepricedata.csv

1. Creating ANN Using TensorFlow and Keras.mp4

27:32

7. Convolutional Neural Network(CNN)

1.1 7.1 Introduction to Convolutional Neural Network(CNN).docx

1. Introduction to Convolutional Neural Network(CNN).mp4

09:31

2.1 7.2 Convolutional Neural Network(CNN)Layers.docx

2. Convolutional Neural Network(CNN)Layers.mp4

11:39

3.1 7.3 Creating a convolutional Neural Network(CNN).docx

3. Creating a convolutional Neural Network(CNN).mp4

28:04

4.1 2.CNN Assignment Solution.docx

4.2 7.4 Making Prediction on a New Image and CNN Assignment.docx

4. Making Prediction on a New Image and CNN Assignment.mp4

07:50

8. Recurrent Neural Network(RNN)

1.1 8.1 Introduction to Recurrent Neural Network(RNN).docx

1. Introduction to Recurrent Neural Network(RNN).mp4

10:55

2.1 3.RNN Assignment Solution.docx

2.2 8.2 Recurrent Neural Network Program.docx

2.3 Google Stock Price Test.csv

2.4 Google Stock Price Train.csv

2. Recurrent Neural Network Program and Assignment.mp4

21:22

9. Natural Language Processing(NLP)

1.1 9.1 Natural Language Processing(NLP)-Introduction.docx

1. Natural Language Processing(NLP)-Introduction.mp4

03:22

2.1 9.2 Tokenization.docx

2. Tokenization.mp4

08:10

3.1 9.3 Stemming.docx

3. Stemming.mp4

05:52

4.1 9.4 Lemmatization.docx

4. Lemmatization.mp4

06:26

5.1 9.5 Stop Words.docx

5. Stop words.mp4

07:40

6.1 9.6 Parts of Speech(POS).docx

6. Parts of Speech(POS).mp4

02:38

7.1 9.7 Feature Extraction in NLP.docx

7. Feature Extraction in NLP.mp4

11:41

8.1 9.7 Feature Extraction in NLP.docx

8.2 review polarity.rar

8. Natural Language Processing(NLP)-Program.mp4

26:38

10. Computer Vision

1.1 10.1 Computer Vision-Introduction.docx

1. Computer Vision-Introduction.mp4

04:57

2.1 10.2 Reading and Displaying images using OpenCV.docx

2.2 tajmahal.zip

2. Reading and Displaying images using OpenCV.mp4

05:16

3.1 10.3 Resizing the Image using OpenCV.docx

3. Resizing the Image using OpenCV.mp4

03:49

4.1 10.4 Face Detection.docx

4.2 haarcascade frontalface default.zip

4.3 haarcascade righteye 2splits.zip

4.4 human.zip

4. Face Detection.mp4

11:52

5.1 10.5 Contours.docx

5.2 couples.zip

5. contours.mp4

06:53

6.1 10.6 Capturing Video.docx

6.2 dance.mp4

00:12

6. capturing video.mp4

10:33

Description

Learn to create Deep Learning models in Python

What You'll Learn?

Deep Learning and Artificial Neural Networks Theory
Types of Activation Functions
Creating Artificail Neural Network (ANN)using Activation functions
Tensof flow and keras Introduction
Creating Convolutional Neural Network (CNN) using Teras flow and keras
Creating Recurrent Neural Networks(RNN)
Natural Language Processing(NLP) theory and Practice program
Understanding about Computur Vision
Assignment on ANN,CNN,RNN

Who is this for?

Beginner on Deep Learning

What You Need to Know?

Basic Python Programing Language

Knowledge on Machine Learning models

More details

Description
This course introduces students to the theory and practice of deep learning, a subfield of machine learning that focuses on learning representations of data. Deep learning has gained significant attention and has achieved state-of-the-art results across various domains such as computer vision, natural language processing, and reinforcement learning. The course will cover foundational topics including neural networks, activation functions, optimization techniques, and regularization methods. Advanced topics such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), Natural Language Processing(NLP),and computer visionÂ will also be explored. Practical aspects of deep learning, including implementation using popular frameworks (e.g., TensorFlow, PyTorch) and the use of GPUs for acceleration, will be an integral part of the course. Real-world applications and case studies will be discussed to illustrate the power and potential of deep learning in solving complex problems. Throughout the course, students will gain hands-on experience through programming assignments where they will apply deep learning techniques to a challenging problem in their area of interest.
Learning Objectives:
Develop a solid understanding of the theoretical foundations of deep learning, including neural network architectures, activation functions, and optimization algorithms
Gain practical experience in implementing deep learning models using TensorFlow and keras
Explore advanced deep learning techniques such as CNNs, RNNs, and ANNs, and understand their applications in various domains.
Learn to evaluate and interpret deep learning models using appropriate metrics and visualization tools.
Apply deep learning principles to real-world problems through hands-on Assignment and case studies
Learning Outcomes: By the end of this course, students should be able to:
Understand the fundamental principles of deep learning and its key algorithms.
Implement and train various types of neural networks using popular deep learning frameworks.
Evaluate and interpret deep learning models for different tasks such as classification, regression, and sequence prediction.
Apply deep learning techniques to real-world problems and datasets.
Critically assess current research in deep learning and stay updated with advancements in the field.
Who this course is for:
Beginner on Deep Learning

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Python

Chaitanya attaluri

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I done masters in university of east London. I am a skilled and experienced instructor with a passion for sharing knowledge and helping others excel in their learning journey. Throughout my career, had the opportunity to work with a diverse range of organizations, This real-world experience allows me to provide students with practical insights and a deep understanding of Subject in programming Courses

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