Deep Learning: Python Deep Learning Masterclass
Unlock the Secrets of Deep Learning: Dive Deep into CNNs, RNNs, NLP, Chatbots, and Recommender Systems - Deep Learning
Business Analytics & Intelligence,Neural Networks,Deep Learning
Lectures -576
Duration -58.5 hours
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Course Description
Welcome to the ultimate Deep Learning masterclass! This comprehensive course integrates six modules, each providing a deep dive into different aspects of Deep Learning using Python. Whether you're a beginner looking to build a strong foundation or an intermediate learner seeking to advance your skills, this course offers practical insights, theoretical knowledge, and hands-on projects to cater to your needs.
Who Should Take This Course?
- Beginners interested in diving into the world of Deep Learning with Python
- Intermediate learners looking to enhance their Deep Learning skills
- Anyone aspiring to understand and apply Deep Learning concepts in real-world projects
Why This Course?
This course offers an all-encompassing resource that covers a wide range of Deep Learning topics, making it suitable for learners at different levels. From fundamentals to advanced concepts, you will gain a comprehensive understanding of Deep Learning using Python through practical applications.
What You Will Learn:
Module 1: Deep Learning Fundamentals with Python
Introduction to Deep Learning
Python basics for Deep Learning
Data preprocessing for Deep Learning algorithms
General machine learning concepts
Module 2: Convolutional Neural Networks (CNNs) in Depth
In-depth understanding of CNNs
Classical computer vision techniques
Basics of Deep Neural Networks
Architectures like LeNet, AlexNet, InceptionNet, ResNet
Transfer Learning and YOLO Case Study
Module 3: Recurrent Neural Networks (RNNs) and Sequence Modeling
Exploration of RNNs
Applications and importance of RNNs
Addressing vanishing gradients in RNNs
Modern RNNs: LSTM, Bi-Directional RNNs, Attention Models
Implementation of RNNs using TensorFlow
Module 4: Natural Language Processing (NLP) Fundamentals
Mastery of NLP
NLP foundations and significance
Text preprocessing techniques
Word embeddings: Word2Vec, GloVe, BERT
Deep Learning in NLP: Neural Networks, RNNs, and Advanced Models
Module 5: Developing Chatbots using Deep Learning
Building Chatbot systems
Deep Learning fundamentals for Chatbots
Comparison of conventional vs. Deep Learning-based Chatbots
Practical implementation of RNN-based Chatbots
Comprehensive package: Projects and advanced models
Module 6: Recommender Systems using Deep Learning
Application of Recommender Systems
Deep Learning's role in Recommender Systems
Benefits and challenges
Developing Recommender Systems with TensorFlow
Real-world project: Amazon Product Recommendation System
Final Capstone Project
Integration and application
Hands-on project: Developing a comprehensive Deep Learning solution
Final assessment and evaluation
This comprehensive course merges the essentials of Deep Learning, covering CNNs, RNNs, NLP, Chatbots, and Recommender Systems, offering a thorough understanding of Python-based implementations. Enroll now to gain expertise in various domains of Deep Learning through hands-on projects and theoretical foundations.
Keywords and Skills:
Deep Learning Mastery
Python Deep Learning Course
CNNs and RNNs Training
NLP Fundamentals Tutorial
Chatbot Development Workshop
Recommender Systems with TensorFlow
AI Course for Beginners
Hands-on Deep Learning Projects
Python Programming for AI
Comprehensive Deep Learning Curriculum
Who this course is for:
- Aspiring Data Scientists: Individuals aiming to specialize in deep learning and expand their knowledge in AI applications.
- Programmers and Developers: Those seeking to venture into the field of artificial intelligence and harness Python for deep learning projects.
- AI Enthusiasts and Learners: Anyone passionate about understanding CNNs, RNNs, NLP, chatbots, and recommender systems within the realm of deep learning.
- Students and Researchers: Those pursuing academic endeavors or conducting research in machine learning and AI-related fields.
- Professionals Exploring Career Shifts: Individuals interested in transitioning or advancing their careers in artificial intelligence and deep learning.
- Tech Enthusiasts: Individuals keen on exploring cutting-edge technologies and applications within the AI domain.
Goals
Hands-on Projects: Engage in practical projects spanning image analysis, language translation, chatbot creation, and recommendation systems.
Deep Learning Fundamentals: Understand the core principles of deep learning and its applications across various domains.
Convolutional Neural Networks (CNNs): Master image processing, object detection, and advanced CNN architectures like LeNet, AlexNet, and ResNet.
Recurrent Neural Networks (RNNs) and Sequence Modeling: Explore sequence processing, language understanding, and modern RNN variants such as LSTM.
Natural Language Processing (NLP) Essentials: Dive into text preprocessing, word embeddings, and deep learning applications in language understanding.
Integration and Application: Combine knowledge from different modules to develop comprehensive deep learning solutions through a capstone project.
Prerequisites
Understanding Python fundamentals is recommended for implementing deep learning concepts covered in the course.
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Curriculum
Check out the detailed breakdown of what’s inside the course
Deep Learning: Deep Neural Network for Beginners Using Python
85 Lectures
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Introduction : Introduction to Instructor 02:53 02:53
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Introduction : Introduction to course 03:36 03:36
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Basics of Deep Learning: Problem to Solve Part 1 02:00 02:00
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Basics of Deep Learning: Problem to Solve Part 2 02:26 02:26
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Basics of Deep Learning: Problem to Solve Part 3 01:42 01:42
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Basics of Deep Learning: Linear Equation 03:18 03:18
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Basics of Deep Learning: Linear Equation Vectorized 03:00 03:00
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Basics of Deep Learning: 3D Feature Space 03:46 03:46
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Basics of Deep Learning: N Dimensional Space 02:30 02:30
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Basics of Deep Learning: Theory of Perceptron 01:46 01:46
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Basics of Deep Learning: Implementing Basic Perceptron 05:37 05:37
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Basics of Deep Learning: Logical Gates for Perceptrons 02:46 02:46
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Basics of Deep Learning: Perceptron Training Part 1 01:40 01:40
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Basics of Deep Learning: Perceptron Training Part 2 03:40 03:40
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Basics of Deep Learning: Learning Rate 03:14 03:14
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Basics of Deep Learning: Perceptron Training Part 3 03:31 03:31
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Basics of Deep Learning: Perceptron Algorithm 01:00 01:00
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Basics of Deep Learning: Coading Perceptron Algo (Data Reading & Visualization) 07:22 07:22
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Basics of Deep Learning: Coading Perceptron Algo (Perceptron Step) 06:43 06:43
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Basics of Deep Learning: Coading Perceptron Algo (Training Perceptron) 06:43 06:43
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Basics of Deep Learning: Coading Perceptron Algo (Visualizing the Results) 03:53 03:53
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Basics of Deep Learning: Problem with Linear Solutions 02:32 02:32
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Basics of Deep Learning: Solution to Problem 01:03 01:03
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Basics of Deep Learning: Error Functions 02:21 02:21
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Basics of Deep Learning: Discrete vs Continuous Error Function 02:25 02:25
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Basics of Deep Learning: Sigmoid Function 03:01 03:01
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Basics of Deep Learning: Multi-Class Problem 01:18 01:18
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Basics of Deep Learning: Problem of Negative Scores 03:02 03:02
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Basics of Deep Learning: Need of Softmax 01:22 01:22
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Basics of Deep Learning: Coding Softmax 04:06 04:06
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Basics of Deep Learning: One Hot Encoding 02:41 02:41
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Basics of Deep Learning: Maximum Likelihood Part 1 05:30 05:30
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Basics of Deep Learning: Maximum Likelihood Part 2 03:48 03:48
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Basics of Deep Learning: Cross Entropy 04:06 04:06
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Basics of Deep Learning: Cross Entropy Formulation 07:38 07:38
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Basics of Deep Learning: Multi Class Cross Entropy 03:51 03:51
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Basics of Deep Learning: Cross Entropy Implementation 04:14 04:14
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Basics of Deep Learning: Sigmoid Function Implementation 00:57 00:57
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Basics of Deep Learning: Output Function Implementation 02:10 02:10
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Deep Learning: Introduction to Gradient Decent 05:21 05:21
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Deep Learning: Convex Functions 02:31 02:31
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Deep Learning: Use of Derivatives 03:13 03:13
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Deep Learning: How Gradient Decent Works 03:34 03:34
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Deep Learning: Gradient Step 01:54 01:54
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Deep Learning: Logistic Regression Algorithm 01:37 01:37
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Deep Learning: Data Visualization and Reading 06:10 06:10
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Deep Learning: Updating Weights in Python 04:14 04:14
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Deep Learning: Implementing Logistic Regression 12:44 12:44
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Deep Learning: Visualization and Results 08:43 08:43
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Deep Learning: Gradient Decent vs Perceptron 04:35 04:35
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Deep Learning: Linear to Non Linear Boundaries 04:42 04:42
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Deep Learning: Combining Probabilities 02:07 02:07
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Deep Learning: Weighted Sums 03:01 03:01
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Deep Learning: Neural Network Architecture 12:09 12:09
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Deep Learning: Layers and DEEP Networks 04:44 04:44
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Deep Learning:Multi Class Classification 02:48 02:48
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Deep Learning: Basics of Feed Forward 07:51 07:51
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Deep Learning: Feed Forward for DEEP Net 04:57 04:57
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Deep Learning: Deep Learning Algo Overview 01:57 01:57
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Deep Learning: Basics of Back Propagation 06:32 06:32
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Deep Learning: Updating Weights 02:46 02:46
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Deep Learning: Chain Rule for BackPropagation 05:53 05:53
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Deep Learning: Sigma Prime 02:23 02:23
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Deep Learning: Data Analysis NN Implementation 05:25 05:25
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Deep Learning: One Hot Encoding (NN Implementation) 03:11 03:11
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Deep Learning: Scaling the Data (NN Implementation) 01:48 01:48
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Deep Learning: Splitting the Data (NN Implementation) 04:55 04:55
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Deep Learning: Helper Functions (NN Implementation) 02:18 02:18
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Deep Learning: Training (NN Implementation) 12:25 12:25
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Deep Learning: Testing (NN Implementation) 03:21 03:21
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Optimizations: Underfitting vs Overfitting 05:19 05:19
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Optimizations: Early Stopping 03:51 03:51
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Optimizations: Quiz 00:58 00:58
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Optimizations: Solution & Regularization 05:59 05:59
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Optimizations: L1 & L2 Regularization 03:12 03:12
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Optimizations: Dropout 02:59 02:59
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Optimizations: Local Minima Problem 02:55 02:55
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Optimizations: Random Restart Solution 04:27 04:27
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Optimizations: Vanishing Gradient Problem 04:16 04:16
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Optimizations: Other Activation Functions 03:19 03:19
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Final Project: Final Project Part 1 11:19 11:19
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Final Project: Final Project Part 2 13:16 13:16
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Final Project: Final Project Part 3 12:58 12:58
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Final Project: Final Project Part 4 12:19 12:19
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Final Project: Final Project Part 5 08:06 08:06
Deep Learning CNN : Convolutional Neural Network With Python
78 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Deep Learning: Recurrent neural networks with Python
136 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
NLP- Natural Language Processing In python(Theory & Projects)
223 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Advanced Chatbots with Deep Learning & Python
26 Lectures
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Recommender Systems: An Applied Approach Using Deep Learning
27 Lectures
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Bonus
1 Lectures
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Instructor Details
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