Deep Learning: Python Deep Learning Masterclass

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Deep Learning: Python Deep Learning Masterclass

Name: Deep Learning: Python Deep Learning Masterclass
Rating: 4.2 (364 reviews)
Author: AISciences

Unlock the Secrets of Deep Learning: Dive Deep into CNNs, RNNs, NLP, Chatbots, and Recommender Systems - Deep Learning

updated on icon Updated on Apr, 2024

language icon Language - English

person icon AISciences

category icon Business Analytics & Intelligence,Neural Networks,Deep Learning

Lectures -576

Duration -58.5 hours

4.2 ★★★★ ★

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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

What will you learn in this course:

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

What are the prerequisites for this course?

Understanding Python fundamentals is recommended for implementing deep learning concepts covered in the course.

Deep Learning: Python Deep Learning Masterclass

Curriculum

Check out the detailed breakdown of what’s inside the course

Deep Learning: Deep Neural Network for Beginners Using Python
85 Lectures

Introduction : Introduction to Instructor 02:53 02:53
Introduction : Introduction to course 03:36 03:36
Basics of Deep Learning: Problem to Solve Part 1 02:00 02:00
Basics of Deep Learning: Problem to Solve Part 2 02:26 02:26
Basics of Deep Learning: Problem to Solve Part 3 01:42 01:42
Basics of Deep Learning: Linear Equation 03:18 03:18
Basics of Deep Learning: Linear Equation Vectorized 03:00 03:00
Basics of Deep Learning: 3D Feature Space 03:46 03:46
Basics of Deep Learning: N Dimensional Space 02:30 02:30
Basics of Deep Learning: Theory of Perceptron 01:46 01:46
Basics of Deep Learning: Implementing Basic Perceptron 05:37 05:37
Basics of Deep Learning: Logical Gates for Perceptrons 02:46 02:46
Basics of Deep Learning: Perceptron Training Part 1 01:40 01:40
Basics of Deep Learning: Perceptron Training Part 2 03:40 03:40
Basics of Deep Learning: Learning Rate 03:14 03:14
Basics of Deep Learning: Perceptron Training Part 3 03:31 03:31
Basics of Deep Learning: Perceptron Algorithm 01:00 01:00
Basics of Deep Learning: Coading Perceptron Algo (Data Reading & Visualization) 07:22 07:22
Basics of Deep Learning: Coading Perceptron Algo (Perceptron Step) 06:43 06:43
Basics of Deep Learning: Coading Perceptron Algo (Training Perceptron) 06:43 06:43
Basics of Deep Learning: Coading Perceptron Algo (Visualizing the Results) 03:53 03:53
Basics of Deep Learning: Problem with Linear Solutions 02:32 02:32
Basics of Deep Learning: Solution to Problem 01:03 01:03
Basics of Deep Learning: Error Functions 02:21 02:21
Basics of Deep Learning: Discrete vs Continuous Error Function 02:25 02:25
Basics of Deep Learning: Sigmoid Function 03:01 03:01
Basics of Deep Learning: Multi-Class Problem 01:18 01:18
Basics of Deep Learning: Problem of Negative Scores 03:02 03:02
Basics of Deep Learning: Need of Softmax 01:22 01:22
Basics of Deep Learning: Coding Softmax 04:06 04:06
Basics of Deep Learning: One Hot Encoding 02:41 02:41
Basics of Deep Learning: Maximum Likelihood Part 1 05:30 05:30
Basics of Deep Learning: Maximum Likelihood Part 2 03:48 03:48
Basics of Deep Learning: Cross Entropy 04:06 04:06
Basics of Deep Learning: Cross Entropy Formulation 07:38 07:38
Basics of Deep Learning: Multi Class Cross Entropy 03:51 03:51
Basics of Deep Learning: Cross Entropy Implementation 04:14 04:14
Basics of Deep Learning: Sigmoid Function Implementation 00:57 00:57
Basics of Deep Learning: Output Function Implementation 02:10 02:10
Deep Learning: Introduction to Gradient Decent 05:21 05:21
Deep Learning: Convex Functions 02:31 02:31
Deep Learning: Use of Derivatives 03:13 03:13
Deep Learning: How Gradient Decent Works 03:34 03:34
Deep Learning: Gradient Step 01:54 01:54
Deep Learning: Logistic Regression Algorithm 01:37 01:37
Deep Learning: Data Visualization and Reading 06:10 06:10
Deep Learning: Updating Weights in Python 04:14 04:14
Deep Learning: Implementing Logistic Regression 12:44 12:44
Deep Learning: Visualization and Results 08:43 08:43
Deep Learning: Gradient Decent vs Perceptron 04:35 04:35
Deep Learning: Linear to Non Linear Boundaries 04:42 04:42
Deep Learning: Combining Probabilities 02:07 02:07
Deep Learning: Weighted Sums 03:01 03:01
Deep Learning: Neural Network Architecture 12:09 12:09
Deep Learning: Layers and DEEP Networks 04:44 04:44
Deep Learning:Multi Class Classification 02:48 02:48
Deep Learning: Basics of Feed Forward 07:51 07:51
Deep Learning: Feed Forward for DEEP Net 04:57 04:57
Deep Learning: Deep Learning Algo Overview 01:57 01:57
Deep Learning: Basics of Back Propagation 06:32 06:32
Deep Learning: Updating Weights 02:46 02:46
Deep Learning: Chain Rule for BackPropagation 05:53 05:53
Deep Learning: Sigma Prime 02:23 02:23
Deep Learning: Data Analysis NN Implementation 05:25 05:25
Deep Learning: One Hot Encoding (NN Implementation) 03:11 03:11
Deep Learning: Scaling the Data (NN Implementation) 01:48 01:48
Deep Learning: Splitting the Data (NN Implementation) 04:55 04:55
Deep Learning: Helper Functions (NN Implementation) 02:18 02:18
Deep Learning: Training (NN Implementation) 12:25 12:25
Deep Learning: Testing (NN Implementation) 03:21 03:21
Optimizations: Underfitting vs Overfitting 05:19 05:19
Optimizations: Early Stopping 03:51 03:51
Optimizations: Quiz 00:58 00:58
Optimizations: Solution & Regularization 05:59 05:59
Optimizations: L1 & L2 Regularization 03:12 03:12
Optimizations: Dropout 02:59 02:59
Optimizations: Local Minima Problem 02:55 02:55
Optimizations: Random Restart Solution 04:27 04:27
Optimizations: Vanishing Gradient Problem 04:16 04:16
Optimizations: Other Activation Functions 03:19 03:19
Final Project: Final Project Part 1 11:19 11:19
Final Project: Final Project Part 2 13:16 13:16
Final Project: Final Project Part 3 12:58 12:58
Final Project: Final Project Part 4 12:19 12:19
Final Project: Final Project Part 5 08:06 08:06

Deep Learning CNN : Convolutional Neural Network With Python
78 Lectures

Deep Learning: Recurrent neural networks with Python
136 Lectures

NLP- Natural Language Processing In python(Theory & Projects)
223 Lectures

Advanced Chatbots with Deep Learning & Python
26 Lectures

Recommender Systems: An Applied Approach Using Deep Learning
27 Lectures

Bonus
1 Lectures

Instructor Details

AISciences

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