2024 Deep Learning for Beginners with Python

2024 Deep Learning for Beginners with Python, available at $59.99, has an average rating of 4.6, with 181 lectures, based on 77 reviews, and has 6690 subscribers.

You will learn about 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. This course is ideal for individuals who are Data scientists, analysts, and engineers who want to expand their knowledge and skills in machine learning. or Developers and programmers who want to learn how to build and deploy machine learning models in a production environment. or Researchers and academics who want to understand the latest developments and applications of machine learning. or Business professionals and managers who want to learn how to apply machine learning to solve real-world problems in their organizations. or Students and recent graduates who want to gain a solid foundation in machine learning and pursue a career in data science or artificial intelligence. or Anyone who is curious about machine learning and wants to learn more about its applications and how it is used in the industry. It is particularly useful for Data scientists, analysts, and engineers who want to expand their knowledge and skills in machine learning. or Developers and programmers who want to learn how to build and deploy machine learning models in a production environment. or Researchers and academics who want to understand the latest developments and applications of machine learning. or Business professionals and managers who want to learn how to apply machine learning to solve real-world problems in their organizations. or Students and recent graduates who want to gain a solid foundation in machine learning and pursue a career in data science or artificial intelligence. or Anyone who is curious about machine learning and wants to learn more about its applications and how it is used in the industry.

Enroll now: 2024 Deep Learning for Beginners with Python

Summary

Title: 2024 Deep Learning for Beginners with Python

Price: $59.99

Average Rating: 4.6

Number of Lectures: 181

Number of Published Lectures: 181

Number of Curriculum Items: 181

Number of Published Curriculum Objects: 181

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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 Should Attend

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.

Target Audiences

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.

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.

Course Curriculum

Chapter 1: Course Setup

Lecture 1: Course Introduction and How to Download Code Files

Lecture 2: Google Colab Introduction

Lecture 3: Deep Learning Environment Setup [Optional]

Lecture 4: Jupyter Notebook Introduction

Chapter 2: Python for Deep Learning

Lecture 1: Python Introduction Part 1

Lecture 2: Python Introduction Part 2

Lecture 3: Python Introduction Part 3

Lecture 4: Numpy Introduction Part 1

Lecture 5: Numpy Introduction Part 2

Lecture 6: Pandas Introduction

Lecture 7: Matplotlib Introduction Part 1

Lecture 8: Matplotlib Introduction Part 2

Lecture 9: Seaborn Introduction Part 1

Lecture 10: Seaborn Introduction Part 2

Chapter 3: Introduction to Machine Learning

Lecture 1: Classical Machine Learning Introduction

Lecture 2: Logistic Regression

Lecture 3: Support Vector Machine – SVM

Lecture 4: Decision Tree

Lecture 5: Random Forest

Lecture 6: L2 Regularization

Lecture 7: L1 Regularization

Lecture 8: Model Evaluation

Lecture 9: ROC-AUC Curve

Lecture 10: Code Along in Python Part 1

Lecture 11: Code Along in Python Part 2

Lecture 12: Code Along in Python Part 3

Lecture 13: Code Along in Python Part 4

Chapter 4: Introduction to Deep Learning and TensorFlow

Lecture 1: Machine Learning Process Introduction

Lecture 2: Types of Machine Learning

Lecture 3: Supervised Learning

Lecture 4: Unsupervised Learning

Lecture 5: Reinforcement Learning

Lecture 6: What is Deep Learning and ML

Lecture 7: What is Neural Network

Lecture 8: How Deep Learning Process Works

Lecture 9: Application of Deep Learning

Lecture 10: Deep Learning Tools

Lecture 11: MLops with AWS

Chapter 5: End to End Deep Learning Project

Lecture 1: What is Neuron

Lecture 2: Multi-Layer Perceptron

Lecture 3: Shallow vs Deep Neural Networks

Lecture 4: Activation Function

Lecture 5: What is Back Propagation

Lecture 6: Optimizers in Deep Learning

Lecture 7: Steps to Build Neural Network

Lecture 8: Customer Churn Dataset Loading

Lecture 9: Data Visualization Part 1

Lecture 10: Data Visualization Part 2

Lecture 11: Data Preprocessing

Lecture 12: Import Neural Networks APIs

Lecture 13: How to Get Input Shape and Class Weights

Lecture 14: Neural Network Model Building

Lecture 15: Model Summary Explanation

Lecture 16: Model Training

Lecture 17: Model Evaluation

Lecture 18: Model Save and Load

Lecture 19: Prediction on Real-Life Data

Chapter 6: Introduction to Computer Vision with Deep Learning

Lecture 1: Introduction to Computer Vision with Deep Learning

Lecture 2: 5 Steps of Computer Vision Model Building

Lecture 3: Fashion MNIST Dataset Download

Lecture 4: Fashion MNIST Dataset Analysis

Lecture 5: Train Test Split for Data

Lecture 6: Deep Neural Network Model Building

Lecture 7: Model Summary and Training

Lecture 8: Discovering Overfitting – Early Stopping

Lecture 9: Model Save and Load for Prediction

Chapter 7: Introduction to Convolutional Neural Networks [Theory and Intuitions]

Lecture 1: What is Convolutional Neural Network?

Lecture 2: Working Principle of CNN

Lecture 3: Convolutional Filters

Lecture 4: Feature Maps

Lecture 5: Padding and Strides

Lecture 6: Pooling Layers

Lecture 7: Activation Function

Lecture 8: Dropout

Lecture 9: CNN Architectures Comparison

Lecture 10: LeNet-5 Architecture Explained

Lecture 11: AlexNet Architecture Explained

Lecture 12: GoogLeNet (Inception V1) Architecture Explained

Lecture 13: RestNet Architecture Explained

Lecture 14: MobileNet Architecture Explained

Lecture 15: EfficientNet Architecture Explained

Chapter 8: Horses vs Humans Classification with Simple CNN

Lecture 1: Overview of Image Classification using CNNs

Lecture 2: Introduction to TensorFlow Datasets (TFDS)

Lecture 3: Download Humans or Horses Dataset Part 1

Lecture 4: Download Humans or Horses Dataset Part 2

Lecture 5: Use of Image Data Generator

Lecture 6: Data Display in Subplots Matrix

Lecture 7: CNN Introduction

Lecture 8: Building CNN Model

Lecture 9: CNN Parameter Calculation

Lecture 10: CNN Parameter Calculations Part 2

Lecture 11: CNN Parameter Calculations Part 3

Instructors

Laxmi Kant | KGP Talkie
AVP, Data Science Join Ventures | IIT Kharagpur | KGPTalkie

Rating Distribution

1 stars: 2 votes
2 stars: 1 votes
3 stars: 5 votes
4 stars: 18 votes
5 stars: 51 votes

Frequently Asked Questions

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