2024 Deep Learning for Beginners with Python

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.

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.

TAKE THIS COURSE