Duration 5 days – 35 hrs
Overview
This training course provides a comprehensive introduction to Deep Learning using Python. Participants will learn the fundamentals of neural networks, implement various architectures, and use Python libraries like TensorFlow and Keras to build and train deep learning models. The course is designed to bridge the gap between theory and practice, equipping participants with the skills to solve real-world problems in fields like computer vision, natural language processing, and predictive analytics.
Objectives
- Understand the foundational concepts of Deep Learning and its applications.
- Build and train neural networks using Python libraries such as TensorFlow and Keras.
- Explore and implement various deep learning architectures, including CNNs, RNNs, and GANs.
- Preprocess and manage datasets for deep learning tasks.
- Fine-tune models for performance optimization and deploy them in real-world scenarios.
- Gain hands-on experience through practical exercises and projects.
Audience
- Data scientists and analysts looking to enhance their skills in deep learning.
- Software developers interested in building AI and ML-powered applications.
- Researchers and academics exploring AI and its applications.
- Professionals working in fields like finance, healthcare, and marketing with a focus on data-driven decision-making.
Prerequisites
- Proficiency in Python programming.
- Basic understanding of machine learning concepts.
- Familiarity with linear algebra, probability, and statistics is recommended but not mandatory.
Course Content
Day 1: Introduction to Deep Learning and Python for AI
- Overview of AI, ML, and Deep Learning
- Key components of Deep Learning
- Setting up the Python environment for Deep Learning
- Python, NumPy, Pandas, Matplotlib
- Introduction to TensorFlow and Keras
Day 2: Fundamentals of Neural Networks
- Understanding perceptrons and activation functions
- Forward propagation and backpropagation
- Loss functions and optimization algorithms
- Building and training simple neural networks in Keras
Day 3: Convolutional Neural Networks (CNNs)
- Concept and architecture of CNNs
- Layers: Convolution, Pooling, and Fully Connected
- Hands-on: Building CNNs for image classification
- Transfer learning and pre-trained models
Day 4: Recurrent Neural Networks (RNNs) and Advanced Architectures
- Understanding sequential data and RNNs
- Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU)
- Introduction to Transformers and attention mechanisms
- Hands-on: Building models for time-series forecasting and text generation
Day 5: Generative Adversarial Networks (GANs) and Deployment
- Overview of GANs and their applications
- Building GANs for data generation
- Model evaluation and optimization techniques
- Deploying deep learning models using Flask or FastAPI
- Capstone project: Building and deploying a complete deep learning solution
