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Deep Learning Architectures: From ANNs to Transformers
Deep Learning Architectures: From ANNs to Transformers
📑 Lessons : 10 Lesson
🕒 Duration : 3 days
🎚️ Skill level : Advanced
📑 Language : English
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Our Deep Learning Architecture course is a comprehensive, hands-on exploration of deep learning models, from artificial neural networks (ANNs) to transformers. Designed for those with foundational knowledge in neural networks, this course provides a structured journey through the evolution of deep learning, focusing on applications in NLP, computer vision, and other domains. This deep learning online course uses Python, TensorFlow, and PyTorch to implement and optimize models, offering a practical approach that bridges theoretical understanding and real-world application. Participants completing this deep learning certification will possess the skills needed to design and deploy advanced architectures, making them valuable assets in the field of machine learning and deep learning.
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What you'll learn
✔️ Core Principles of Deep Learning Architectures: Discover the fundamentals of deep learning, tracing the evolution of neural networks from ANNs to advanced architectures.
✔️ Training Techniques: Learn key model training techniques like backpropagation and gradient descent.
✔️ Hands-On CNNs and RNNs: Master Convolutional Neural Networks (CNNs) for computer vision and Recurrent Neural Networks (RNNs) for sequential data processing.
✔️ Introduction to Transformers: Explore the structure and applications of transformer architectures in NLP and computer vision.
✔️ Practical Implementation: Build, fine-tune, and deploy deep learning models with TensorFlow and PyTorch.
✔️ Capstone Project: Apply your knowledge in a real-world project where you’ll design, optimize, and deploy a custom deep learning model.
Prerequisites
✔️ Basic experience with Python programming and machine learning concepts.
✔️ Familiarity with core neural network principles.
Course Content
Module 1: Introduction to Deep Learning Architectures (Duration: 1 hour)
✔️ History and evolution of neural networks
✔️ Key components: Layers, activation functions, loss functions
✔️ Overview of modern deep learning architectures
✔️ Hands-on: Setting up deep learning environments (TensorFlow/PyTorch)
Module 2: Artificial Neural Networks (ANNs) (Duration: 3 hours)
✔️ Architecture of a simple ANN
✔️ Activation functions: Sigmoid, ReLU, Tanh
✔️ Backpropagation and gradient descent
✔️ Hands-on: Building and training an ANN for image classification
Module 3: Convolutional Neural Networks (CNNs) (Duration: 3 hours)
✔️ CNN architecture: Convolutional layers, pooling layers, and fully connected layers
✔️ Use cases: Image recognition, object detection, and segmentation
✔️ Hands-on: Implementing a CNN using TensorFlow/PyTorch
Module 4: Advanced CNN Architectures (Duration: 3 hours)
✔️ Deep CNNs: VGG, ResNet, Inception
✔️ Transfer learning and pre-trained models
✔️ Hands-on: Fine-tuning a pre-trained model on a custom dataset
Module 5: Recurrent Neural Networks (RNNs) (Duration: 2 hours)
✔️ Understanding sequential data and RNNs
✔️ Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs)
✔️ Hands-on: Implementing LSTM and GRU networks for text generation
Module 6: Attention Mechanisms and Seq2Seq Models (Duration: 2 hours)
✔️ Introduction to attention mechanisms
✔️ Sequence-to-sequence models and applications in translation and summarization
✔️ Hands-on: Building a Seq2Seq model with attention for language translation
Module 7: Introduction to Transformers (Duration: 3 hours)
✔️ The transformer architecture: Self-attention and positional encoding
✔️ BERT, GPT, and other transformer-based models
Module 8: Generative Models and Autoencoders (Duration: 2 hours)
✔️ Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs)
✔️ Applications: Image generation, anomaly detection
✔️ Hands-on: Building a VAE for image generation
Module 9: Model Optimization and Deployment (Duration: 2 hours)
✔️ Model optimization techniques: Batch normalization, dropout, learning rate scheduling
✔️ Deploying deep learning models with TensorFlow Serving and Flask
✔️ Hands-on: Optimizing and deploying a deep learning model
Module 10: Capstone Project: Designing and Deploying a Deep Learning Model (Duration: 3 hours)
✔️ Problem identification and data preparation
✔️ Choosing and implementing the right architecture
✔️ Hands-on: Building, training, and deploying a custom deep learning model
Frequently Asked Questions
📍 Question1: What is the format of this deep learning architecture course?
Answer: This is a virtual deep learning course, conducted live online to offer an interactive learning experience. Participants will engage in hands-on projects and coding exercises to reinforce their understanding of complex architectures.
📍 Question2: Will I earn a certification upon completing this course?
Answer: Yes, participants who complete this course will receive a deep learning certification, which can help professionals advance their careers in machine learning and artificial intelligence fields.
📍 Question3: Does this course cover the latest architectures in machine learning?
Answer: Absolutely! This online deep learning course includes both foundational models like ANNs and cutting-edge architectures such as transformers, covering the entire spectrum of machine learning model architectures.
📍 Question4: Is prior experience with deep learning required?
Answer: This course is designed for learners who have a basic understanding of neural networks and machine learning. It’s an advanced course that builds on foundational knowledge, making it suitable for professionals looking to deepen their expertise.
📍 Question5: Will I gain practical skills from this architecture for machine learning course?
Answer: Yes! Our course emphasizes practical application, allowing participants to work with Python, TensorFlow, and PyTorch. By the end, you’ll have developed a strong skill set in building and deploying deep learning models.