20th August 2023
Why to Join This Course
Join Our Deep Learning Engineer Today!
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Curriculum for Deep Learning Engineer
Module 1: Neural Networks and Deep Learning Architectures (10 hours)
- Introduction to neural networks and deep learning
- Feedforward neural networks and backpropagation
- Activation functions and optimization techniques
- Regularization and dropout
- Model selection and Hyperparameter tuning
- Hands-on project: Building and training a basic neural network.
Module 2: Convolutional Neural Networks (CNNs) for Computer Vision (10 hours)
- Introduction to CNNs and their applications
- CNN architecture and layers (convolution, pooling, etc.)
- Transfer learning and fine-tuning pre-trained models
- Object detection and image segmentation with CNNs
- Deep learning frameworks for computer vision (e.g., TensorFlow, PyTorch)
- Hands-on project: Implementing a CNN for image classification.
Module 3: Recurrent Neural Networks (RNNs) for Natural Language Processing (10 hours)
- Introduction to RNNs and their applications
- RNN architecture and types (LSTM, GRU)
- Word embeddings and text preprocessing
- Language modeling and sentiment analysis with RNNs
- Text generation and machine translation
- Hands-on project: Building a sentiment analysis model using RNNs.
Module 4: Generative Models and Adversarial Networks (10 hours)
- Introduction to generative models and their applications
- Autoencoders and variational autoencoders (VAEs)
- Generative adversarial networks (GANs)
- Image generation and style transfer with GANs
- Text synthesis and other applications of generative models
- Hands-on project: Creating a generative adversarial network for image generation.
Module 5: Real-World Projects (5 hours)
- Applying deep learning techniques to real-world projects and applications.
- Working on a comprehensive deep learning project to showcase learned skills.
- Demonstration and presentation of project outcomes.
For admission to this Professional Certificate course in Data Analyst Course, candidates should have:
- Basic Programming Knowledge
- Database Fundamentals
- Data Analytics Basics
- Mathematics and Statistics (recommended but not mandatory)
- Data Analysis Tools (e.g., Pandas, NumPy, SQL) (recommended but not mandatory)
Upon completion of this course, students will be able to:
- Explain the concepts and components of neural networks and deep learning architectures.
- Implement and train convolutional neural networks (CNNs) for computer vision tasks, such as image classification and object detection.
- Build and train recurrent neural networks (RNNs) for natural language processing tasks, such as language modeling and sentiment analysis.
- Explore generative models and adversarial networks for tasks like image generation and text synthesis.
- Fine-tune deep learning models and optimize their performance for specific applications.
- Demonstrate their deep learning skills through practical projects and applications.