Deep Learning with Python Training
Introduction to Deep Learning
Learn the fundamentals of deep learning, a subset of machine learning focused on neural networks. Understand the basic concepts, applications, and the significance of deep learning in modern AI systems.
Python for Deep Learning
Explore Python libraries and tools essential for deep learning. Learn about popular libraries such as TensorFlow, Keras, and PyTorch, and how to use them to build and train deep learning models.
Neural Networks Basics
Study the architecture of neural networks, including layers, activation functions, and loss functions. Understand how neural networks process and learn from data.
Building and Training Neural Networks
Learn how to build and train neural networks using Python. Explore techniques for creating custom models, optimizing hyperparameters, and evaluating model performance.
Convolutional Neural Networks (CNNs)
Dive into convolutional neural networks (CNNs), a type of neural network particularly effective for image processing tasks. Learn about convolutional layers, pooling, and how CNNs are used in image recognition and classification.
Recurrent Neural Networks (RNNs)
Explore recurrent neural networks (RNNs) for sequence data analysis. Understand concepts like LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) and their applications in natural language processing and time series forecasting.
Generative Adversarial Networks (GANs)
Study generative adversarial networks (GANs) and their ability to generate synthetic data. Learn about the architecture of GANs, including the generator and discriminator, and their applications in data augmentation and image synthesis.
Transfer Learning and Fine-Tuning
Learn about transfer learning and how to fine-tune pre-trained models for specific tasks. Understand how to leverage existing models to improve performance on new data with less training time.
Model Evaluation and Deployment
Explore techniques for evaluating deep learning models, including metrics and validation methods. Learn about model deployment strategies and how to integrate trained models into production systems.
Ethical Considerations in Deep Learning
Study the ethical considerations and challenges associated with deep learning. Learn about issues related to fairness, transparency, and the responsible use of AI technologies.
Case Studies and Practical Exercises
Engage in case studies and practical exercises to apply deep learning concepts. Work on real-world projects to build, train, and deploy deep learning models using Python.
Certification and Career Development
Prepare for deep learning certifications and advance your career in AI and machine learning. Get guidance on study resources, exam preparation, and career development strategies for roles involving deep learning with Python.
Deep Learning with Python syllabus
Introduction to Deep Learning
- Overview of Deep Learning: History, applications, and significance
- Neural Networks Basics: Neurons, layers, activation functions
- Deep Learning vs. Machine Learning: Key differences and use cases
Python Fundamentals for Deep Learning
- Introduction to Python for Data Science: Basics of Python programming
- Essential Libraries: NumPy, Pandas for data manipulation
- Data Visualization: Matplotlib, Seaborn for plotting data
Introduction to TensorFlow and Keras
- Overview of TensorFlow: TensorFlow vs. other frameworks
- Introduction to Keras: Keras API for building deep learning models
- Installing TensorFlow and Keras: Setting up the development environment
Building and Training Neural Networks
- Building Blocks of Neural Networks: Layers, activation functions, loss functions
- Sequential and Functional API: Creating models in Keras
- Training Neural Networks: Compiling models, specifying optimizer and loss, fitting data
Convolutional Neural Networks (CNNs)
- Introduction to CNNs: Architecture and applications in image recognition
- Convolutional Layers: Filters, strides, padding
- Pooling Layers: Max pooling, average pooling
Recurrent Neural Networks (RNNs)
- Introduction to RNNs: Understanding sequential data processing
- LSTM (Long Short-Term Memory) Networks: Architecture and applications
- GRU (Gated Recurrent Unit) Networks: Simplified RNN variant
Advanced Deep Learning Architectures
- Transfer Learning: Using pre-trained models (e.g., VGG, ResNet)
- Autoencoders: Unsupervised learning for dimensionality reduction
- Generative Adversarial Networks (GANs): Generating new data samples
Natural Language Processing (NLP) with Deep Learning
- Word Embeddings: Word2Vec, GloVe embeddings
- Sequence-to-Sequence Models: Encoder-Decoder architectures
- Attention Mechanism: Improving performance in NLP tasks
Advanced Topics in Deep Learning
- Hyperparameter Tuning: Grid search, random search, Bayesian optimization
- Regularization Techniques: Dropout, batch normalization
- Handling Imbalanced Data: Techniques for dealing with skewed datasets
Deep Learning for Computer Vision
- Object Detection: YOLO (You Only Look Once), SSD (Single Shot MultiBox Detector)
- Image Segmentation: Semantic and instance segmentation
- Image Generation and Style Transfer: Generating artistic images
Deep Learning Deployment and Production
- Model Deployment: Converting models to deployment formats (TensorFlow Serving, TensorFlow Lite)
- Model Interpretability: Techniques for understanding model predictions
- Scaling Deep Learning Models: Distributed training, GPU acceleration
Ethics and Responsible AI in Deep Learning
- Bias and Fairness: Addressing biases in data and models
- Explainable AI: Interpreting black-box models
- AI Ethics Guidelines and Regulations
Deep Learning Projects and Case Studies
- Real-world Deep Learning Projects: Implementation and evaluation
- Case Studies: Industry-specific applications (e.g., healthcare, finance)
- Presentation and Documentation of Deep Learning Projects
Training
Basic Level Training
Duration : 1 Month
Advanced Level Training
Duration : 1 Month
Project Level Training
Duration : 1 Month
Total Training Period
Duration : 3 Months
Course Mode :
Available Online / Offline
Course Fees :
Please contact the office for details