The Deep Learning and Neural Networks is designed to provide a comprehensive understanding of artificial intelligence, focusing on building, training, and deploying deep learning models. Participants will gain hands-on experience with neural network architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs). The course covers essential concepts such as supervised and unsupervised learning, backpropagation, activation functions, optimization techniques, and performance evaluation. By the end of the program, learners will be equipped to develop AI-powered applications for computer vision, natural language processing, and other cutting-edge domains.
Module 1: Introduction to Deep Learning & Neural NetworksBasics of AI, Machine Learning, and Deep Learning Overview of Neural Networks and their applications Activation functions, loss functions, and optimization
Module 2: Python for Deep Learning Python programming essentials for AI NumPy, Pandas, Matplotlib for data handling and visualization Jupyter Notebook and Google Colab setup.
Module 3: Fundamentals of Neural Networks Perceptron, Multi-Layer Perceptron (MLP) Forward and backward propagation Gradient descent and optimization techniques.
Module 4: Convolutional Neural Networks (CNNs) Architecture and working of CNNs Image classification and recognition Hands-on implementation with TensorFlow/PyTorch.
Module 5: Recurrent Neural Networks (RNNs) & LSTMs Sequence modeling and time series prediction Working with RNNs and LSTMs Applications in NLP and forecasting.
Module 6: Advanced Deep Learning Architectures Generative Adversarial Networks (GANs) Autoencoders and transfer learning Practical implementation of advanced models.
Module 7: Natural Language Processing (NLP) Text preprocessing and tokenization Sentiment analysis and text classification Hugging Face Transformers and pre-trained models.
Module 8: Model Training, Evaluation & Optimization Hyperparameter tuning, regularization, and dropout Model evaluation metrics: accuracy, precision, recall, F1-score Techniques for improving model performance.
Module 9: Deployment of Deep Learning Models Saving and loading models Deploying models on web and cloud platforms Integration with real-world applications.
Module 10: Capstone Project & Case Studies Real-world projects in computer vision, NLP, or AI applicationsHands-on problem-solving with end-to-end implementation Industry case studies and best practices.
Mobile: 9100348679
Email: coursedivine@gmail.com
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