Softlogic Systems Neural Networks And Deep Learning Course Syllabus is specifically designed for College Students, Freshers, and Job Seekers. Our Neural Networks And Deep Learning syllabus covers perceptrons, multi-layer neural networks, backpropagation, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and real-world AI project implementation. Our Neural Networks And Deep Learning Course Content helps you learn Neural Networks And Deep Learning step by Step with real-time projects and Interview Preparations.
Neural Networks And Deep Learning Syllabus
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Syllabus for The Neural Networks And Deep Learning Syllabus Course
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Module 1: Introduction to Artificial Intelligence and Deep Learning
- What is Artificial Intelligence, Machine Learning, and Deep Learning?
- History and evolution of neural networks
- Difference between traditional ML and deep learning
- Real-world applications of deep learning in vision, NLP, healthcare, and more
- Deep learning workflow and lifecycle
- Introduction to tools: Anaconda, Jupyter, Google Colab
Module 2: Python Programming for Deep Learning
- Python basics: variables, data types, functions, loops
- Working with Numpy, Pandas, and Matplotlib for data manipulation and visualization
- Introduction to Scikit-learn and its ML utilities
- Setting up TensorFlow and Keras for deep learning development
Module 3: Mathematics for Neural Networks
- Linear algebra basics: matrices, vectors, and operations
- Calculus: derivatives, gradients, and optimization
- Probability and statistics for model evaluation
- Understanding how math applies to deep learning algorithms
Module 4: Fundamentals of Neural Networks
- Biological inspiration and perceptron model
- Activation functions: Sigmoid, Tanh, ReLU, Leaky ReLU, Softmax
- Loss and cost functions (MSE, Cross-Entropy)
- Forward propagation algorithm
- Backward propagation and chain rule
- Optimization: Gradient Descent, Mini-Batch, Adam, RMSProp
- Epochs, learning rate, and hyperparameter tuning
Module 5: Deep Neural Networks (DNNs)
- Multi-layer perceptrons (MLPs)
- Network depth and width considerations
- Initializations: Xavier, He, Random
- Regularization techniques: Dropout, L1/L2
- Batch Normalization and Early Stopping
- Vanishing and Exploding Gradient problems
Module 6: Convolutional Neural Networks (CNNs)
- Overview of image data and CNN use cases
- Convolution operation and kernel filters
- Padding, Stride, Pooling layers (Max, Average)
- CNN architectures: LeNet, AlexNet, VGG, ResNet
- Feature extraction and visualization
- Transfer learning and fine-tuning with pretrained models (VGG, Inception, ResNet)
- Image classification project using CNN
Module 7: Recurrent Neural Networks (RNNs) and LSTMs
- Introduction to sequential data and RNNs
- Structure and working of vanilla RNNs
- Problems with RNNs: vanishing gradient and short memory
- Long Short-Term Memory (LSTM) networks
- Gated Recurrent Units (GRUs)
- Applications in time-series prediction and NLP
- Text generation and sentiment analysis projects
Module 8: Advanced Deep Learning Architectures
- Autoencoders: architecture and reconstruction
- Denoising and Sparse Autoencoders
- Variational Autoencoders (VAEs) and latent space
- Generative Adversarial Networks (GANs)
- Architecture: Generator and Discriminator
- GAN training techniques
- Use cases: image generation, style transfer
- Attention Mechanism and Transformers (introductory level)
Module 9: Model Evaluation and Tuning
- Evaluation metrics: Accuracy, Precision, Recall, F1-score, AUC
- Confusion matrix and classification report
- ROC-AUC curve
- Hyperparameter tuning with Grid Search and Random Search
- Cross-validation techniques
- Underfitting vs Overfitting
Module 10: Deployment and Real-Time Projects
- Saving/loading models using Pickle, Joblib, and TensorFlow
- Deployment using Flask and Streamlit
- REST API creation for ML models
- Integration with frontend tools
- Real-time end-to-end project:
- Image classification
- Chatbot or NLP classifier
- Predictive analytics with time series
Module 11: Career Support and Certification
- Resume building for AI/ML roles
- GitHub portfolio development
- Soft skills training for interviews
- Mock technical and HR interviews
- Certification guidance and career roadmap
- 100% Placement Assistance
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