Deep Learning with Neural Networks

About Course

This comprehensive course explores the fundamental
principles, advanced architectures, and practical
applications of deep learning and neural networks.
Students will gain both a profound theoretical
understanding and extensive hands-on experience in
designing, training, optimizing, and deploying
sophisticated neural networks. The curriculum covers a
wide array of AI applications, from state-of-the-art
image recognition and intricate natural language
processing to robust time series analysis, preparing
students to tackle complex real-world challenges

Grasp the theoretical underpinnings of neural networks, including backpropagation, loss functions, and optimization algorithms, alongside foundational deep learning concepts.
Implement and train diverse feedforward neural network architectures, such as Multi-Layer Perceptrons (MLPs), for classification and regression tasks using popular frameworks like TensorFlow and PyTorch.
Design, build, and apply advanced Convolutional Neural Networks (CNNs), including architectures like ResNet and Inception, for computer vision tasks
such as image classification, object detection, and semantic segmentation.
Develop and utilize Recurrent Neural Networks (RNNs), specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, as well as Transformer models, for processing sequential data in natural language processing and time series forecasting.
Efficiently leverage pre-trained models through transfer learning, fine-tuning, and domain adaptation techniques to accelerate development and improve
performance on new datasets.
Optimize neural network performance using advanced techniques like hyperparameter tuning, regularization (dropout, batch normalization), and
early stopping, while addressing common deep learning challenges such as overfitting, underfitting, and vanishing gradients.

Course Content

Neural Network Fundamentals
Explore the essential building blocks of neural networks: perceptrons, various activation functions (ReLU, Sigmoid, Tanh, Leaky ReLU), and fundamental network architectures. Understand the mechanics of forward and backward propagation in detail, along with core optimization algorithms such as Stochastic Gradient Descent (SGD), Adam, and RMSprop. Implement basic neural networks from scratch using NumPy to solidify a deep understanding of their internal workings.

Deep Neural Networks
Dive into the complexities of multi-layer networks, examining advanced initialization strategies (Xavier, He) and crucial issues like vanishing/exploding gradients. Master regularization techniques including Dropout, Batch Normalization, and L1/L2 regularization. Discover effective architectural patterns and key design considerations for building robust and scalable feedforward deep neural networks.

Advanced Topics: Autoencoders and Generative Models
Delve into the principles and applications of autoencoders for tasks such as dimensionality reduction, data denoising, and anomaly detection. Explore powerful generative models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) for creating realistic new data instances (e.g., images, text) and understanding complex latent spaces. Implement basic GANs for image generation.

Convolutional Neural Networks
Master the intricate architecture of CNNs, including various types of convolutional layers (1D, 2D, 3D), pooling operations (max pooling, average pooling), and effective use of fully connected layers. Apply CNNs extensively to computer vision tasks such as image classification (e.g., classifying dog breeds), object detection (e.g., identifying cars and pedestrians in videos), and semantic segmentation. Study and implement popular CNN architectures including AlexNet, VGG, ResNet, Inception, and EfficientNet.

Recurrent Neural Networks
Learn sequence modeling with RNNs, focusing on the sophisticated LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) architectures for handling sequential data. Explore the benefits of bidirectional RNNs and their specific applications in natural language processing (e.g., sentiment analysis, machine translation), time series prediction (e.g., stock prices, weather data), and speech recognition. Develop strategies for effectively handling long- range dependencies in sequences

Transformers and Attention
Gain a comprehensive understanding of attention mechanisms and self-attention, the core innovation behind the Transformer architecture. Cover position encoding, multi-head attention, and the encoder- decoder structure. Explore the vast applications of Transformers in Natural Language Processing (NLP) and beyond, including an in-depth overview and practical application of prominent transformer- based models like BERT (Bidirectional Encoder Representations from Transformers), GPT (Generative Pre-trained Transformer), and T5.

Advanced Deep Learning Techniques
Explore a variety of advanced topics crucial for real-world deep learning projects, such as transfer learning, fine- tuning pre-trained models, and sophisticated data augmentation strategies. Learn about systematic hyperparameter optimization (e.g., grid search, random search, Bayesian optimization), model compression techniques (pruning, distillation), and quantization for efficient deployment. Crucial considerations for deploying deep learning models in production environments, including latency, throughput, and edge deployment, will also be covered.

Capstone Project
Students will undertake a significant project to design, implement, and rigorously train a deep neural network from scratch or by fine-tuning a pre-trained model. This project will solve a complex, real-world problem within a chosen domain such as advanced computer vision (e.g., medical image diagnosis), natural language processing (e.g., building a custom chatbot), or time series forecasting (e.g., predicting energy consumption). The project requires thorough documentation of the chosen approach, detailed experimental design, analysis of results, and performance evaluation metrics in a comprehensive technical report and a final presentation demonstrating their solution.

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Access cutting-edge deep learning frameworks, specifically TensorFlow and PyTorch, along with practical experienceutilizing powerful GPU-accelerated computing resources via cloud platforms (e.g., Google Colab, AWS EC2). Students will also gain access to a comprehensive library of pre-trained models (e.g., from Hugging Face, TensorFlow Hub) and benchmark datasets such as ImageNet, COCO, GLUE, and custom industry-specific datasets for practical application. Visualization tools for neural network interpretation (e.g., TensorBoard, Grad-CAM) and access to a curated collection of academic papers detailing state-of-the-art techniques will also be provided to support advanced study.
This course builds directly upon the foundational knowledge established in "Course 4: Machine Learning Fundamentals," guiding students deeper into the powerful and transformative techniques that have revolutionized artificial intelligence. Graduates will be exceptionally well-equipped to implement, optimize, and deploy cutting-edge deep learning solutions for a wide range of complex, real-world applications across various industries

Deep Learning with Neural Networks

Deep Learning with Neural Networks

About Course

What Will You Learn?

Course Content

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Deep Learning with Neural Networks

Deep Learning with Neural Networks

About Course

What Will You Learn?

Course Content

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