Deep Learning for AI: Build, Train & Deploy Neural Networks

Deep Learning for AI: Build, Train & Deploy Neural Networks

Learn hands-on Deep Learning with Neural Networks, CNNs, RNNs, NLP & Model Deployment using TensorFlow, Keras & PyTorch.

What you'll learn

• Understand Deep Learning Fundamentals – Explain the core concepts of deep learning, including neural networks, activation functions, and backpropagation.
• Differentiate Between Neural Network Architectures – Recognize the differences between ANN, CNN, RNN, LSTM, and Transformers, and their real-world applications.
• Implement Neural Networks using Keras & TensorFlow – Build, train, and evaluate artificial neural networks using industry-standard frameworks.
• Optimize Model Performance – Apply techniques like loss functions, gradient descent, and regularization to improve deep learning models.
• Develop Image Classification Models using CNNs – Understand and implement convolutional layers, pooling, and transfer learning for computer vision tasks.
• Apply RNNs and LSTMs for Sequential Data – Build models for time-series forecasting, text generation, and sentiment analysis using RNNs and LSTMs.
• Utilize NLP Techniques in Deep Learning – Perform tokenization, word embeddings, and build NLP models with transformers like BERT.
• Train and Fine-Tune Transformer-Based Models – Implement transformer architectures for NLP tasks such as text classification and summarization.
• Deploy Deep Learning Models – Learn various deployment strategies, including TensorFlow Serving, Docker, and cloud-based deployment.
• Compare PyTorch and TensorFlow for Model Development – Understand the differences between PyTorch and TensorFlow and choose the right framework for use-cases.
• Apply Transfer Learning and Fine-Tuning – Use pre-trained models for improving model efficiency and accuracy with minimal training data.
• Perform Hyperparameter Tuning and Cross-Validation – Optimize models using advanced tuning techniques like Grid Search, Random Search, and Bayesian Optimization
• Explore Real-World Deep Learning Use Cases – Analyze case studies in healthcare, finance, IoT, and other industries.
• Scale Deep Learning Models for Large Datasets – Implement distributed training and parallel computing techniques for handling big data.
• Execute an End-to-End Deep Learning Project – Work on a final project covering data preprocessing, model training, evaluation, and deployment.

A warm welcome to the Deep Learning for AI: Build, Train & Deploy Neural Networks course by Uplatz.

Deep learning is a specialized branch of machine learning that focuses on using multi-layered artificial neural networks to automatically learn complex patterns and representations from data. 

Deep learning enables computers to learn and make intelligent decisions by automatically discovering the representations needed for tasks such as classification, prediction, and more—all by processing data through layers of artificial neurons.

Deep learning is a subfield of machine learning that focuses on using artificial neural networks with many layers (hence “deep”) to learn complex patterns directly from data. 

It has revolutionized how we approach problems in image recognition, natural language processing, speech recognition, and more. 

Below is an overview covering how deep learning works, its key features, the tools and technologies used, its benefits, and the career opportunities it presents.