Level: All | Genre: eLearning | Language: English | Duration: 49 Lectures ( 44h 46m ) | Size: 18.1 GB
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.
