Deep Learning with TensorFlow & Keras is an in-depth course designed for individuals aiming to master neural network architectures and deep learning workflows using two of the most powerful open-source frameworks—TensorFlow and Keras. The course begins with the basics of artificial neural networks (ANNs), explaining how neurons, activation functions, and weight updates through backpropagation work. Learners understand the computational graph model of TensorFlow and the simplicity of Keras as a high-level API built on top of it. The course then dives into various deep learning architectures, including feedforward neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and transformers. Each architecture is introduced with theoretical insights followed by hands-on coding in Python. Learners build and train models for tasks such as image classification (using CIFAR-10, MNIST), time series forecasting, text generation, and sentiment analysis. The course also covers model optimization techniques such as dropout, batch normalization, and learning rate scheduling. TensorBoard is introduced for visualization, and students learn to debug and interpret models using explainability tools like SHAP and LIME. Learners also explore transfer learning using pre-trained models such as VGG16, ResNet, and BERT, and deploy trained models using TensorFlow Serving and TFLite. Performance tuning on GPUs and TPUs is discussed to help scale training workflows. By the end of the course, students will be proficient in designing, training, evaluating, and deploying deep learning models for production use. This course is ideal for aspiring AI engineers, data scientists, and developers seeking to work on advanced machine learning applications in fields such as computer vision, NLP, and speech recognition.