LSET’s Deep Learning with TensorFlow & PyTorch Course offers an in-depth exploration of modern deep learning principles and real-world applications. The course is structured to provide both theoretical grounding and practical implementation skills, preparing you to build and deploy intelligent systems. You will learn how to design, train, and optimise deep neural networks using industry-leading frameworks, equipping you with the capabilities to tackle complex AI problems in computer vision and natural language processing.
Technologies Covered in This Course
TensorFlow is a widely-used open-source framework developed by Google for building and training deep learning models. It supports scalable machine learning workflows and is used extensively in production-level AI systems.
PyTorch is a flexible and user-friendly deep learning library developed by Meta AI. Known for its dynamic computation graphs, PyTorch is widely adopted in research and rapid prototyping environments.
Keras is a high-level neural networks API integrated with TensorFlow, offering a simple and intuitive interface to quickly build and test deep learning models.
NumPy is a core Python library for numerical computing. It supports multi-dimensional arrays and is essential for data preprocessing, matrix operations, and tensor manipulation in deep learning.
Pandas provides powerful tools for data analysis and manipulation. It is used to clean, structure, and analyse datasets before feeding them into deep learning models.
Matplotlib & Seaborn are Python libraries for data visualisation. These tools help in plotting model performance, visualising datasets, and interpreting results during model training and evaluation.
OpenCV is an open-source computer vision library that allows for real-time image processing. It is integrated into deep learning workflows for tasks like object detection, image enhancement, and facial recognition.
Transformers (Hugging Face) are state-of-the-art architectures in natural language processing. This library allows developers to fine-tune pre-trained models like BERT, GPT, and T5 for text classification, summarisation, and more.
Scikit-learn is a versatile library for traditional machine learning tasks such as data splitting, evaluation metrics, and preprocessing pipelines used alongside deep learning workflows.
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