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Gain a comprehensive understanding of PyTorch, covering fundamental to state-of-the-art models
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Tackle real-world issues enabling you to develop the skills required to excel in the field of deep learning
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Modify advanced algorithms, such as Transformers, to suit specific datasets effectively
PyTorch is a Python framework developed by Facebook to develop and deploy deep learning models. It is one of the most popular deep-learning frameworks nowadays.
You will begin with learning the deep learning concept. Dive deeper into tensor handling, acquiring the finesse to create and manipulate tensors while leveraging PyTorch’s automatic gradient calculation through Autograd. Then transition to modeling by constructing linear regression models from scratch. After that, you will dive deep into classification models, mastering both multilabel and multiclass. You will then see the theory behind object detection and acquire the prowess to build object detection models. Embrace the cutting edge with YOLO v7, YOLO v8, and faster RCNN, and unleash the potential of pre-trained models and transfer learning.
Delve into RNNs and look at recommender systems, unlocking matrix factorization techniques to provide personalized recommendations. Refine your skills in model debugging and deployment, where you will debug models using hooks, and navigate the strategies for both on-premise and cloud deployment. Finally, you will explore ChatGPT, ResNet, and Extreme Learning Machines.
By the end of this course, you will have learned the key concepts, models, and techniques, and have the confidence to craft and deploy robust deep-learning solutions.
This course is ideal for Python developers and data enthusiasts seeking to expand their skills. This will also benefit aspiring data scientists, machine learning engineers, AI enthusiasts, and anyone intrigued by the transformative potential of deep learning. Whether you are a beginner or possess some prior knowledge, this course offers a smooth progression that will empower you to develop, deploy, and innovate with deep learning models using PyTorch.
Basic Python knowledge is required to fully engage with the material.
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Grasp deep learning concepts and install tools/packages/IDE/libraries
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Master CNN theory, image classification, layer dimensions, and transformations
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Dive into audio classification using torchaudio and spectrograms
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Do object detection with the help of YOLO v7, YOLO v8, and Faster RCNN
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Learn word embeddings, sentiment analysis, and pre-trained NLP models
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Deploy models using Google Cloud and other strategies
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