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.
publication date
2023-09-28
what you will learn
Grasp deep learning concepts and install tools/packages/IDE/libraries
Master CNN theory, image classification, layer dimensions, and transformations
Dive into audio classification using torchaudio and spectrograms
Do object detection with the help of YOLO v7, YOLO v8, and Faster RCNN
Learn word embeddings, sentiment analysis, and pre-trained NLP models
Deploy models using Google Cloud and other strategies
key features
Gain a comprehensive understanding of PyTorch, covering fundamental to state-of-the-art models * Tackle real-world issues enabling you to develop the skills required to excel in the field of deep learning * Modify advanced algorithms, such as Transformers, to suit specific datasets effectively
approach
This course is highly practical, comprehensive, and strategically designed to ensure that you not only grasp the fundamental concepts but also master their practical application. Through a problem-solving approach, you will be challenged to tackle real-world issues independently before delving into the author’s solutions. This hands-on experience helps in guiding you through the intricate layers of this dynamic field.
audience
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.
short description
Gain the essential skills to develop and deploy powerful deep-learning models tailored to your data. From mastering core techniques such as regression, classification, CNNs, and RNNs, to delving into advanced realms such as GANs, NLP, and Recommender Systems, this course covers everything. Discover cutting-edge architectures such as Transformers, YOLOv7, and ChatGPT that are reshaping the AI landscape.
subtitle
Become a Proficient Practitioner in Deep Learning Using the Leading Framework: PyTorch
keywords
Deep Learning, PyTorch, Python, Machine Learning, Artificial Intelligence, Neural Networks, Data Science, AI Programming, Model Deployment, NLP, Computer Vision, CNNs, RNNs, GANs, Transformers, Autoencoders, Recommender Systems, Object Detection, Style Transfer, Transfer Learning, Sentiment Analysis, NumPy, Seaborn, Matplotlib, Pandas, YOLOv7, YOLOv8, Google Cloud, Postman
Product ISBN
9781801070089
