Deep Learning with Tensorflow and Pytorch (Tutorial Ongoing)

Free Download Deep Learning with Tensorflow and Pytorch (Tutorial Ongoing)
Published 6/2023
Created by Jerin Antony
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 23 Lectures ( 2h 35m ) | Size: 1.24 GB

Clear and simple deep learning tutorial, you will love it. Will be adding new topics on a weekly basis.
What you’ll learn
Quick demo of a model using Gradio
Basics of Deep Learning
Train MNIST classifier using Tensorflow and Pytorch
Add a CICD for your deep learning repo
Data Generator inbuilt and custom
Train Age (multi-class) classifier using Tensorflow and Pytorch
Requirements
Python basics needed, else learn python also parallely.
Colab and GitHub account
Description
Welcome to the Deep Learning Fundamentals course on Udemy! Are you ready to unlock the power of neural networks and delve into the exciting world of artificial intelligence? Look no further! This comprehensive course is designed to equip you with the essential knowledge and practical skills needed to become proficient in both Tensorflow and Pytorch based deep learning together!Deep learning has revolutionized the field of AI, enabling machines to learn from vast amounts of data and make accurate predictions, recognize patterns, and perform complex tasks. In this course, we will demystify the concepts behind deep learning and guide you through hands-on exercises to build and train your neural networks.Here’s an overview of what you’ll learn:Introduction to Deep Learning:Understand the fundamentals of artificial neural networks.Explore the history and evolution of deep learning.Gain insights into real-world applications and their impact.Neural Networks and Architectures:Study the structure and functioning of artificial neurons.Learn about various neural network architectures, including feedforward, convolutional, and recurrent networks.Explore activation functions, weight initialization, and regularization techniques.Building Deep Learning Models:Implement deep learning models using popular frameworks such as TensorFlow or PyTorch.Understand the process of data preprocessing, including feature scaling and one-hot encoding.Design effective training and validation sets for model evaluation.Training Neural Networks:Grasp the concept of backpropagation and how it enables model training.Explore optimization algorithms like stochastic gradient descent (SGD) and Adam.Learn techniques to prevent overfitting, such as dropout and early stopping.Convolutional Neural Networks (CNNs):Dive into CNN architecture and its role in image and video analysis.Build CNN models for tasks like image classification, object detection, and image generation.Understand advanced techniques like transfer learning and data augmentation.Recurrent Neural Networks (RNNs):Discover the power of RNNs in sequential data analysis, such as text and speech.Create RNN models for tasks like language translation, sentiment analysis, and speech recognition.Explore advanced RNN variants like LSTMs and GRUs.Generative Adversarial Networks (GANs):Learn about GANs and their ability to generate realistic synthetic data.Build GAN models for tasks like image generation and style transfer.Explore cutting-edge research and applications in the field of GANs.Deployment and Real-World Applications:Discover strategies for deploying deep learning models in production environments.Explore real-world applications of deep learning, such as autonomous driving, healthcare, and natural language processing.By the end of this course, you will have a strong foundation in deep learning principles and practical skills to tackle a wide range of AI challenges. Join us on this exciting journey and become a proficient deep learning practitioner!Enroll now and unlock the limitless potential of deep learning!
Who this course is for
Developers who are interested in learning: deep learning, how to achieve production quality models.
Homepage

https://www.udemy.com/course/deep-learning-tutorial-with-tensorflow-and-pytorch/

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