Pytorch Deep Learning

Pytorch Deep Learning, available at $39.99, has an average rating of 4.4, with 40 lectures, based on 13 reviews, and has 99 subscribers.

You will learn about Python Pytorch BERT Deep Learning Image Processing Natural Language Processing Neural Network Gradient Descent transformers huggingface T5 This course is ideal for individuals who are Beginner python developers who are curious about deep learning or Beginner python developers who are curious about pytorch or Beginner python developers who are curious about Natural Language Processing or Beginner python developers who are curious about huggingface or Python developers who are curious about implementing BERT transfer learning model or Python developers who are curious about implementing T5 generative summarization learning model It is particularly useful for Beginner python developers who are curious about deep learning or Beginner python developers who are curious about pytorch or Beginner python developers who are curious about Natural Language Processing or Beginner python developers who are curious about huggingface or Python developers who are curious about implementing BERT transfer learning model or Python developers who are curious about implementing T5 generative summarization learning model.

Enroll now: Pytorch Deep Learning

Summary

Title: Pytorch Deep Learning

Price: $39.99

Average Rating: 4.4

Number of Lectures: 40

Number of Published Lectures: 39

Number of Curriculum Items: 40

Number of Published Curriculum Objects: 39

Original Price: $74.99

Quality Status: approved

Status: Live

What You Will Learn

Python
Pytorch
BERT
Deep Learning
Image Processing
Natural Language Processing
Neural Network
Gradient Descent
transformers
huggingface
T5

Who Should Attend

Beginner python developers who are curious about deep learning
Beginner python developers who are curious about pytorch
Beginner python developers who are curious about Natural Language Processing
Beginner python developers who are curious about huggingface
Python developers who are curious about implementing BERT transfer learning model
Python developers who are curious about implementing T5 generative summarization learning model

Target Audiences

Beginner python developers who are curious about deep learning
Beginner python developers who are curious about pytorch
Beginner python developers who are curious about Natural Language Processing
Beginner python developers who are curious about huggingface
Python developers who are curious about implementing BERT transfer learning model
Python developers who are curious about implementing T5 generative summarization learning model

Pytorch&Hugginface Deep Learning Course(Colab Hands-On)

Welcome to Pytorch Deep Learning From Zero To Hero Series.

If you have already mastered the basic syntax of python and don’t know what to do next, this course will be a rocket booster to skyrocket your programming skill to a business applicable level.

In this course, you will be able to master implementing deep neural network from the very beginning(simple perceptron) to BERT transfer learning/Google’s T5 by using pytorch and huggingface yourself by colab. Each Section will have one assignment for you to think and code yourself.

The Agenda is below.

Agenda:

Introduction
Google Colaboratory
Neuron
Perceptron
Make Your Perceptron Trainable
Normalize Data
Activation Function
Loss Function
Gradient Descent
Elegant Pytorch Gradient Descent
Final Project
Final Project Explained
Multi Layer Perceptron(MLP)
One Hot Encoding
Prepare data for MLP
Define MLP
Train & Evaluate MLP
Final Project for MLP
FCNN Explained
FCNN LOVE Letters Classification using MLP
Final Project For FCNN
CNN Explained
CNN Prepare data(Fashion MNIST)
CNN Define Model
CNN Train&Evaluate Model
CNNInference
Final Project For CNN
RNN Explained
RNN Prepare data
RNN Define Model
RNN Train Model
RNN Inference
BERT Sesame Street
BERT Prepare Data IMDB
BERT Model definition
BERT Model Training
BERT Model Evaluation
BERT Model Prediction
BERT Final Project
T5 Prepare Data
T5 Model definition
T5 Model Training
T5 Model Evaluation
T5 Model Prediction
T5 Final Project

Let’s start our journey together.

Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren’t special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one– and preferably only one –obvious way to do it.
Although that way may not be obvious at first unless you’re Dutch.
Now is better than never.
Although never is often better than *right* now.
If the implementation is hard to explain, it’s a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea — let’s do more of those!

Course Curriculum

Chapter 1: Introduction to Deep Learning (Neural Network) by Pytorch

Lecture 1: A01_Introduction

Lecture 2: A02 Google Colaboratory

Lecture 3: A03 Neuron

Lecture 4: A04 Perceptron

Lecture 5: A05 Make Your Perceptron Trainable

Lecture 6: A06 Normalize Data

Lecture 7: A07 Activation Function

Lecture 8: A08 Loss Function

Lecture 9: A09 Gradient Descent

Lecture 10: A10 Elegant Implementation of Gradient Descent by pytorch

Lecture 11: A11 Final Project For A

Lecture 12: A12 Final Project for A Explained

Chapter 2: From Simple Perceptron to Multi Layer Perceptron(MLP) by pytorch

Lecture 1: B01 Multi Layer Perceptron(MLP)

Lecture 2: B02 Prepare Dataset

Lecture 3: B03 Define MLP Model

Lecture 4: B04 Multi Layer Perceptron Training&Evaluation

Lecture 5: B05 Final Project Assignment & Explanation

Chapter 3: Fully Connected Neural Network Explained

Lecture 1: C01 Fully Connected Neural Network(FCNN) explained

Lecture 2: C02 FCNN LOVE Letters Image Recognition

Lecture 3: C03 Final Project Assignment and Explanation

Chapter 4: Convolutional Neural Network (CNN)

Lecture 1: D01 CNN explained

Lecture 2: D02 Prepare Dataset

Lecture 3: D03 CNN Define Model

Lecture 4: D04 CNN Train and Evaluate Model

Lecture 5: D05 CNN Make a prediction

Lecture 6: D06 Final Project

Chapter 5: Recurrent Neural Network(RNN)

Lecture 1: Recurrent Neural Network(RNN) explained

Lecture 2: E02 RNN prepare data

Lecture 3: E03 RNN define model

Lecture 4: E04 RNN Train and Evaluate Model

Lecture 5: E05 RNN Inference

Chapter 6: BERT

Lecture 1: Sesame Street

Lecture 2: F02 BERT IMDB

Lecture 3: BERT Model Definition

Lecture 4: F04 BERT Model Training

Lecture 5: F05 BERT Model Evaluation

Lecture 6: F06 BERT Model Prediction

Lecture 7: F07 BERT Final Project

Chapter 7: Google's T5

Lecture 1: Google's T5 Abstractive Summarization

Instructors

経知神草
chatgpt/whisper/python/numpy/pandas/matplotlib/pytorch/AI/DL

Rating Distribution

1 stars: 0 votes
2 stars: 1 votes
3 stars: 2 votes
4 stars: 5 votes
5 stars: 5 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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