A deep dive in deep learning ocean with Pytorch & TensorFlow from Udemy

Course Contents

Deep Learning and revolutionized Artificial Intelligence and data science. Deep Learning teaches computers to process data in a way that is inspired by the human brain.

This is complete and comprehensive course on deep learning. This course covers the theory and intuition behind deep learning models and then implementing all the deep learning models both in Pytorch and Tensor flow.

Practical Oriented explanations Deep Learning Models with implementation both in Pytorch and Tensor Flow.

No need of any prerequisites. I will teach you everything from scratch.

Job Oriented Structure

Sections of the Course

· Introduction of the Course

· Introduction to Google Colab

· Python Crash Course

· Data Preprocessing

· Regression Analysis

· Logistic Regression

· Introduction to Neural Networks and Deep Learning

· Activation Functions

· Loss Functions

· Back Propagation

· Neural Networks for Regression Analysis

· Neural Networks for Classification

· Dropout Regularization and Batch Normalization

· Optimizers

· Adding Custom Loss Function and Custom Layers to Neural Networks

· Convolutional Neural Network (CNN)

· One Dimensional CNN

· Setting Early Stopping Criterion in CNN

· Recurrent Neural Network (RNN)

· Long Short-Term Memory (LSTM) Network

· Bidirectional LSTM

· Generative Adversarial Network (GAN)

· DCGANs

· Autoencoders

· LSTM Autoencoders

· Variational Autoencoders

· Neural Style Transfer

· Transformers

· Vision Transformer

· Time Series Transformers

. K-means Clustering

. Principle Component Analysis

. Deep Learning Models with implementation both in Pytorch and Tensor Flow.

What's inside

Learning objectives

Theory, maths and implementation of all the deep learning models
Pre-requisites for deep learning such as data preprocessing, regression analysis and logistic regression
Build artificial neural networks and use them for regression and classification problems
Using gpu with deep learning models.
Creating our own custom loss functions and custom layers for deep learning models
Convolutional neural network ( cnn )
One dimensional cnn
Setting early stopping criterion in deep learning models
Transfer learning
Recurrent neural networks ( rnn )
Time series prediction and classification

Autoencoders and variational autoencoder ( vae )
Cnn autoencoder and lstm autoencoder
Generative adversarial networks (gans)
Lstm and bidirectional lstm
Transformer
Vision transformer
Time series transformer
Neural style transfer
Implementation of deep learning models in pytorch and tensor flow
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Theory, maths and implementation of all the deep learning models
Pre-requisites for deep learning such as data preprocessing, regression analysis and logistic regression
Build artificial neural networks and use them for regression and classification problems
Using gpu with deep learning models.
Creating our own custom loss functions and custom layers for deep learning models
Convolutional neural network ( cnn )
One dimensional cnn
Setting early stopping criterion in deep learning models
Transfer learning
Recurrent neural networks ( rnn )
Time series prediction and classification
Autoencoders and variational autoencoder ( vae )
Cnn autoencoder and lstm autoencoder
Generative adversarial networks (gans)
Lstm and bidirectional lstm
Transformer
Vision transformer
Time series transformer
Neural style transfer
Implementation of deep learning models in pytorch and tensor flow
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Show less

Syllabus

Introduction about the course and get the course material

Introduction of the course

Course Material

How to succeed in this course

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Covers both PyTorch and TensorFlow, which are two of the most popular deep learning frameworks used in both industry and research, giving learners flexibility

Includes a Python crash course and covers data preprocessing, regression analysis, and logistic regression, which are essential skills for machine learning

Explores advanced topics such as GANs, autoencoders, transformers, and neural style transfer, which are cutting-edge techniques in deep learning research and applications

Teaches how to create custom loss functions and custom layers, which allows learners to extend the functionality of existing deep learning models and tailor them to specific tasks

Includes time series prediction and classification, LSTM autoencoders, and time series transformers, which are useful for analyzing sequential data

Shows how to use GPUs with deep learning models and Google Colab, which provides free access to cloud-based computing resources

Reviews summary

Comprehensive deep learning with pytorch & tensorflow

According to learners, this course offers a comprehensive deep dive into the world of deep learning, covering a wide array of models and concepts. A key strength highlighted by students is the approach of implementing models in both PyTorch and TensorFlow, providing valuable dual-framework experience. Many found the explanations of theory and underlying math to be helpful. The hands-on coding and projects are frequently cited as a highlight, offering practical application. However, some learners note that despite the 'no prerequisites' claim, a background in Python, math, and machine learning fundamentals is strongly recommended, finding the pace potentially too fast for true beginners. There are also occasional comments about code examples needing updates due to library changes.

Explains the theory and math clearly.

"The explanations of the underlying theory and math are really solid and helped my understanding."

"I felt the theoretical concepts were explained well before diving into the code."

"Understanding the 'why' behind the models through the theory lectures was very beneficial."

"Instructor does a good job breaking down the math behind concepts like backpropagation."

Teaches using both PyTorch and TensorFlow.

"Implementing the models in both PyTorch and TensorFlow was incredibly useful for my career goals."

"I loved seeing how to do the same thing in two major frameworks. It broadened my skill set."

"The side-by-side comparison and implementation in both libraries is the unique value proposition here."

"Getting hands-on experience with PyTorch and TensorFlow in one course is a major plus."

Includes valuable hands-on coding exercises.

"The hands-on coding and projects are definitely the strongest part of the course for me."

"Working through the practical examples solidified my learning immensely."

"I enjoyed building real deep learning models from scratch using the provided project files."

"The projects felt relevant and helped me apply what I learned immediately."

Covers a wide range of deep learning topics.

"This course provides a really comprehensive overview of deep learning models from ANN to Transformers."

"I appreciated the breadth of topics covered; it felt like a complete curriculum."

"The sheer amount of information and different models taught is impressive and valuable."

"Learned about so many different architectures - CNNs, RNNs, GANs, Autoencoders, Transformers... it's all here."

Some examples may need minor updates.

"Ran into a few issues with outdated library versions in the code examples."

"Had to do some debugging and adjust code due to package changes since the lectures were recorded."

"Some code snippets had minor typos or required slight modification to run."

"It would be great if the code repos were regularly updated to current library versions."

Requires some existing programming/math skills.

"Despite the claim, I felt you really needed a strong Python and math background to keep up."

"The pace was too fast for me as a complete beginner. Some prerequisites are definitely helpful."

"I struggled in the early sections because I wasn't already comfortable with numpy and pandas."

"If you're not already familiar with basic machine learning concepts, the start might be tough."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in A deep dive in deep learning ocean with Pytorch & TensorFlow with these activities:

Review Linear Algebra Fundamentals

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Solidify your understanding of linear algebra concepts, which are fundamental to many deep learning algorithms.

Browse courses on Linear Algebra

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Review matrix operations such as addition, multiplication, and transposition.
Study eigenvalues and eigenvectors and their role in dimensionality reduction.
Practice solving linear equations and understanding vector spaces.

Brush Up on Calculus Concepts

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Revisit calculus concepts, especially derivatives and gradients, which are essential for understanding backpropagation.

Browse courses on Calculus

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Review the concept of derivatives and their application in optimization.
Study the chain rule and its importance in calculating gradients.
Practice finding the minimum and maximum of functions using calculus.

Read 'Deep Learning' by Goodfellow, Bengio, and Courville

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Supplement your learning with a comprehensive textbook that covers the theoretical underpinnings of deep learning.

View Deep Learning on Amazon

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Read the chapters relevant to the current module in the course.
Take notes on key concepts and definitions.
Work through the examples and exercises provided in the book.

Four other activities

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Implement Neural Networks from Scratch

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Reinforce your understanding by implementing basic neural networks using only NumPy.

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Implement a feedforward neural network with one hidden layer.
Implement backpropagation to train the network.
Test the network on a simple classification problem.

Read 'Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow' by Aurélien Géron

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Use this book as a reference for practical implementation details and best practices in TensorFlow and Keras.

View Hands-On Machine Learning with Scikit-Learn,... on Amazon

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Read the chapters relevant to the current module in the course.
Run the code examples provided in the book.
Adapt the code examples to your own projects.

Create a Blog Post on a Deep Learning Topic

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Solidify your understanding by explaining a deep learning concept in your own words.

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Choose a specific deep learning topic covered in the course.
Research the topic thoroughly and gather relevant information.
Write a clear and concise blog post explaining the concept.
Include examples and visualizations to illustrate the concept.

Build an Image Classifier with CNNs

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Apply your knowledge of CNNs to build a practical image classification project.

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Choose a suitable image dataset (e.g., CIFAR-10, MNIST).
Design and implement a CNN architecture using PyTorch or TensorFlow.
Train the model on the dataset and evaluate its performance.
Experiment with different hyperparameters and architectures to improve accuracy.

Career center

Learners who complete A deep dive in deep learning ocean with Pytorch & TensorFlow will develop knowledge and skills that may be useful to these careers:

Deep Learning Engineer

A Deep Learning Engineer designs, develops, and deploys deep learning models for various applications. This course, with its comprehensive coverage of deep learning models and their implementation in both PyTorch and TensorFlow, is directly relevant to the responsibilities of a Deep Learning Engineer. Diving into topics like Convolutional Neural Networks, Recurrent Neural Networks, Generative Adversarial Networks, and Transformers will help you build a strong foundation. The course teaches the practical aspects of deep learning, giving you hands-on experience with model development and optimization. Furthermore, the course's exploration of custom loss functions and layers is especially pertinent to problem solving, which is crucial for any Deep Learning Engineer. This course gives you the tools to succeed in the world of deep learning.

See salaries and explore the career path for Deep Learning Engineer

Machine Learning Engineer

A Machine Learning Engineer builds and deploys machine learning models, often using deep learning techniques. This course, with its comprehensive coverage of deep learning models and implementation using PyTorch and TensorFlow, is an excellent starting point. The course covers essential topics such as data preprocessing, regression analysis, logistic regression, neural networks, convolutional neural networks, recurrent neural networks, and transformers, providing a broad and solid foundation for a career. This curriculum helps develop the practical skills needed to implement, train, and deploy machine learning models effectively. The course's hands-on approach, combined with the theoretical understanding of various deep learning architectures, is useful for anyone in the role of Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Artificial Intelligence Researcher

An Artificial Intelligence Researcher explores and develops new algorithms and models in the field of AI, often with a focus on deep learning. This course, providing a deep dive into deep learning with both PyTorch and TensorFlow, is highly relevant. The course's coverage of fundamental concepts, various neural network architectures (CNNs, RNNs, LSTMs, GANs, Transformers), and implementation details builds a strong foundation for research. The emphasis on both theory and practical implementation will allow you to not only understand the existing techniques, but also let you modify them and make new algorithms. The course's coverage of custom loss functions would be invaluable for the AI Researcher.

See salaries and explore the career path for Artificial Intelligence Researcher

Natural Language Processing Engineer

A Natural Language Processing Engineer develops models and systems that enable computers to understand and process human language. This course section covering data preprocessing, Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Bidirectional LSTMs, and Transformers is highly relevant for a Natural Language Processing Engineer. The focus on both PyTorch and TensorFlow will make you proficient with the tools used in industry. Studying data preprocessing in this course will be invaluable for the Natural Language Processing Engineer.

See salaries and explore the career path for Natural Language Processing Engineer

Computer Vision Engineer

A Computer Vision Engineer develops algorithms and systems that enable computers to "see" and interpret images. This course, particularly its deep dive into Convolutional Neural Networks (CNNs), Vision Transformers and Neural Style Transfer is directly applicable to the work of a Computer Vision Engineer. The course's coverage of data preprocessing techniques is also invaluable for preparing image data for model training. The focus on both PyTorch and TensorFlow ensures that you are proficient with industry-standard tools. The study of CNNs in this course would be invaluable for the Computer Vision Engineer.

See salaries and explore the career path for Computer Vision Engineer

Data Scientist

A Data Scientist analyzes data, builds models, and extracts insights to inform business decisions. This course, providing a comprehensive overview of deep learning and its applications, is a valuable asset for a Data Scientist. The curriculum covers data preprocessing, regression analysis, logistic regression, neural networks, convolutional neural networks, recurrent neural networks, generative adversarial networks, autoencoders, and transformers, providing a broad understanding of deep learning techniques. This understanding will prepare you to apply deep learning models to solve complex data science problems. Furthermore, the implementation of these things in both Tensorflow and Pytorch may be of use to the Data Scientist.

See salaries and explore the career path for Data Scientist

Robotics Engineer

A Robotics Engineer designs, builds, and programs robots for various applications. This course, and its coverage of deep learning techniques, can significantly enhance the capabilities of robots. The study of computer vision through Convolutional Neural Networks (CNNs) and Transformers enables robots to visually perceive their environment. The course's coverage of Recurrent Neural Networks (RNNs) and LSTMs enables robots to process sequential data such as sensor readings. By studying the contents of the course you can build robots that are more intelligent and capable. You might study this course to help build your foundation as a Robotics Engineer.

See salaries and explore the career path for Robotics Engineer

Quantitative Analyst

A Quantitative Analyst develops and implements mathematical and statistical models for financial analysis and risk management. This course, and its introduction to deep learning techniques, will allow you to expand your toolkit. The study of time series prediction and classification using Recurrent Neural Networks (RNNs) and Transformers helps the Quantitative Analyst in financial forecasting. By studying the contents of the course you can build models that are more intelligent and capable. The Quantitative Analyst may wish to study the contents of the course.

See salaries and explore the career path for Quantitative Analyst

Software Engineer

A Software Engineer designs, develops, and maintains software systems. This course, while focused on deep learning, may provide relevant skills for certain software engineering roles. The course covers fundamental programming concepts, data structures, and algorithms, which are essential for software development. The course's hands-on experience with PyTorch and TensorFlow can be valuable for Software Engineers working on AI-powered applications. The various techniques in this course may improve the Software Engineer's performance.

See salaries and explore the career path for Software Engineer

Data Analyst

A Data Analyst collects, processes, and analyzes data to identify trends and insights. This course, with its focus on deep learning, may provide additional skills for a Data Analyst to build predictive models and gain deeper insights from data. The course covers data preprocessing techniques, regression analysis, and other machine learning algorithms that can be valuable tools for a data analyst. Taking this course may allow the Data Analyst to better understand data.

See salaries and explore the career path for Data Analyst

Research Scientist

A Research Scientist conducts research to advance knowledge in a particular field. With its dive into deep learning techniques, this course can be valuable for a Research Scientist exploring applications of AI. The course covers various neural network architectures, optimization techniques, and model evaluation methods, providing a comprehensive understanding of the field. Use your understanding of the course to help you as Research Scientist.

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Business Intelligence Analyst

A Business Intelligence Analyst analyzes business data to identify trends and insights that can improve decision-making. The techniques taught in this course may be useful for a Business Intelligence Analyst. The material on data preprocessing, regression, and neural networks, are especially useful. The deep learning techniques in this course may improve the Business Intelligence Analyst's performance.

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Statistician

A Statistician collects, analyzes, and interprets data to draw conclusions and make predictions. While this course focuses on deep learning, certain statistical concepts may enhance the Statistician's performance. This course covers regression analysis, loss functions, and evaluation metrics. These concepts help expand their knowledge and improve their ability to analyze data effectively. The Statistician may find some of the techniques in this course to be useful.

See salaries and explore the career path for Statistician

Business Analyst

A Business Analyst identifies business needs and recommends solutions that deliver value to stakeholders. While this course primarily focuses on deep learning techniques, a Business Analyst can use the knowledge from this course to improve project outcomes. Specifically, this course provides a better understanding of underlying algorithm optimization that can improve overall performance. In turn, the project outcomes are more likely to be successful. A Business Analyst may find this course to be useful.

See salaries and explore the career path for Business Analyst

Market Research Analyst

A Market Research Analyst analyzes market trends and consumer behavior to provide insights for marketing and product development. While this course primarily focuses on deep learning implementations, a Market Research Analyst can use the knowledge from this course to improve project outcomes. Specifically, this course provides a better understanding of underlying algorithm optimization that can improve overall performance. In turn, the project outcomes are more likely to be successful. A Market Research Analyst may find this course to be useful.

See salaries and explore the career path for Market Research Analyst

A deep dive in deep learning ocean with Pytorch & TensorFlow

What's inside

Learning objectives

Syllabus

Traffic lights

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Reviews summary

Comprehensive deep learning with pytorch & tensorflow

Activities

Career center

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