Udemy's Finest Deep Learning Programs
Udemy's Finest Deep Learning Programs
Course 1
Deep Learning A-Z: Hands-On Artificial Neural Networks
You will have the chance to use both in this course and learn when Tensorflow is preferable and PyTorch is the best choice. In the tutorials, we compare the two and offer advice on which would be the better option depending on the situation.
There are many different Deep Learning tools, and in this course we'll make sure to show you the most significant and forward-thinking ones so that when you finish Deep Learning A-ZTM, your knowledge will be at the cutting edge of current technology.
If you are just starting out into Deep Learning,
then you will find this course extremely useful.
Deep Learning A-Z™ is structured around special coding blueprint approaches meaning that you won’t get bogged down in unnecessary programming or mathematical complexities and instead you will be applying Deep Learning techniques from very early on in the course.
You will build your knowledge from the ground up and you will see how with every tutorial you are getting more and more confident.
- Understand the intuition behind Artificial Neural Networks
- Apply Artificial Neural Networks in practice
- Understand the intuition behind Convolutional Neural Networks
- Apply Convolutional Neural Networks in practice
- Understand the intuition behind Boltzmann Machines
- Apply Boltzmann Machines in practice
Best for: Beginners and intermediate | Time Duration: 22.5hrs | Provider: Udemy
Total Articles: 38 | Total Downloadable resources: 5
Info: Visit this course and get amazing offers on this course
A deep understanding of deep learning (with Python intro)
This course's goal is to give students a thorough understanding of deep learning. You will acquire adaptable, essential, and long-lasting deep learning competence.
You will have a thorough understanding of the underlying principles of deep learning, enabling you to pick up on new subjects and fashions as they develop in the future.
This course is not for those looking for a brief introduction to deep learning and a few worked-out examples.
Instead, this course is intended for those who are serious about learning how and why deep learning operates.
When and how to choose metaparameters like optimizers, normalizations, and learning rates.
How to assess the effectiveness of deep neural network models, and how to adapt and modify existing models to address new challenges.
- Theory: Why are deep learning models built the way they are?
- Math: What are the formulas and mechanisms of deep learning?
- Implementation: How are deep learning models actually constructed in Python (using the PyTorch library)?
- Intuition: Why is this or that metaparameter the right choice? How to interpret the effects of regularization? etc.
- Python: If you’re completely new to Python, go through the 8+ hour coding tutorial appendix. If you’re already a knowledgeable coder, then you’ll still learn some new tricks and code optimizations.
- Google-colab: Colab is an amazing online tool for running Python code, simulations, and heavy computations using Google’s cloud services. No need to install anything on your computer.
Best for: Beginners and intermediate | Time Duration: 57.5hrs | Provider: Udemy
Total Articles: 3 | Total Downloadable resources: 1
Info: Visit this course and get amazing offers on this course
Course 3
Tensorflow 2.0: Deep Learning and Artificial Intelligence
This course has "in-depth" sections if you want to delve a little deeper into the theory.
but, it is also aimed for those who want to learn quickly (like what is a loss function, and what are the different types of gradient descent approaches).
In this course, the emphasis is on breadth rather than depth, with less theory and more emphasis on developing fun things.
This is not the course for you if you're searching for one with more theory.
I already have classes that are solely devoted to each of these topics (recommender systems, natural language processing, reinforcement learning, computer vision, GANs, etc.).
- Deploying a model with Tensorflow Serving (Tensorflow in the cloud)
- Deploying a model with Tensorflow Lite (mobile and embedded applications)
- Distributed Tensorflow training with Distribution Strategies
- Writing your own custom Tensorflow model
- Converting Tensorflow 1.x code to Tensorflow 2.0
- Constants, Variables, and Tensors
- Eager execution
- Gradient tape
- Use Tensorflow Serving to serve your model using a RESTful API
- Use Tensorflow’s Distribution Strategies to parallelize learning
- Natural Language Processing (NLP) with Deep Learning
- Artificial Neural Networks (ANNs) / Deep Neural Networks (DNNs)
Best for: Beginners and intermediate | Time Duration: 22hrs | Provider: Udemy
Info: Visit this course and get amazing offers on this course
Course 4
The Data Science Course 2022: Complete Data Science Bootcamp
The training costs a quarter of what conventional programs do and teaches you everything you need to know to become a data scientist.
(not to mention the amount of time you will save).
If you want to learn more about data science or become a data scientist, you should enroll in this course.
The training is excellent for novices as well because it starts with the basics and gradually develops your skills.
Predictive modeling is the foundation of data science, and this section on "advanced statistics" will help you become an expert in these techniques.
In addition to providing you with the tools you need, this course also teaches you how to use them.
You learn how to think like a scientist through statistics.
Python is an essential programming language when it comes to creating, implementing, and deploying machine learning models using potent frameworks like scikit-learn, TensorFlow, etc.
- The course provides the entire toolbox you need to become a data scientist
- Fill up your resume with in demand data science skills:
- Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow.
- Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
- Start coding in Python and learn how to use it for statistical analysis
- Perform linear and logistic regressions in Python
- Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
- Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
Best for: Beginners and intermediate | Time Duration: 31hrs | Provider: Udemy
Total Articles: 92 | Total Downloadable resources: 542
Info: Visit this course and get amazing offers on this course
Course 5
PyTorch for Deep Learning with Python Bootcamp
This course focuses on striking a balance between key theoretical ideas and useful hands-on exercises and projects.
That allow you to learn how to apply the course's ideas to your own data sets.
When you sign up for this course, you will have access to meticulously organized notebooks that clearly explain ideas and include both code and explanations side by side.
You will also have access to our slides, which use simple graphics to illustrate theory.
By the end of this course you will be able to create a wide variety of deep learning models to solve your own problems with your own data sets.
In this course we will teach you everything you need to know to get started with Deep Learning with Pytorch, including:
- Learn how to use NumPy to format data into arrays
- Use pandas for data manipulation and cleaning
- Learn classic machine learning theory principals
- Use PyTorch Deep Learning Library for image classification
- Use PyTorch with Recurrent Neural Networks for Sequence Time Series Data
- Create state of the art Deep Learning models to work with tabular data
Best for: Intermediate and Python Developers | Time Duration: 17hrs | Provider: Udemy
Total Articles: 2 | Total Downloadable resources: 2
Info: Visit this Course and get amazing offers on this course
Course 6
Python for Computer Vision with OpenCV and Deep Learning
This course is your best resource for learning how to use the Python programming language for Computer Vision.
Learning about numerical processing with the NumPy library and how to open and work with images with NumPy are good places to start the course.
The OpenCV library will then be used to open and manipulate basic image data after that.
Then, as we begin to process photos, we'll learn how to use a number of effects, such as color mapping, blending, thresholds, gradients, and more.
After that, we'll move on to learning the fundamentals of video using OpenCV, including how to work with webcam streaming video.
Following that, we'll study direct video subjects like optical flow and object detection. including object tracking and face detection.
After that, we'll continue with a segment of the course devoted to the most recent developments in deep learning, such as picture recognition and customized image classifications.
Even the most recent deep learning networks, such as the YOLO (you only look once) deep learning network, will be covered.
- Perform image manipulation with OpenCV, including smoothing, blurring, thresholding, and morphological operations.
- Use Python and OpenCV to draw shapes on images and videos
- Open and Stream video with Python and OpenCV
- Detect Objects, including corner, edge, and grid detection techniques with OpenCV and Python
- Work with Tensorflow, Keras, and Python to train on your own custom images.
- Use Python and Deep Learning to build image classifiers
Best for: Intermediate and Python Developers | Time Duration: 14hrs | Provider: Udemy
Total Articles: 4 | Total Downloadable resources: 3
Info: Visit this Course and get amazing offers on this course
Course 7
Deep Learning: Convolutional Neural Networks in Python
The Convolutional Neural Network (CNN) has been used to obtain state-of-the-art results in computer vision tasks such as object detection, image segmentation, and generating photo-realistic images of people and things that don’t exist in the real world!
This course will teach you the fundamentals of convolution and why it’s useful for deep learning and even NLP (natural language processing).
This course focuses on “how to build and understand“, not just “how to use”. Anyone can learn to use an API in 15 minutes after reading some documentation. It’s not about “remembering facts”, it’s about “seeing for yourself” via experimentation. It will teach you how to visualize what’s happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.
You will learn about modern techniques such as data augmentation and batch normalization, and build modern architectures such as VGG yourself.
- The basics of machine learning and neurons (just a review to get you warmed up!)
- Neural networks for classification and regression (just a review to get you warmed up!)
- How to model image data in code
- How to model text data for NLP (including preprocessing steps for text)
- How to build an CNN using Tensorflow 2
- How to use batch normalization and dropout regularization in Tensorflow 2
- How to do image classification in Tensorflow 2
- How to do data preprocessing for your own custom image dataset
- How to use Embeddings in Tensorflow 2 for NLP
- How to build a Text Classification CNN for NLP (examples: spam detection, sentiment analysis, parts-of-speech tagging, named entity recognition)
Best for: Intermediate and Developers | Time Duration: 12hrs | Provider: Udemy
Total Articles: 4 | Total Downloadable resources: 3
Info: Visit this course and get amazing offers on this course
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