Deep Learning Bootcamp: Neural Networks with Python, PyTorch

Master Neural Networks, DNNs, and CNNs with Python, PyTorch, and TensorFlow in this all-in-one Deep Learning Bootcamp.

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Are you ready to unlock the full potential of Deep Learning and AI by mastering not just one but multiple tools and frameworks? This comprehensive course will guide you through the essentials of Deep Learning using Python, PyTorch, and TensorFlow—the most powerful libraries and frameworks for building intelligent models.

Whether you're a beginner or an experienced developer, this course offers a step-by-step learning experience that combines theoretical concepts with practical hands-on coding. By the end of this journey, you'll have developed a deep understanding of neural networks, gained proficiency in applying Deep Neural Networks (DNNs) to solve real-world problems, and built expertise in cutting-edge deep learning applications like Convolutional Neural Networks (CNNs) and brain tumor detection from MRI images.

Why Choose This Course?

This course stands out by offering a comprehensive learning path that merges essential aspects from three leading frameworks: Python, PyTorch, and TensorFlow. With a strong emphasis on hands-on practice and real-world applications, you'll quickly advance from fundamental concepts to mastering deep learning techniques, culminating in the creation of sophisticated AI models.

Key Highlights:

Python: Learn Python from the basics, progressing to advanced-level programming essential for implementing deep learning algorithms.

PyTorch: Master PyTorch for neural networks, including tensor operations, optimization, autograd, and CNNs for image recognition tasks.

TensorFlow: Unlock TensorFlow's potential for creating robust deep learning models, utilizing tools like Tensorboard for model visualization.

Real-world Projects: Apply your knowledge to exciting projects like IRIS classification, brain tumor detection from MRI images, and more.

Data Preprocessing & ML Concepts: Learn crucial data preprocessing techniques and key machine learning principles such as Gradient Descent, Back Propagation, and Model Optimization.

Course Content Overview:

Module 1: Introduction to Deep Learning and Python

Introduction to the course structure, learning objectives, and key frameworks.

Overview of Python programming: from basics to advanced, ensuring you can confidently implement any deep learning concept.

Module 2: Deep Neural Networks (DNNs) with Python and NumPy

Programming with Python and NumPy: Understand arrays, data frames, and data preprocessing techniques.

Building DNNs from scratch using NumPy.

Implementing machine learning algorithms, including Gradient Descent, Logistic Regression, Feed Forward, and Back Propagation.

Module 3: Deep Learning with PyTorch

Learn about tensors and their importance in deep learning.

Perform operations on tensors and understand autograd for automatic differentiation.

Build basic and complex neural networks with PyTorch.

Implement CNNs for advanced image recognition tasks.

Final Project: Brain Tumor Detection using MRI Images.

Module 4: Mastering TensorFlow for Deep Learning

Dive into TensorFlow and understand its core features.

Build your first deep learning model using TensorFlow, starting with a simple neuron and progressing to Artificial Neural Networks (ANNs).

TensorFlow Playground: Experiment with various models and visualize performance.

Explore advanced deep learning projects, learning concepts like gradient descent, epochs, backpropagation, and model evaluation.

Who Should Take This Course?

Aspiring Data Scientists and Machine Learning Enthusiasts eager to develop deep expertise in neural networks.

Software Developers looking to expand their skillset with PyTorch and TensorFlow.

Business Analysts and AI Enthusiasts interested in applying deep learning to real-world problems.

Anyone passionate about learning how deep learning can drive innovation across industries, from healthcare to autonomous driving.

What You’ll Learn:

Programming with Python, NumPy, and Pandas for data manipulation and model development.

How to build and train Deep Neural Networks and Convolutional Neural Networks using PyTorch and TensorFlow.

Practical deep learning applications like brain tumor detection and IRIS classification.

Key machine learning concepts, including Gradient Descent, Model Optimization, and more.

How to preprocess and handle data efficiently using tools like DataLoader in PyTorch and Transforms for data augmentation.

Hands-on Experience:

By the end of this course, you will not only have learned the theory but will also have built multiple deep learning models, gaining hands-on experience in real-world projects.

Who this course is for:

• Anyone interested in Data Science.

• People who want to master DNNs with real datasets in Deep Learning.

• People who want to implement DNNs in realistic projects.

• Software developers and data scientists looking to expand their skillset with PyTorch.

• Beginners who want to enter the field of deep learning and artificial intelligence.

• Anyone Curious About Deep Learning and TensorFlow

Deep Learning: Advanced Computer Vision (GANs, SSD, +More!)

Deep Learning: Advanced Computer Vision (GANs, SSD, +More!) - VGG, ResNet, Inception, SSD, RetinaNet, Neural Style Transfer, GANs +More in Tensorflow, Keras, and Python

Sure! Here’s an overview of what you can expect to learn in the course Deep Learning: Advanced Computer Vision (GANs, SSD, +More!):

• Understand and apply transfer learning
• Understand and use state-of-the-art convolutional neural nets such as VGG, ResNet and Inception
• Understand object detection algorithms such as SSD and RetinaNet
• Understand semantic segmentation algorithms such as FCN and PSPNet
• Understand neural style transfer
• Understand and use generative adversarial networks (GANs)
• Understand and use deep reinforcement learning for vision-based tasks

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Deep Learning Mastery: Build your AI Foundation

Deep Learning Mastery: Build your AI Foundation - Build AI Models from Scratch | Create LLM like GPT| Master CNN, RNN, Transformers for NLP, Vision, Timeseries | Keras/TF

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Become part of AI revolution with "Deep Learning Mastery: Build Your AI Foundation" course. 

Today AI, LLM's, LMM's are everywhere, but how are these LLM's powered? what core technology is driving them?

Its Deep Learning and in this course you will gain the foundational knowledge and practical know-how to build powerful AI models from scratch with just few lines of code. Whether you're a beginner in AI or an aspiring data scientist, this course will guide you in mastering core Deep Learning concepts including Transformers Architecture.

Most importantly you will gain the ability to build your own Language models similar to GPT1 using the Transformer Architecture.

You’ll understand the core intuition behind Deep Learning Algorithms and master the techniques to build deep neural networks.

You will dive deep into different neural network architectures like CNN, RNNs LSTM, GRU, and most importantly TRANSFORMERS which are the foundation for all latest AI, Large Language Models (LLM), Large Multimodal Models (LMM).

With hands-on projects you will build your own models from scratch trained on public data.

You’ll gain real-world experience solving problems in computer vision, time-series forecasting and natural language processing (NLP) inlcuding Language Modeling.

By the end of the course, you'll not only understand how deep learning works but will also have the confidence to apply it to real-world problems, LLM's, leveraging tools like TensorFlow and Keras to build cutting-edge AI solutions.

A deep understanding of deep learning (with Python intro)

REDEEM

Master deep learning in PyTorch using an experimental scientific approach, with lots of examples and practice problems.

Description
Deep learning is increasingly dominating technology and has major implications for society.

From self-driving cars to medical diagnoses, from face recognition to deep fakes, and from language translation to music generation, deep learning is spreading like wildfire throughout all areas of modern technology.

But deep learning is not only about super-fancy, cutting-edge, highly sophisticated applications. Deep learning is increasingly becoming a standard tool in machine-learning, data science, and statistics. Deep learning is used by small startups for data mining and dimension reduction, by governments for detecting tax evasion, and by scientists for detecting patterns in their research data.

Deep learning is now used in most areas of technology, business, and entertainment. And it's becoming more important every year.



How does deep learning work?

Deep learning is built on a really simple principle: Take a super-simple algorithm (weighted sum and nonlinearity), and repeat it many many times until the result is an incredibly complex and sophisticated learned representation of the data.

Is it really that simple? mmm OK, it's actually a tiny bit more complicated than that ;) but that's the core idea, and everything else -- literally everything else in deep learning -- is just clever ways of putting together these fundamental building blocks. That doesn't mean the deep neural networks are trivial to understand: there are important architectural differences between feedforward networks, convolutional networks, and recurrent networks.

Given the diversity of deep learning model designs, parameters, and applications, you can only learn deep learning -- I mean, really learn deep learning, not just have superficial knowledge from a youtube video -- by having an experienced teacher guide you through the math, implementations, and reasoning. And of course, you need to have lots of hands-on examples and practice problems to work through. Deep learning is basically just applied math, and, as everyone knows, math is not a spectator sport!



What is this course all about?

Simply put: The purpose of this course is to provide a deep-dive into deep learning. You will gain flexible, fundamental, and lasting expertise on deep learning. You will have a deep understanding of the fundamental concepts in deep learning, so that you will be able to learn new topics and trends that emerge in the future.

Please note: This is not a course for someone who wants a quick overview of deep learning with a few solved examples. Instead, this course is designed for people who really want to understand how and why deep learning works; when and how to select metaparameters like optimizers, normalizations, and learning rates; how to evaluate the performance of deep neural network models; and how to modify and adapt existing models to solve new problems.



You can learn everything about deep learning in this course.

In this course, you will learn

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.



Unique aspects of this course

Clear and comprehensible explanations of concepts in deep learning, including transfer learning, generative modeling, convolutional neural networks, feedforward networks, generative adversarial networks (GAN), and more.

Several distinct explanations of the same ideas, which is a proven technique for learning.

Visualizations using graphs, numbers, and spaces that provide intuition of artificial neural networks.

LOTS of exercises, projects, code-challenges, suggestions for exploring the code. You learn best by doing it yourself!

Active Q&A forum where you can ask questions, get feedback, and contribute to the community.

8+ hour Python tutorial. That means you don't need to master Python before enrolling in this course.



So what are you waiting for??

Watch the course introductory video and free sample videos to learn more about the contents of this course and about my teaching style. If you are unsure if this course is right for you and want to learn more, feel free to contact with me questions before you sign up.

I hope to see you soon in the course!

Mike

Who this course is for:
Students in a deep learning course
Machine-learning enthusiasts
Anyone interested in mechanisms of AI (artificial intelligence)
Data scientists who want to expand their library of skills
Aspiring data scientists
Scientists and researchers interested in deep learning

Deep Learning From Scratch

Deep Learning From Scratch

Master the Essentials and Advanced Techniques in Deep Learning

REDEEM

Data Science: Modern Deep Learning in Python

Build with modern libraries like Tensorflow, Theano, Keras, PyTorch, CNTK, MXNet. Train faster with GPU on AWS.

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What you'll learn

• Apply momentum to backpropagation to train neural networks
• Apply adaptive learning rate procedures like AdaGrad, RMSprop, and Adam to backpropagation to train neural networks
• Understand the basic building blocks of TensorFlow
• Build a neural network in TensorFlow
• Write a neural network using Keras
• Write a neural network using PyTorch
• Understand the difference between full gradient descent, batch gradient descent, and stochastic gradient descent
• Understand and implement dropout regularization
• Understand and implement batch normalization
• Understand the basic building blocks of Theano
• Build a neural network in Theano
• Write a neural network using CNTK
• Write a neural network using MXNet

Description

This course continues where my first course, Deep Learning in Python, left off. You already know how to build an artificial neural network in Python, and you have a plug-and-play script that you can use for TensorFlow. Neural networks are one of the staples of machine learning, and they are always a top contender in Kaggle contests. If you want to improve your skills with neural networks and deep learning, this is the course for you.

You already learned about backpropagation, but there were a lot of unanswered questions. How can you modify it to improve training speed? In this course you will learn about batch and stochastic gradient descent, two commonly used techniques that allow you to train on just a small sample of the data at each iteration, greatly speeding up training time.

You will also learn about momentum, which can be helpful for carrying you through local minima and prevent you from having to be too conservative with your learning rate. You will also learn about adaptive learning rate techniques like AdaGrad, RMSprop, and Adam which can also help speed up your training.

Because you already know about the fundamentals of neural networks, we are going to talk about more modern techniques, like dropout regularization and batch normalization, which we will implement in both TensorFlow and Theano. The course is constantly being updated and more advanced regularization techniques are coming in the near future.

In my last course, I just wanted to give you a little sneak peak at TensorFlow. In this course we are going to start from the basics so you understand exactly what's going on - what are TensorFlow variables and expressions and how can you use these building blocks to create a neural network? We are also going to look at a library that's been around much longer and is very popular for deep learning - Theano. With this library we will also examine the basic building blocks - variables, expressions, and functions - so that you can build neural networks in Theano with confidence.

Theano was the predecessor to all modern deep learning libraries today. Today, we have almost TOO MANY options. Keras, PyTorch, CNTK (Microsoft), MXNet (Amazon / Apache), etc. In this course, we cover all of these! Pick and choose the one you love best.

Because one of the main advantages of TensorFlow and Theano is the ability to use the GPU to speed up training, I will show you how to set up a GPU-instance on AWS and compare the speed of CPU vs GPU for training a deep neural network.

With all this extra speed, we are going to look at a real dataset - the famous MNIST dataset (images of handwritten digits) and compare against various benchmarks. This is THE dataset researchers look at first when they want to ask the question, "does this thing work?"

These images are important part of deep learning history and are still used for testing today. Every deep learning expert should know them well.

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.

"If you can't implement it, you don't understand it"

Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".

My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?

After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...

Suggested Prerequisites:

Know about gradient descent

Probability and statistics

Python coding: if/else, loops, lists, dicts, sets

Numpy coding: matrix and vector operations, loading a CSV file

Know how to write a neural network with Numpy

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)

Who this course is for:

• Students and professionals who want to deepen their machine learning knowledge
• Data scientists who want to learn more about deep learning
• Data scientists who already know about backpropagation and gradient descent and want to improve it with stochastic batch training, momentum, and adaptive learning rate procedures like RMSprop
• Those who do not yet know about backpropagation or softmax should take my earlier course, deep learning in Python, first

Practical Deep Learning with PyTorch

Udemy - Practical Deep Learning with PyTorch, Accelerate your deep learning with PyTorch covering all the fundamentals of deep learning with a python-first framework.

• BESTSELLER
• Created by Deep Learning Wizard
•  English [Auto-generated]
• 6.5 hours on-demand video
• 3 downloadable resources
• Full lifetime access
• Access on mobile and TV
• Certificate of Completion

What you'll learn

• Effectively wield PyTorch, a Python-first framework, to build your deep learning projects
• Master deep learning concepts and implement them in PyTorch

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Deep Learning A-Z™: Hands-On Artificial Neural Networks

Deep Learning A-Z™: Hands-On Artificial Neural Networks, Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. Templates included.

• BESTSELLER
• Created by Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team
•  English
•  English [Auto-generated], Italian [Auto-generated], 4 more
• 22.5 hours on-demand video
• 30 articles
• Full lifetime access
• Access on mobile and TV
• Certificate of Completion

What you'll learn

• 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 Recurrent Neural Networks
• Apply Recurrent Neural Networks in practice
• Understand the intuition behind Self-Organizing Maps
• Apply Self-Organizing Maps in practice
• Understand the intuition behind Boltzmann Machines
• Apply Boltzmann Machines in practice
• Understand the intuition behind AutoEncoders
• Apply AutoEncoders in practice

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Deep Learning: Advanced Computer Vision

Online Courses Udemy

Deep Learning: Advanced Computer Vision, Advanced Computer Vision and Convolutional Neural Networks in Tensorflow, Keras, and Python

• HIGHEST RATED
• Created by Lazy Programmer Inc.
•  English
•  English [Auto-generated], Indonesian [Auto-generated], 6 more
• 7 hours on-demand video
• Full lifetime access
• Access on mobile and TV
• Certificate of Completion

What you'll learn

• Understand and apply transfer learning
• Understand and use state-of-the-art convolutional neural nets such as VGG, ResNet and Inception
• Understand and use object detection algorithms like SSD
• Understand and apply neural style transfer
• Understand state-of-the-art computer vision topics

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Zero to Deep Learning™ with Python and Keras

Zero to Deep Learning™ with Python and Keras, Understand and build Deep Learning models for images, text and more using Python and Keras

• BESTSELLER
• Created by Data Weekends, Jose Portilla, Francesco Mosconi
•  English
•  English [Auto-generated], Portuguese [Auto-generated], 1 more
• 10 hours on-demand video
• 6 articles
• Full lifetime access
• Access on mobile and TV
• Certificate of Completion

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What you'll learn

• To describe what Deep Learning is in a simple yet accurate way
• To explain how deep learning can be used to build predictive models
• To distinguish which practical applications can benefit from deep learning
• To install and use Python and Keras to build deep learning models
• To apply deep learning to solve supervised and unsupervised learning problems involving images, text, sound, time series and tabular data.
• To build, train and use fully connected, convolutional and recurrent neural networks
• To look at the internals of a deep learning model without intimidation and with the ability to tweak its parameters
• To train and run models in the cloud using a GPU
• To estimate training costs for large models
• To re-use pre-trained models to shortcut training time and cost (transfer learning)

Deep Learning, Neuronale Netze & AI: Der Komplettkurs

Deep Learning, Neuronale Netze & AI: Der Komplettkurs, Deep Learning & Artificial Intelligence in Python: Bitcoin-Preise voraussagen, AI entwickeln - in Keras & Tensorflow

• BESTSELLER
• Created by Jannis Seemann (CodingCourses.TV), Jannis Seemann
•  German
• 25.5 hours on-demand video
• 8 articles
• 3 downloadable resources
• Full lifetime access
• Access on mobile and TV
• Certificate of Completion

What you'll learn

• Verwende Keras und Tensorflow, um Neuronale Netze zu erstellen
• Entwickle eine Artificial Intelligence
• Entdecke Neuronale Netze und AI komplett am Beispiel
• Schreibe eine Bilderkennung, die Autos auf einem Bild markiert
• Entwickle ein Modell, um den Preis von Bitcoin und Aktien vorherzusagen
• Generiere Reden im Stil von Donald Trump

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Deep Learning with Python

Deep Learning with Python, Dive into the future of data science and implement intelligent systems using deep learning with Python

Created by Packt Publishing

Includes

• 2 hours on-demand video
• Full lifetime access
• Access on mobile and TV
• Certificate of Completion

Description

Deep learning is currently one of the best providers of solutions regarding problems in image recognition, speech recognition, object recognition, and natural language with its increasing number of libraries that are available in Python. The aim of deep learning is to develop deep neural networks by increasing and improving the number of training layers for each network, so that a machine learns more about the data until it’s as accurate as possible. Developers can avail the techniques provided by deep learning to accomplish complex machine learning tasks, and train AI networks to develop deep levels of perceptual recognition.

Deep learning is the next step to machine learning with a more advanced implementation. Currently, it’s not established as an industry standard, but is heading in that direction and brings a strong promise of being a game changer when dealing with raw unstructured data. Deep learning is currently one of the best providers of solutions regarding problems in image recognition, speech recognition, object recognition, and natural language processing. Developers can avail the benefits of building AI programs that, instead of using hand coded rules, learn from examples how to solve complicated tasks. With deep learning being used by many data scientists, deeper neural networks are evaluated for accurate results.

This course takes you from basic calculus knowledge to understanding backpropagation and its application for training in neural networks for deep learning and understand automatic differentiation. Through the course, we will cover thorough training in convolutional, recurrent neural networks and build up the theory that focuses on supervised learning and integrate into your product offerings such as search, image recognition, and object processing. Also, we will examine the performance of the sentimental analysis model and will conclude with the introduction of Tensorflow.

By the end of this course, you can start working with deep learning right away. This course will make you confident about its implementation in your current work as well as further research.

About The Author

Eder Santana is a PhD candidate on Electrical and Computer Engineering. His thesis topic is on Deep and Recurrent neural networks. After working for 3 years with Kernel Machines (SVMs, Information Theoretic Learning, and so on), Eder moved to the field of deep learning 2.5 years ago, when he started learning Theano, Caffe, and other machine learning frameworks. Now, Eder contributes to Keras: Deep Learning Library for Python. Besides deep learning, he also likes data visualization and teaching machine learning, either on online forums or as a teacher assistant.

Who is the target audience?

• This course is for developers who are looking for free, open source deep learning solutions for media (image and text) classification.

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TensorFlow 101: Introduction to Deep Learning

TensorFlow 101: Introduction to Deep Learning, Ready to build the future with Deep Neural Networks? Stand on the shoulder of TensorFlow and Keras for Machine Learning.

Publisher : Sefik Ilkin Serengil

Course Length : 3 hours

Course Language : English

Description

This course provides you to be able to build Deep Neural Networks models for different business domains with one of the most common machine learning library TensorFlow provided by Google AI team. The both concept of deep learning and its applications will be mentioned in this course. Also, we will focus on Keras. This course appeals to ones who interested in Machine Learning, Data Science and AI. Also, you don't have to be attend any ML course before.

Who is the target audience?

• One who interested in Machine Learning, Data Science and AI
• Anyone who would like to learn TensorFlow framework

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