Deep Learning Masterclass with TensorFlow 2 Over 20 Projects from Udemy

Deep Learning is one of the most popular fields in computer science today. It has applications in many and very varied domains. With the publishing of much more efficient deep learning models in the early 2010s, we have seen a great improvement in the state of the art in domains like Computer Vision, Natural Language Processing, Image Generation, and Signal Processing.

The demand for Deep Learning engineers is skyrocketing and experts in this field are highly paid, because of their value. However, getting started in this field isn’t easy. There’s so much information out there, much of which is outdated and many times don't take the beginners into consideration :(

In this course, we shall take you on an amazing journey in which you'll master different concepts with a step-by-step and project-based approach. You shall be using Tensorflow 2 (the world's most popular library for deep learning, and built by Google) and Huggingface. We shall start by understanding how to build very simple models (like Linear regression models for car price prediction, text classifiers for movie reviews, binary classifiers for malaria prediction) using Tensorflow and Huggingface transformers, to more advanced models (like object detection models with YOLO, lyrics generator model with GPT2 and Image generation with GANs)

After going through this course and carrying out the different projects, you will develop the skill sets needed to develop modern deep-learning solutions that big tech companies encounter.

You will learn:

The Basics of Tensorflow (Tensors, Model building, training, and evaluation)
Deep Learning algorithms like Convolutional neural networks and Vision Transformers
Evaluation of Classification Models (Precision, Recall, Accuracy, F1-score, Confusion Matrix, ROC Curve)
Mitigating overfitting with Data augmentation
Advanced Tensorflow concepts like Custom Losses and Metrics, Eager and Graph Modes and Custom Training Loops, Tensorboard
Machine Learning Operations (MLOps) with Weights and Biases (Experiment Tracking, Hyperparameter Tuning, Dataset Versioning, Model Versioning)
Binary Classification with Malaria detection
Multi-class Classification with Human Emotions Detection
Transfer learning with modern Convnets (Vggnet, Resnet, Mobilenet, Efficientnet) and Vision Transformers (VITs)
Object Detection with YOLO (You Only Look Once)
Image Segmentation with UNet
People Counting with Csrnet
Model Deployment (Distillation, Onnx format, Quantization, Fastapi, Heroku Cloud)
Digit generation with Variational Autoencoders
Face generation with Generative Adversarial Neural Networks
Text Preprocessing for Natural Language Processing.
Deep Learning algorithms like Recurrent Neural Networks, Attention Models, Transformers, and Convolutional neural networks.
Sentiment analysis with RNNs, Transformers, and Huggingface Transformers (Deberta)
Transfer learning with Word2vec and modern Transformers (GPT, Bert, ULmfit, Deberta, T5...)
Machine translation with RNNs, attention, transformers, and Huggingface Transformers (T5)
Model Deployment (Onnx format, Quantization, Fastapi, Heroku Cloud)
Intent Classification with Deberta in Huggingface transformers
Named Entity Relation with Roberta in Huggingface transformers
Neural Machine Translation with T5 in Huggingface transformers
Extractive Question Answering with Longformer in Huggingface transformers
E-commerce search engine with Sentence transformers
Lyrics Generator with GPT2 in Huggingface transformers
Grammatical Error Correction with T5 in Huggingface transformers
Elon Musk Bot with BlenderBot in Huggingface transformers
Speech recognition with RNNs

If you are willing to move a step further in your career, this course is destined for you and we are super excited to help achieve your goals.

This course is offered to you by Neuralearn. And just like every other course by Neuralearn, we lay much emphasis on feedback. Your reviews and questions in the forum will help us better this course. Feel free to ask as many questions as possible on the forum. We do our very best to reply in the shortest possible time.

Enjoy.

What's inside

Syllabus

Introduction

Welcome

General Introduction

Link to Code

Master the basics of Tensorflow Tensors and Variables

Training and Optimization

Tensor Basics

Tensor Initialization and Casting

Indexing

Maths Operations in Tensorflow

Linear Algebra Operations in Tensorflow

Common Methods

Ragged Tensors

Sparse Tensors

String Tensors

Tensorflow Variables

House Price Prediction with a Neural Network

Link to Dataset

Task Understanding

Data Preparation

Building Convnets in Tensorflow

Linear Regression Model

Error sanctioning

Performance Measurement

Validation and testing

Corrective Measures

TensorFlow Datasets

Build a simple working malaria diagnosis system

Task understanding

Data visualization

Data Processing

How and Why Convolutional Neural Networks work

Binary Crossentropy loss

Convnet training

Model evaluation and testing

Loading and Saving Tensorflow Models to Google Drive

Building more advanced Models with Functional API, Subclassing and Custom Layers

Functional API

Model Subclassing

Custom Layers

Evaluating Classification Models

Precision,Recall and Accuracy

Confusion Matrix

ROC Plots

Improving Model Performance

Tensorflow Callbacks

Learning rate scheduling

Model checkpointing

Mitigating overfitting and underfitting

Data augmentation

Data augmentation with TensorFlow using tf.image and Keras Layers

Mixup Data augmentation with TensorFlow 2 with intergration in tf.data

Cutmix Data augmentation with TensorFlow 2 and intergration in tf.data

Albumentations with TensorFlow 2 and PyTorch for Data augmentation

Advanced Tensorflow Concepts

Custom Loss and Metrics

Eager and graph modes

Custom training loops

Tensorboard integration

Data logging

Viewing model graphs

Hyperparameter tuning

Profiling and other visualizations with Tensorboard.

MLOps with Weights and Biases

Experiment tracking

Hyperparameter tuning with wandb

Dataset Versioning with Weights and Biases and TensorFlow 2

Model Versioning with Weights and Biases and TensorFlow 2

Human Emotions Detection

Data preparation

Modeling and training

Tensorflow records

Modern Convolutional Neural Networks

Alexnet

Vggnet

Resnet

Coding Resnets

Mobilenet

Efficientnet

Transfer Learning

Leveraging pretrained models

Finetuning

Understanding the blackbox

Visualizing intermediate layers

Grad-cam method

Ensembling and class imbalance

Ensembling

Class Imbalance

Transformers in Vision

Understanding VITs

Building VITs from scratch

Finetuning Huggingface transformers

Model evaluation with wandb

Data efficient transformers

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 Deep Learning Masterclass with TensorFlow 2 Over 20 Projects with these activities:

Review Linear Algebra Fundamentals

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Solidify your understanding of linear algebra concepts, which are fundamental to understanding tensor operations and neural network architectures used in deep learning.

Browse courses on Linear Algebra

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Review matrix operations (addition, multiplication, transpose).
Study vector spaces and linear transformations.
Practice solving linear equation systems.

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

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Gain a solid theoretical foundation in deep learning by studying this comprehensive textbook, which covers the mathematical and conceptual underpinnings of the field.

View Deep Learning on Amazon

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Read the chapters relevant to the course syllabus.
Work through the exercises and examples.
Take notes on key concepts and definitions.

Implement Neural Networks from Scratch

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Reinforce your understanding of neural network architectures by implementing them from scratch using NumPy or TensorFlow, without relying on high-level APIs.

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Implement a simple feedforward neural network.
Implement backpropagation for training.
Test your implementation on a simple dataset.

Four other activities

Expand to see all activities and additional details

Show all seven activities

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

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Enhance your practical skills in building and deploying deep learning models using TensorFlow 2 by working through the examples and exercises in this hands-on guide.

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

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Read the chapters relevant to the course syllabus.
Work through the code examples and exercises.
Experiment with different model architectures and hyperparameters.

Build a Custom Image Classifier

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Apply your knowledge of convolutional neural networks and transfer learning to build a custom image classifier for a specific domain of your choice.

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Choose a dataset of images to classify.
Preprocess the data and split into training and validation sets.
Build and train a CNN model using TensorFlow.
Evaluate the model's performance and fine-tune as needed.

Write a Blog Post on a Deep Learning Topic

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Solidify your understanding of a specific deep learning topic by writing a blog post explaining the concepts, implementation details, and potential applications.

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Choose a deep learning topic from the course.
Research the topic and gather relevant information.
Write a clear and concise blog post explaining the topic.
Include code examples and visualizations to illustrate the concepts.

Contribute to a TensorFlow Open Source Project

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Deepen your understanding of TensorFlow and contribute to the community by contributing to an open-source TensorFlow project.

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Identify a TensorFlow open-source project on GitHub.
Find an issue to work on or propose a new feature.
Submit a pull request with your changes.

Career center

Learners who complete Deep Learning Masterclass with TensorFlow 2 Over 20 Projects 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 may be helpful for those interested in this role, as it focuses on using TensorFlow 2 and provides hands-on experience with over 20 projects. The course covers essential deep learning algorithms like Convolutional Neural Networks and Transformers, helping one gain expertise in building and training models. Furthermore, the course covers MLOps with Weights and Biases, which is essential for experiment tracking, hyperparameter tuning, and model versioning. Developing projects such as object detection models with YOLO and image generation with GANs demonstrates crucial skills for a Deep Learning Engineer.

See salaries and explore the career path for Deep Learning Engineer

Machine Learning Engineer

Machine Learning Engineers build and deploy machine learning models, which includes deep learning models. This course may be useful for those interested in machine learning particularly because it teaches TensorFlow 2. It will introduce you to building simple models like linear regression for car prices or text classifiers for movie reviews. The material covers advanced topics like custom losses and metrics, eager and graph modes, and custom training loops, enabling a Machine Learning Engineer to fine-tune models for optimal performance. The course also discusses transfer learning, which is valuable skill for a Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Computer Vision Engineer

A Computer Vision Engineer specializes in developing algorithms that enable computers to “see” and interpret images. If you would like to be a Computer Vision Engineer, you may find considerable material in this course of use. This course provides practical experience with using deep learning for computer vision tasks. This course helps in understanding and implementing models for object detection with YOLO and image segmentation with UNet, which are core skills for a Computer Vision Engineer. The curriculum will help you learn more about topics such as transfer learning with modern Convnets and Vision Transformers. The skills gained from this course will strengthen your ability to contribute to innovative computer vision projects.

See salaries and explore the career path for Computer Vision Engineer

Natural Language Processing Engineer

Natural Language Processing Engineers work on systems that allow computers to understand and process human language. This course addresses key skills for those who would like to become a Natural Language Processing Engineer. The course covers essential techniques for text preprocessing, sentiment analysis, and machine translation. It also discusses transfer learning with Word2vec and modern Transformers, such as GPT, Bert, and T5, which are crucial for state-of-the-art natural language processing applications. This course may be useful as it offers the chance to apply these skills through projects like building lyrics generators with GPT2.

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

Artificial Intelligence Researcher

An Artificial Intelligence Researcher explores new algorithms and models to advance the field of artificial intelligence. This course may be useful for those interested in research, as it covers a lot of deep learning algorithms and techniques, such as Generative Adversarial Networks for image generation and Variational Autoencoders for digit generation. This course touches on topics like custom losses and metrics, eager and graph modes, and custom training loops. The course also covers a great variety of topics, such as data augmentation, transfer learning, and model deployment, providing researchers with diverse tools. Those seeking careers as Artificial Intelligence Researchers may find this course valuable.

See salaries and explore the career path for Artificial Intelligence Researcher

Data Scientist

Data Scientists analyze complex data sets to extract insights and inform decision-making, and deep learning is a key tool in this field. This course may be useful as it provides strong grounding in TensorFlow 2 and hands-on experience with various deep learning projects. The curriculum spans various topics like building simple models for prediction and classification to advanced models for object detection and image generation. Data Scientists can leverage this knowledge to build predictive models, classify complex patterns, and generate insights from unstructured data. The knowledge of model evaluation and hyperparameter tuning covered in the course is valuable for improving a Data Scientist's models.

See salaries and explore the career path for Data Scientist

Machine Learning Scientist

A Machine Learning Scientist researches and develops new machine learning algorithms and techniques, often with a focus on deep learning. This course may be useful for those in the field of machine learning because it will introduce you to the practical applications of concepts like custom loss functions, eager and graph modes, and custom training loops. This course goes into detail on topics such as mitigating overfitting and underfitting, and advanced TensorFlow concepts. It is also a good way to learn about MLOps with Weights and Biases, which is essential for experiment tracking, hyperparameter tuning, and model versioning. Those seeking careers as Machine Learning Scientists may find this course valuable.

See salaries and explore the career path for Machine Learning Scientist

Robotics Engineer

Robotics Engineers design, build, and program robots, often using computer vision and machine learning. This course helps those interested in robotics to develop skills in deep learning, which is increasingly used in robotics for tasks like object detection, image segmentation, and autonomous navigation. The curriculum includes hands-on projects like object detection with YOLO and image segmentation with UNet, providing practical experience in computer vision. This course also covers transfer learning with modern networks, and finetuning Huggingface transformers.

See salaries and explore the career path for Robotics Engineer

Software Engineer

Software Engineers design, develop, and test software applications, and an understanding of deep learning algorithms can enhance their capabilities, especially in AI-driven applications. This course may be useful for a software engineer who wants to add deep learning to their qualifications. It covers TensorFlow 2 and offers practical experience with over 20 projects. This course helps develop skills in building and deploying deep learning models, using evaluation methods, and improving model performance with techniques like data augmentation and callbacks. The knowledge of model deployment with Fastapi and Heroku Cloud prepares the Software Engineer to integrate deep learning models into real-world applications.

See salaries and explore the career path for Software Engineer

Data Engineer

Data Engineers build and maintain the infrastructure for data storage and processing, which includes supporting machine learning and deep learning pipelines. This course may be useful for a data engineer to learn deep learning approaches. The curriculum covers TensorFlow Datasets and MLOps with Weights and Biases. It also gives the learner the opportunity to learn about machine learning concepts that are crucial for building efficient data pipelines, experiment tracking, hyperparameter tuning, and model versioning. This course offers a good foundation for Data Engineers to support deep learning initiatives.

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Cloud Engineer

Cloud Engineers manage and maintain cloud computing infrastructure, and since many machine learning applications are deployed in the cloud, the course may be useful. It covers model deployment using Fastapi and Heroku Cloud, which are important skills for deploying deep learning models on cloud platforms. This course offers a foundation for understanding how deep learning models can be integrated into cloud-based applications, making you a more versatile Cloud Engineer.

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Quantitative Analyst

A Quantitative Analyst uses mathematical and statistical methods to solve financial problems, and machine learning is increasingly used in this field. This course may give a quantitative analyst a background in deep learning approaches. The curriculum includes building simple models for prediction and classification, which can be applied to financial forecasting and risk analysis. You may find the training helpful in model building, evaluation, and hyperparameter tuning, which can enhance quantitative models used in finance. Knowledge of TensorFlow use will allow the analyst to bring additional tools to their work.

See salaries and explore the career path for Quantitative Analyst

Research Scientist

Research Scientists conduct experiments and analyze data to advance knowledge in their field, and deep learning is a powerful tool for many research areas. This course may be useful for research scientists to learn about TensorFlow 2 and gain hands-on experience with deep learning projects. It spans topics like building models for prediction and classification to advanced models for object detection and image generation. Those seeking careers as Research Scientists may find this course valuable.

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Business Intelligence Analyst

Business Intelligence Analysts analyze data to identify trends and insights that can improve business performance, and machine learning can enhance this process. This course may be useful to Business Intelligence Analysts as it provides a foundational understanding of deep learning models, how to build simple models for prediction and binary classification. The skills gained can be applied to improve predictive capabilities in business analytics. Understanding how to perform hyperparameter tuning may allow you to bring additional experience to your role.

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Site Reliability Engineer

Site Reliability Engineers ensure the reliability and performance of software systems, and understanding machine learning can help them automate tasks and improve system monitoring. This course may be useful as it provides a foundational understanding of deep learning models, and may support the use of machine learning techniques in system monitoring and anomaly detection. This background could allow you to bring additional knowledge to your role.

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Deep Learning Masterclass with TensorFlow 2 Over 20 Projects

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