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Deep Learning Masterclass with TensorFlow 2 Over 20 Projects
Last updated 2/2026
Created by Neuralearn Dot AI
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English + subtitle | Duration: 204 Lectures ( 58h 28m ) | Size: 34.3 GB
Master Deep Learning with TensorFlow 2 with Computer Vision,Natural Language Processing, Sound Recognition & Deployment

What you'll learn

The Basics of Tensors and Variables with Tensorflow

Basics of Tensorflow and training neural networks with TensorFlow 2.

Convolutional Neural Networks applied to Malaria Detection

Building more advanced Tensorflow models with Functional API, Model Subclassing and Custom Layers

Evaluating Classification Models using different metrics like: Precision,Recall,Accuracy and F1-score

Classification Model Evaluation with Confusion Matrix and ROC Curve

Tensorflow Callbacks, Learning Rate Scheduling and Model Check-pointing

Mitigating Overfitting and Underfitting with Dropout, Regularization, Data augmentation

Data augmentation with TensorFlow using TensorFlow image and Keras Layers

Advanced augmentation strategies like Cutmix and Mixup

Data augmentation with Albumentations with TensorFlow 2 and PyTorch

Custom Loss and Metrics in TensorFlow 2

Eager and Graph Modes in TensorFlow 2

Custom Training Loops in TensorFlow 2

Integrating Tensorboard with TensorFlow 2 for data logging, viewing model graphs, hyperparameter tuning and profiling

Machine Learning Operations (MLOps) with Weights and Biases

Experiment tracking with Wandb

Hyperparameter tuning with Wandb

Dataset versioning with Wandb

Model versioning with Wandb

Human emotions detection

Modern convolutional neural networks(Alexnet, Vggnet, Resnet, Mobilenet, EfficientNet)

Transfer learning

Visualizing convnet intermediate layers

Grad-cam method

Model ensembling and class imbalance

Transformers in Vision

Model deployment

Conversion from tensorflow to Onnx Model

Quantization Aware training

Building API with Fastapi

Deploying API to the Cloud

Object detection from scratch with YOLO

Image Segmentation from scratch with UNET model

People Counting from scratch with Csrnet

Digit generation with Variational autoencoders (VAE)

Face generation with Generative adversarial neural networks (GAN)

Sentiment Analysis with Recurrent neural networks, Attention Models and Transformers from scratch

Neural Machine Translation with Recurrent neural networks, Attention Models and Transformers from scratch

Intent Classification with Deberta 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
Requirements

Basic Math

Access to an internet connection, as we shall be using Google Colab (free version)

Basic Knowledge of Python
Description
Deep Learningis 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 likeComputer Vision, Natural Language Processing, Image Generation, and Signal Processing.
Thedemand for Deep Learning engineers is skyrocketing and experts in this field arehighly 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 usingTensorflow 2 (the world's most popular library for deep learning, and built by Google) andHuggingface.We shall start by understanding how to build very simple models (like Linear regression models forcar price prediction, text classifiers formovie reviews, binary classifiers formalaria prediction) using Tensorflow and Huggingface transformers, to more advanced models (like object detection models withYOLO, lyrics generator model withGPT2and Image generation withGANs)
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 likeConvolutional neural networks and Vision Transformers

Evaluation of Classification Models (Precision, Recall, Accuracy, F1-score, Confusion Matrix, ROC Curve)

Mitigating overfitting withData augmentation

Advanced Tensorflow concepts likeCustom 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 withMalaria detection

Multi-class Classification withHuman 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 withUNet

People Counting withCsrnet

Model Deployment (Distillation, Onnx format, Quantization, Fastapi, Heroku Cloud)

Digit generation withVariational Autoencoders

Face generation withGenerative Adversarial Neural Networks

Text Preprocessing for Natural Language Processing.

Deep Learning algorithms likeRecurrent 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 withDebertain Huggingface transformers

Named Entity Relation withRoberta in Huggingface transformers

Neural Machine Translation withT5 in Huggingface transformers

Extractive Question Answering withLongformer in Huggingface transformers

E-commerce search engine withSentence transformers

Lyrics Generator withGPT2 in Huggingface transformers

Grammatical Error Correction withT5 in Huggingface transformers

Elon Musk Bot withBlenderBot in Huggingface transformers

Speech recognition with RNNs
If you are willing to move astep 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 byNeuralearn. 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!!!
Who this course is for

Beginner Python Developers curious about Applying Deep Learning for Computer vision and Natural Language Processing

Deep Learning for Computer vision Practitioners who want gain a mastery of how things work under the hood

Anyone who wants to master deep learning fundamentals and also practice deep learning for computer vision using best practices in TensorFlow.

Computer Vision practitioners who want to learn how state of art computer vision models are built and trained using deep learning.

Natural Language Processing practitioners who want to learn how state of art NLP models are built and trained using deep learning.

Anyone wanting to deploy ML Models

Learners who want a practical approach to Deep learning for Computer vision, Natural Language Processing and Sound recognition
Homepage

Code:

https://www.udemy.com/course/deep-learning-masterclass-with-tensorflow-2-over-15-projects

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Deep Learning Masterclass with TensorFlow 2 Over 20 Projects

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