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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 transformersRequirements
Basic Math Access to an internet connection, as we shall be using Google Colab (free version) Basic Knowledge of PythonDescription
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 ρáíd, 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 RNNsIf 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 recognitionHomepage
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