From Basics to Advanced Deep Learning Training(AI)
Note: Course Free for limited time and limited users(500 limit) so Enroll ASAP. If not free it means you are late. Scroll Down and click Enroll Now button to get enrolled
Scroll Down and click Enroll Now Button to get enrolled into the Free Course
Apply Coupon Code: FEBFREE02
What you’ll learn
- Understand PyTorch fundamentals, including tensors and computation graphs
- Build and train neural networks using PyTorch’s nn_Module
- Preprocess and load datasets with DataLoaders and custom datasets
- Implement advanced architectures like CNNs, RNNs, and Transformers
- Perform transfer learning and fine-tune pre-trained models
- Optimize models using hyperparameter tuning and regularization
- Deploy trained models using TorchScript and cloud services
- Debug and troubleshoot deep learning models effectively
- Develop custom layers, loss functions, and models
- Collaborate with the PyTorch community and contribute to open-source projects
Explore related topics
This course includes:
- 2.5 hours on-demand video
- 100 articles
- Access on mobile and TV
- Full lifetime access
- Closed captions
- Certificate of completion
Course content
6 sections • 119 lectures • 4h 18m total length
- Certificate of Completion
- Introduction to Learning PyTorch from Basics to Advanced Complete Training
- Introduction to PyTorch
- Getting Started with PyTorch
- Working with Tensors
- Autograd and Dynamic Computation Graphs
- Building Simple Neural Networks
- Loading and Preprocessing Data
- Model Evaluation and Validation
- Advanced Neural Network Architectures
- Transfer Learning and Fine-Tuning
- Handling Complex Data
- Model Deployment and Production
- Debugging and Troubleshooting
- Distributed Training and Performance Optimization
- Custom Layers and Loss Functions
- Research-oriented Techniques
- Integration with Other Libraries
- Contributing to PyTorch and Community Engagement
- Project 1: Linear Regression from Scratch
- Project 2: Logistic Regression Classifier
- Project 3: MNIST Digit Classifier
- Project 4: Binary Image Classifier (Cats vs Dogs)
- Project 5: Neural Network with Custom Activation
- Project 6: Softmax Classifier on FashionMNIST
- Project 7: Multi-layer Perceptron with Dropout
- Project 8: Visualizing Gradients using Hooks
- Project 9: Implement Batch Normalization
- Project 10: Weight Initialization
- Project 11: Handwritten Character Recognition (EMNIST)
- Project 12: XOR Neural Network
- Project 13: Sentiment Classifier with Bag-of-Words
- Project 14: CNN on CIFAR-10
- Project 15: Image Denoising with Autoencoders
- Project 16: PyTorch Lightning Hello World
- Project 17: Image Augmentation Pipeline
- Project 18: Saving & Loading Models
- Project 19: Confusion Matrix Visualization
- Project 20: Training Loop vs DataLoader Comparison
- Project 21: LeNet Implementation
- Project 22: AlexNet from Scratch
- Project 23: VGG-16 Classifier
- Project 24: ResNet-18 with Transfer Learning
- Project 25: Inception Network (GoogLeNet)
- Project 26: MobileNetV2 for Mobile Inference
- Project 27: EfficientNet on Custom Dataset
- Project 28: Custom CNN with Skip Connections
- Project 29: Training with Mixed Precision (AMP)
- Project 30: Visualize Feature Maps in CNN
- Project 31: Grad-CAM Visualization
- Project 32: Fine-Tune Pretrained ResNet
- Project 33: Train a Network on GPU and CPU
- Project 34: Comparison of Optimizers (SGD vs Adam)
- Project 35: Neural Style Transfer
- Project 36: Siamese Network for Face Similarity
- Project 37: Implement CutMix & MixUp
- Project 38: Contrastive Learning with SimCLR
- Project 39: Vision Transformer (ViT) from Scratch
- Project 40: Swin Transformer Mini Project
- Project 41: Text Classification with RNN
- Project 42: Named Entity Recognition (NER)
- Project 43: Bi-LSTM for Sequence Tagging
- Project 44: GRU vs LSTM Comparison
- Project 45: Character-Level Language Model
- Project 46: Machine Translation with Seq2Seq
- Project 47: Transformer-Based Text Summarization
- Project 48: Text Generation with GPT-2
- Project 49: Fine-Tune BERT for Sentiment Analysis
- Project 50: Question Answering with BERT
- Project 51: Semantic Search with Sentence Transformers
- Project 52: Text Clustering using Sentence Embeddings
- Project 53: Text Classification using DistilBERT
- Project 54: Text-to-Image Embedding with CLIP
- Project 55: Visual Question Answering with BLIP
- Project 56: OCR with Transformers (TrOCR)
- Project 57: Zero-Shot Text Classification with NLI Models
- Project 58: Multimodal Sentiment Analysis (Text + Image)
- Project 59: Speech-to-Text with Wav2Vec2
- Project 60: Speech Emotion Recognition
- Project 61: Audio Classification with CNNs
- Project 62: Real-Time Audio Classification
- Project 63: Keyword Spotting with Custom Commands
- Project 64: Real-Time Keyword Spotting with Microphone Input
- Project 65: Emotion Detection from Facial Images
- Project 66: Gender and Age Prediction from Faces
- Project 67: Real-Time Face Recognition System
- Project 68: Mask Detection on Live Video Feed
- Project 69: Gesture Recognition using Hand Keypoints
- Project 70: Eye Gaze Tracking and Blink Detection
- Project 71: Pose Estimation with MediaPipe
- Project 72: Yoga Pose Correction System
- Project 73: Sign Language Alphabet Recognition
- Project 74: Real-Time ASL Recognition with Webcam
- Project 75: Text-to-Speech (TTS) with Tacotron2 and WaveGlow
- Project 76: Neural Voice Cloning (Multi-Speaker TTS)
- Project 77: Music Genre Classification with CNNs
- Project 78: Music Generation with RNN (Note Sequences)
- Project 79: Automatic Chord Recognition from Audio
- Project 80: Audio Denoising with Autoencoders
- Project 81: Real-Time Noise Suppression using Denoising Autoencoder
- Project 82: Speaker Diarization (Who Spoke When)
- Project 83: Real-Time Speaker Identification
- Project 84: Voice Activity Detection (VAD)
- Project 85: Transcription with Speaker Tags (ASR + Diarization)
- Project 86: Emotion-Aware Transcription System
- Project 87: Audio-Driven Avatar Lip Sync Generator
- Project 88: Music Source Separation (Vocals, Drums, etc.)
- Project 89: Audio Captioning (Describing Sound Events)
- Project 90: Acoustic Scene Classification
- Project 91: Urban Sound Event Detection (Gunshot, Siren, Dog Bark, etc.)
- Project 92: Audio Fingerprinting and Song Recognition (Shazam-Like System)
- Project 93: Environmental Sound Tagging (Multi-Label Audio Classification)
- Project 94: AI Audio Scene Narrator (Sound → Story Description)
- Project 95: Lip Reading from Silent Video using Vision Transformers
- Project 96: Audio-Visual Emotion Recognition (Multimodal)
- Project 97: Audio Style Transfer (Convert Guitar to Violin Style)
- Project 98: Audio-Based Anomaly Detection (Industrial Machines, ECG, etc.)
- Project 99: Speaker Conversion (Make One Voice Sound Like Another)
- Project 100: Real-Time Voice Cloning and Emotion Transfer
- Basic Computer Skills: Familiarity with using a computer and installing software
- Python Programming: Basic knowledge of Python (variables, functions, loops)
- Mathematics: Understanding of basic algebra, linear algebra, and calculus concepts (vectors, matrices, derivatives)
- Machine Learning Basics (optional): Awareness of ML concepts like models, training, and evaluation is helpful but not mandatory
- Enthusiasm to Learn: A willingness to learn through hands-on projects and experiments
Description
The “Mastering PyTorch: From Basics to Advanced Deep Learning Training” course is a complete learning journey designed for beginners and professionals aiming to excel in artificial intelligence and deep learning. This course begins with the fundamentals of PyTorch, covering essential topics such as tensor operations, automatic differentiation, and building neural networks from scratch. Learners will gain a deep understanding of how PyTorch’s dynamic computation graph works, enabling flexible model creation and troubleshooting.
As the course progresses, students will explore advanced topics, including complex neural network architectures such as CNNs, RNNs, and Transformers. It also dives into transfer learning, custom layers, loss functions, and model optimization techniques. Learners will practice building real-world projects, such as image classifiers, NLP-based sentiment analyzers, and GAN-powered applications.
The course places a strong emphasis on hands-on implementation, offering step-by-step exercises, coding challenges, and projects that reinforce key concepts. Additionally, learners will explore cutting-edge techniques like distributed training, cloud deployment, and integration with popular libraries.
By the end of the course, learners will be proficient in designing, building, and deploying AI models using PyTorch. They will also be equipped to contribute to open-source projects and pursue careers as AI engineers, data scientists, or ML researchers in the growing field of deep learning.
Who this course is for:
- Beginners in AI/ML: Those with no prior deep learning experience but eager to learn PyTorch from scratch
- Data Science Enthusiasts: Aspiring data scientists looking to add PyTorch to their ML toolkit
- Developers and Engineers: Software developers transitioning into AI and deep learning roles
- Researchers and Academics: Those exploring cutting-edge ML research using PyTorch
- Career Switchers: Professionals transitioning to AI-related careers
Note: The course is free for a limited time and limited to 500 users. Enroll as soon as possible. If it is no longer free, it means you are late, and it has already exceeded its limits.
🚨How to Enroll into free Udemy courses
👉 https://youtu.be/bN6IfeIW-6E?feature=shared
🎫 After adding the course into Cart at Checkout page, Apply Coupon Code➛. FEBFREE02 Note:- After applying coupon, if it says ”This coupon has exceeded its maximum possible redemptions…”, then it means you’re Late, and first 500 coupons already redeemed.
