Deep Learning Courses and Certifications

Learning Deep Learning: Building AI Applications

Build CNNs for image classification with AlexNet, VGGNet and ResNet, RNNs and LSTMs for forecasting, and Transformers with attention for machine translation in TensorFlow and PyTorch.

• Coursera
• 7 hours 40 minutes
• Self-Paced
• Paid Course

Board Infinity

Deep Learning: Advanced Backbones and Efficient GPU Training

Build and fine-tune ConvNeXt and Vision Transformer backbones with PyTorch Lightning and timm, applying RMSNorm, SwiGLU, RoPE, mixed precision, DDP/FSDP, and W&B tracking.

• Coursera
• 4 weeks, 5 hours a week
• Self-Paced
• Paid Course

Packt

Deep Learning, NLP, and AI Applications

Build deep learning models with TensorFlow, Keras, and PyTorch: train CNNs for image classification, RNNs for sequence tasks, and transformers like BERT and GPT for summarization and translation.

• Coursera
• 6 weeks, 3 hours a week
• Self-Paced
• Paid Course

Coursera Career Certificate

Machine Learning Engineer: ML and Deep Learning Models

Turn real problems into machine learning tasks: build supervised models, design custom neural networks in PyTorch, and tune, debug, and optimize deep learning models.

• Coursera
• 8 weeks, 10 hours a week
• Self-Paced
• Paid Course
• 4 courses

Learning Deep Learning: Generative AI and Large Language Models

Build deep learning models with BERT, GPT, R-CNN, U-Net and Mask R-CNN in TensorFlow and PyTorch, covering multimodal networks, multitask learning, object detection, segmentation and AI ethics.

• Coursera
• 6 hours 29 minutes
• Self-Paced
• Paid Course

Packt

Deep Learning - Crash Course 2023

Build neural networks from MP Neuron and Perceptron to Sigmoid Neuron and deep networks in TensorFlow 2.x and Keras, with Python, NumPy, Pandas, and Matplotlib.

• Coursera
• 17 weeks, 1 hour a week
• Self-Paced
• Paid Course

Whizlabs

Fundamentals of Deep Learning

Train and evaluate neural networks: forward and backward propagation, gradient descent, activation functions, MNIST multi-class classification, convolutional networks, and transfer learning on image datasets.

• Coursera
• 2 weeks, 3 hours a week
• Self-Paced
• Paid Course

Packt

Modern Deep Learning Foundations

Learn deep learning from the ground up: loss functions, backpropagation, CNNs, RNNs, autoencoders and Transformers, plus data augmentation, explainability, and deployment with TensorFlow, PyTorch and Colab.

• Coursera
• 5 weeks, 1 hour a week
• Self-Paced
• Paid Course

Coursera

Deep Learning and Modern AI Architectures

Build and evaluate CNNs such as LeNet, VGG, and ResNet, fine-tune pre-trained models with transfer learning, and compare RNNs, LSTMs, transformers, autoencoders, VAEs, and GANs.

• Coursera
• 4 weeks, 7 hours a week
• Self-Paced
• Paid Course

Packt

Deep Learning and Advanced Techniques

Build and train neural networks in TensorFlow, Keras, and PyTorch, apply ensemble methods like XGBoost and LightGBM, and fine-tune models with transfer learning on CIFAR-10.

• Coursera
• 3 weeks, 3 hours a week
• Self-Paced
• Paid Course

Packt

Deep Learning & Modern AI Architectures

Build CNNs, RNNs, and Transformers with TensorFlow, Keras, and PyTorch: classify CIFAR-10 images, generate text, and fine-tune pre-trained BERT and GPT models.

• Coursera
• 5 weeks, 3 hours a week
• Self-Paced
• Paid Course

Board Infinity

Deep Learning: Train Neural Networks and Deploy with Docker

Design and train neural networks in PyTorch, track experiments with TensorBoard and Weights & Biases, then serve and scale models via FastAPI and Docker.

• Coursera
• 4 weeks, 4 hours a week
• Self-Paced
• Paid Course

LearnKartS

Become AI-Ready: Deep Learning Fundamentals

Learn how deep learning models are built, trained, and corrected: neural network forward pass and backpropagation in TensorFlow Playground, plus RNNs and LSTMs for sequence data.

• Coursera
• 3 weeks, 3 hours a week
• Self-Paced
• Paid Course

EDUCBA

Analyze and Build Deep Learning Models with TensorFlow

Build end-to-end deep learning models in TensorFlow and Keras: neural network layers, activation functions, backpropagation, dataset preparation, and evaluating models for classification.

• Coursera
• 2 weeks, 2 hours a week
• Self-Paced
• Paid Course

EDUCBA Career Certificate

Applied Deep Learning and neural networks

Build and evaluate deep learning models with TensorFlow, apply computer vision techniques including object detection and segmentation, and predict car prices using regression and regularization.

• Coursera
• 4 weeks, 10 hours a week
• Self-Paced
• Paid Course
• 3 courses