What is the price of the PyTorch Training - Master Deep Learning in 5 Days training?

The price of the PyTorch Training - Master Deep Learning in 5 Days training is 5250 € HT. This includes access to all modules, training materials and personalized support. Funding solutions are available through OPCO.

Is the training Qualiopi certified?

Yes, Learni is a Qualiopi-certified training organization. This certification guarantees the quality of our courses and enables funding through OPCO and other mechanisms.

What is the duration of the PyTorch Training - Master Deep Learning in 5 Days training?

The PyTorch Training - Master Deep Learning in 5 Days training takes place over 35 hours. The program is structured in progressive modules for optimal skill development.

How can I fund my training?

Our courses are eligible for OPCO funding. Learni supports you through the funding process. A personalized quote is available upon request.

What are the prerequisites for this training?

The prerequisites for the PyTorch Training - Master Deep Learning in 5 Days training are: Proficiency in Python, basics in NumPy, knowledge of linear algebra.. A preliminary interview validates your eligibility.

What are the objectives of the PyTorch Training - Master Deep Learning in 5 Days training?

The objectives of the PyTorch Training - Master Deep Learning in 5 Days training are: Install and configure PyTorch with GPU.. Manipulate tensors and autograd for automatic calculations.. Build simple neural network models.. Train and optimize models on real datasets.. Evaluate performance with precise metrics.. Deploy a basic PyTorch model in production.. Upon completion, you will be able to put these skills into practice.

Is the training available in person?

Yes, the PyTorch Training - Master Deep Learning in 5 Days training is available in in-person. The format can be adapted to your needs (inter, intra, custom).

PyTorch Training - Master Deep Learning in 5 Days | Learni

The story of Learni

Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.

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Methods, approaches, and teaching resources

The PyTorch Training - Master Deep Learning in 5 Days training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.

The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.

Teaching resources provided

To ensure the quality of the PyTorch Training - Master Deep Learning in 5 Days training, Learni provides the following teaching resources:

Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, video projector or interactive touchscreen (for remote sessions)
Training environments installed on workstations or accessible online
Course materials, practical exercises, and supplementary resources
Post-training access to materials and teaching resources

For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.

* contact us for remote feasibility** ratio varies depending on the training followed

Assessment methods

The assessment of skills acquired during the PyTorch Training - Master Deep Learning in 5 Days training is carried out through:

During training: case studies, practical exercises, and professional scenarios
At the end of training: self-assessment questionnaire and skills evaluation by the trainer
After training: training completion certificate detailing acquired skills

Training accessibility

Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.

Training access terms and deadlines

Training program

Module 1Day 1: PyTorch Installation and Essential Tensors (CUDA tools, vector operations, deliverables: interactive notebook)

Dive into PyTorch by installing the GPU environment, manipulate tensors for fast calculations, complete exercises on shapes and broadcasts, code your first gradients with autograd, test on MNIST datasets, and leave with a functional notebook ready to scale your AI projects.

Module 2Day 2: nn.Module and Basic Architectures (build methods, forward pass, deliverables: linear model)

Build your first nn.Module modules, assemble linear layers and ReLU activations, train a classifier on Fashion-MNIST using DataLoader, optimize with SGD and Adam, visualize losses in real-time, and apply to a concrete image classification case to boost your practical skills.

Module 3Day 3: CNN and PyTorch Convolutions (Conv2D tools, pooling, deliverables: convolutional network)

Master CNNs with Conv2d and MaxPool, code a network for CIFAR-10 object recognition, handle data augmentations with transforms, monitor training with TensorBoard, debug overfitting with dropout, and finish with a high-performing model on validation for immediate results.

Module 4Day 4: RNN, LSTM and Sequences (recurrent methods, backprop through time, deliverables: text predictor)

Explore RNN and LSTM for time series, process text with embeddings, train a sequence generator on IMDB reviews, handle masks and packing, evaluate perplexity, integrate into a concrete NLP project, and gain fluency in analyzing your own sequential data.

Module 5Day 5: Deployment and Advanced Projects (TorchScript, ONNX, deliverables: deployed app)

Optimize models with quantization, export to TorchScript for fast inference, test Flask deployment, integrate GPU inference, finalize a capstone project on a custom dataset, receive expert feedback, and leave as an expert ready to integrate PyTorch into your professional workflows.