Deep Learning Academy
Master deep learning with PyTorch, from neurons and backpropagation to CNNs, Transformers, and production MLOps.
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How You'll Learn
30 Courses
Every course in the Deep Learning Academy learning path.
What Deep Learning Really Is
Explain how deep learning differs from classic ML and where it shines.
- AI vs Machine Learning vs Deep Learning
- Why Neural Nets Beat Hand-Crafted Features
- Where Deep Learning Wins (and Where It Doesn't)
- +1 more
Set Up Your PyTorch Workshop
Install PyTorch, run on CPU or GPU, and confirm your environment works.
- Install PyTorch and Verify It Imports
- CPU vs GPU vs MPS: Pick a Device
- Notebooks, Scripts & Reproducible Seeds
- +1 more
Tensors: The Data of Deep Learning
Create, reshape, and do math with PyTorch tensors confidently.
- Shapes, Dtypes & Indexing
- Reshape, View, Squeeze & Unsqueeze
- Broadcasting Rules That Save You Loops
- +1 more
From NumPy to Vectorized Thinking
Replace Python loops with fast vectorized tensor operations.
- Why Loops Are Slow for Math
- Elementwise Ops & Reductions
- Matrix Multiply with matmul and @
- +1 more
The Neuron & The Perceptron
Build a single neuron and watch it draw a decision boundary.
- Weights, Bias & the Weighted Sum
- The Step Function & Linear Decisions
- Code a Perceptron from Scratch
- +1 more
Activation Functions That Make Nets Nonlinear
Choose ReLU, sigmoid, tanh, and softmax for the right job.
- Why Nonlinearity Unlocks Real Power
- ReLU and Its Leaky & GELU Cousins
- Sigmoid & Tanh: Squashing to a Range
- +1 more
Gradient Descent Intuition
Understand how a model rolls downhill toward lower loss.
- Loss as a Landscape to Descend
- Gradients Point Uphill — So Step the Other Way
- Learning Rate: Too Big, Too Small, Just Right
- +1 more
Autograd: PyTorch Does Calculus for You
Use requires_grad and backward() to compute gradients automatically.
- requires_grad and the Computation Graph
- Call backward() to Get Gradients
- Reading and Zeroing .grad
- +1 more
Your First Neural Network with nn.Module
Define, instantiate, and run a feedforward network in PyTorch.
- Subclass nn.Module: __init__ and forward
- Stacking Linear Layers
- nn.Sequential for Quick Models
- +1 more
Train a Classifier on Real Data
Train a small network end-to-end and measure its accuracy.
- Forward Pass, Loss, Backward, Step
- Write a Minimal Training Loop
- Track Accuracy While You Train
- +1 more
Backpropagation Demystified
Trace how errors flow backward to update every weight.
- The Chain Rule, Layer by Layer
- Forward Caches, Backward Reuses
- Backprop a Tiny Net by Hand
- +1 more
Loss Functions for Every Task
Pick the right loss for regression, binary, and multiclass problems.
- MSE & MAE for Regression
- Binary Cross-Entropy with Logits
- Cross-Entropy for Multiclass
- +1 more
Optimizers Beyond Plain SGD
Tune SGD, momentum, and Adam to train faster and more stably.
- SGD with Momentum
- Adam & AdamW Explained
- Weight Decay vs L2 Regularization
- +1 more
Build a Robust Training Loop
Add validation, checkpoints, and metrics to a production-grade loop.
- Split Train, Validation & Test
- An Epoch Loop with Validation
- Save & Load with state_dict
- +1 more
Data Pipelines with Dataset & DataLoader
Feed data efficiently with custom Datasets and batched DataLoaders.
- Write a Custom Dataset Class
- Batching, Shuffling & num_workers
- collate_fn for Variable-Length Inputs
- +1 more
Fight Overfitting & Regularize
Diagnose overfitting and apply dropout, augmentation, and norms.
- Read the Train/Val Gap
- Dropout: Randomly Drop Neurons
- Batch Norm & Layer Norm
- +1 more
Convolutional Networks for Images
Build a CNN that learns visual features from raw pixels.
- Convolution: Kernels Slide Over Pixels
- Stride, Padding & Pooling
- Channels, Feature Maps & Receptive Fields
- +1 more
Classic CNN Architectures
Understand LeNet to ResNet and the ideas that made nets deeper.
- LeNet & AlexNet: The First Wins
- VGG: Stacks of Small Filters
- ResNet: Skip Connections Go Deep
- +1 more
Sequence Models: RNNs & LSTMs
Model ordered data with recurrent networks and gated memory.
- Why Sequences Need Memory
- The Vanilla RNN Cell
- LSTM & GRU Gates
- +1 more
Embeddings & Text as Numbers
Turn words into dense vectors a network can learn from.
- Tokenize and Build a Vocabulary
- nn.Embedding: Learnable Word Vectors
- Why Embeddings Capture Meaning
- +1 more
Attention & The Transformer
Implement self-attention and assemble a transformer block.
- Self-Attention: Query, Key & Value
- Scaled Dot-Product & Multi-Head
- Positional Encoding for Order
- +1 more
Transfer Learning & Fine-Tuning
Adapt pretrained models to your task with little data.
- Freeze the Backbone, Train the Head
- Fine-Tune with a Lower Learning Rate
- Discriminative Layer-Wise Rates
- +1 more
Autoencoders & Representation Learning
Compress and reconstruct data to learn useful latent codes.
- Encoder, Bottleneck & Decoder
- Denoising Autoencoders
- Variational Autoencoders & the Latent Space
- +1 more
Generative Adversarial Networks
Train a generator and discriminator to create new images.
- Generator vs Discriminator: The Game
- The Adversarial Loss
- Build a DCGAN
- +1 more
Diffusion Models & Modern Generation
Grasp how diffusion models denoise their way to new samples.
- Forward Noising & Reverse Denoising
- Predict the Noise with a U-Net
- Sampling Schedules & Guidance
- +1 more
Train Faster: Mixed Precision & Profiling
Speed up training with AMP, profiling, and GPU memory tactics.
- Mixed Precision with autocast & GradScaler
- Gradient Accumulation for Big Batches
- Profile the Bottleneck
- +1 more
Scale Out: Distributed Training
Train across multiple GPUs with DataParallel and DDP.
- Data vs Model Parallelism
- DistributedDataParallel Basics
- Sync Batch Norm & Sharded State
- +1 more
Evaluate & Debug Deep Models
Measure models honestly and interpret why they predict.
- Precision, Recall, F1 & ROC-AUC
- Confusion Matrices & Error Analysis
- Grad-CAM: See What the Model Looks At
- +1 more
Deploy Models to Production
Export, optimize, and serve a trained model behind an API.
- TorchScript & torch.compile
- Export to ONNX
- Quantization for Smaller, Faster Models
- +1 more
MLOps: Experiments to Reliable Pipelines
Track, version, and monitor models like a production team.
- Track Experiments with Weights & Biases
- Version Data & Models
- Detect Data & Model Drift
- +1 more
Frequently Asked Questions
Is the Deep Learning Academy course free?
Yes. You can start the Deep Learning Academy course for free and complete its interactive lessons at no cost. An optional PRO subscription unlocks advanced AI tools and a shareable certificate.
Do I need prior experience to learn PYTHON?
No. The course begins with the fundamentals and gradually moves to more advanced topics, so you can start even with no prior PYTHON experience.
How will I learn PYTHON on CoddyKit?
You learn by doing. Short interactive lessons pair a clear explanation with a hands-on coding exercise that runs in real time, and a 24/7 AI tutor gives personalized help whenever you get stuck.
Do I get a certificate for completing Deep Learning Academy?
Yes. PRO learners can take an exam and earn a shareable certificate of completion with a verifiable code for the Deep Learning Academy course.
Can I learn PYTHON on my phone?
Yes. CoddyKit is available on the web and as native iOS and Android apps, so you can learn PYTHON on any device and your progress syncs across them.
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