| Concept | Description |
|---|---|
| **Basic Framework of a Neural Network** | |
| Deep Learning vs Classical Machine Learning | Learns useful features directly from raw or lightly processed data, rather than requiring hand-engineered inputs |
| Neurons, Weights, and Biases | A neuron computes a weighted sum of its inputs plus a bias; weights and biases are what training actually learns |
| Activation Functions | A nonlinear function (ReLU, sigmoid, tanh) applied to each neuron's output; without it, depth would add no power |
| Layers and Depth | Input, hidden, and output layers; several stacked hidden layers is what makes a network 'deep' |
| The Forward Pass | Computing a prediction by passing input values through every layer's weighted sums and activation functions in sequence |
| **Types, Workflow, and Applications** | |
| Types of Neural Networks | Feedforward, CNN, RNN, reinforcement learning, and concurrent (multi-branch) networks, each suited to different data |
| Workflow | Collect data, preprocess, select architecture, train via backpropagation, regularize, evaluate, then deploy |
| Where Deep Learning Gets Used | Disease detection, yield forecasting from imagery, price forecasting, irrigation scheduling, multi-sensor fusion |
| Advantages | Automatic feature learning, strong performance on unstructured data, and broad architectural flexibility |
| Challenges | Requires substantial labeled data and compute, offers limited interpretability, and carries real overfitting risk |
70 Introduction to Deep Learning
Every technique covered so far, regression, logistic regression, decision trees, random forests, even the unsupervised methods in the previous unit, shares a quiet assumption: a person has already decided which features matter. NDVI, soil moisture, farm size, fertilizer spend, someone chose those columns before any model ever saw the data. Deep learning removes that assumption. A neural network with enough layers can take something close to raw input, the individual pixel values of a drone photograph, the sequence of daily rainfall readings, and learn its own useful features directly from the data, rather than requiring a person to engineer them by hand first.
That capability comes from stacking many simple computational units, called neurons, into layers, and stacking many layers on top of each other, which is where the “deep” in deep learning comes from. This topic lays out the basic framework shared by every deep learning architecture, then previews the specific types, feedforward networks, convolutional networks, recurrent networks, reinforcement learning, and multi-branch architectures, that the rest of this unit covers in depth.
70.1 Basic Framework of a Neural Network
70.1.1 Key Components
- Neuron: the basic computational unit. A neuron takes several numeric inputs, multiplies each by a learned weight, adds a learned bias term, sums the result, and passes that sum through an activation function to produce a single output value.
- Weights and biases: the parameters a network actually learns during training. A weight scales how much influence one input has on a neuron’s output; a bias shifts the neuron’s output up or down independent of the inputs. Everything a trained network “knows” is stored in its weights and biases.
- Activation function: a nonlinear function applied to a neuron’s weighted sum before it is passed on. Without this nonlinearity, stacking layers would be mathematically no more powerful than a single layer, since a chain of purely linear transformations collapses back into one linear transformation. Common choices include the sigmoid function (squashes values into a 0-to-1 range, historically popular, now used mainly for output layers in binary classification), ReLU (Rectified Linear Unit; outputs the input directly if positive, zero otherwise, the default choice for hidden layers in most modern networks because it trains faster and avoids some of sigmoid’s technical problems), and tanh (similar to sigmoid but squashes values into a -1-to-1 range).
- Layers: neurons are organized into an input layer (one unit per feature, no learning happens here), one or more hidden layers (where the actual representation-learning occurs), and an output layer (produces the final prediction, shaped to match the problem: one unit for regression, one unit with a sigmoid for binary classification, multiple units with a softmax for multi-class classification).
- Depth: the number of hidden layers. A network with one or two hidden layers is often still called “shallow” in casual usage; “deep” generally refers to networks with several hidden layers stacked in sequence, each learning increasingly abstract representations of the previous layer’s output.
70.1.2 The Forward Pass
Computing a prediction from a trained (or untrained) network is called a forward pass: input values flow through the input layer, through each hidden layer in sequence, to the output layer, with every neuron along the way computing its weighted sum, adding its bias, and applying its activation function. For a single neuron, this is:
\[ a = f\left(\sum_{i=1}^{n} w_i x_i + b\right) \]
where \(x_i\) are the inputs, \(w_i\) the weights, \(b\) the bias, \(f\) the activation function, and \(a\) the neuron’s output. A full network is simply this calculation repeated across every neuron in every layer, with one layer’s outputs becoming the next layer’s inputs.
The reverse process, adjusting every weight and bias so the network’s predictions get closer to the correct answer, is called backpropagation, and it is covered in depth in Advanced Neural Network Techniques, the next topic.
70.2 Types of Neural Networks
Every specific architecture in the deep learning family builds on the same neuron-and-layer framework above, but arranges and connects those neurons differently depending on what kind of data and pattern it needs to capture. The next four topics in this unit work through five specific types in depth:
- Feedforward networks (multilayer perceptrons): the simplest architecture, information flows in one direction only, from input to output, with no loops or memory of previous inputs. Trained via backpropagation, the foundation every other architecture builds on. Covered first in Advanced Neural Network Techniques.
- Convolutional Neural Networks (CNNs): built for grid-structured data, most commonly images, using a specialized operation called convolution that scans small, learned filters across an image to detect local patterns, edges, textures, shapes, regardless of where in the image they appear. Directly relevant to drone and satellite imagery analysis in agriculture.
- Recurrent Neural Networks (RNNs): built for sequential data, where the order of observations carries information a feedforward network would simply ignore. An RNN maintains an internal memory that carries information from earlier steps in a sequence forward to later ones, useful for a growing season’s week-by-week sensor readings or a multi-year price history.
- Reinforcement learning: a different training paradigm entirely, not a single architecture. Rather than learning from a fixed labeled dataset, a reinforcement learning agent learns by taking actions in an environment and receiving rewards or penalties, gradually learning a policy that maximizes cumulative reward, a natural fit for sequential decision problems like irrigation scheduling.
- Concurrent (multi-branch) networks: architectures with more than one input stream, each processed by its own sub-network, with the resulting representations merged before a final prediction. Useful whenever a decision genuinely depends on combining different kinds of data at once, satellite imagery alongside weather records alongside soil sensor readings, rather than any one source alone.
All five are covered in Advanced Neural Network Techniques (Topic 19), with computer vision applications built on CNNs covered separately in Deep Learning Applications (Topic 20).
70.3 Workflow
- Data collection: deep learning models generally need substantially more training examples than the classical models covered earlier in this book, since they are learning both the features and the final mapping simultaneously, with many more parameters to fit.
- Data preprocessing: images are typically resized to a consistent resolution and pixel values scaled to a 0-to-1 or similar range; sequences are padded or truncated to a consistent length. The standardization principle from Introduction to Unsupervised Learning applies here too.
- Architecture selection: choosing the type of network (feedforward, CNN, RNN, or a concurrent combination) based on the structure of the input data, plus the number of layers, the number of neurons per layer, and the activation functions.
- Training via backpropagation and gradient descent: the network’s weights are adjusted iteratively to reduce a loss function, using the algorithms covered in depth in Optimization Techniques (Topic 21).
- Regularization: deep networks, with their large number of parameters, are especially prone to overfitting; L1 and L2 regularization, also covered in Topic 21, are standard defenses, alongside techniques such as dropout that are specific to neural networks.
- Evaluation: performance is checked on a held-out test set, using the same kind of metrics introduced in Introduction to Supervised Learning, RMSE for regression, accuracy or F1-score for classification.
- Deployment: a trained network is used to score new cases, often the specific point of building an IoT-connected agricultural system in the first place, covered in IoT and Agribusiness Applications (Topic 22).
70.4 Where Deep Learning Gets Used
| Application | Architecture | Example |
|---|---|---|
| Crop disease detection | CNN | Classifying leaf images from a smartphone camera as healthy or diseased |
| Yield forecasting from imagery | CNN | Estimating expected yield directly from satellite or drone imagery |
| Price and demand forecasting | RNN | Forecasting a sequence of future commodity prices from historical price and weather sequences |
| Irrigation scheduling | Reinforcement learning | Learning a watering policy that balances yield against water cost over a season |
| Multi-sensor field monitoring | Concurrent networks | Combining satellite imagery, soil sensor readings, and weather data into one stress prediction |
| General tabular prediction | Feedforward networks | A deep network as an alternative to the classical regression and classification models covered earlier |
70.5 Advantages and Challenges
Advantages
- Automatic feature learning: a deep network can learn useful representations directly from raw or lightly processed data, removing much of the manual feature-engineering burden the classical models in this book rely on.
- Strong performance on unstructured data: images, audio, and free text are exactly the kinds of data where deep learning has produced the largest gains over classical methods, since these data types resist the kind of hand-engineered features tabular data allows.
- Flexibility: the same basic framework, neurons, layers, backpropagation, adapts to an unusually wide range of problem types by changing the architecture around it.
Challenges
- Data hunger: deep networks generally need far more labeled training examples than the classical models covered earlier to reach comparable reliability, a real constraint in agricultural settings where labeled data (confirmed disease diagnoses, ground-truthed yield figures) is expensive to collect.
- Computational cost: training a deep network, especially a CNN on image data, typically requires substantially more computing power than fitting a regression or a random forest.
- Interpretability: a deep network’s millions of weights do not offer the same direct, human-readable explanation a linear regression coefficient or a decision tree’s split rules provide, a real concern when a result has to be explained to a farmer or a regulator.
- Overfitting risk: the very flexibility that makes deep networks powerful also makes them prone to memorizing training data rather than learning generalizable patterns, which is why regularization, covered in Topic 21, matters more here than almost anywhere else in this book.