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diff --git a/_sources/book/machinelearning/modeltraining.ipynb b/_sources/book/machinelearning/modeltraining.ipynb
@@ -1644,6 +1644,87 @@
     "results = tuner.fit()\n",
     "print(results.get_best_result(metric=\"mean_accuracy\", mode=\"max\").config)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62334c73",
+   "metadata": {},
+   "source": [
+    "## Use PyTorch with scikit-learn API with `skorch`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ac45c78",
+   "metadata": {},
+   "source": [
+    "PyTorch and scikit-learn are one of the most popular libraries for ML/DL.\n",
+    "\n",
+    "So, why not combine PyTorch with scikit-learn?\n",
+    "\n",
+    "Try `skorch`!\n",
+    "\n",
+    "`skorch` is a high-level library for PyTorch that provides a scikit-learn-compatible neural network module.\n",
+    "\n",
+    "It allows you to use the simple scikit-learn interface for PyTorch.\n",
+    "\n",
+    "Therefore you can integrate PyTorch models into scikit-learn workflows.\n",
+    "\n",
+    "See below for an example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "09a00fe5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install skorch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ad9ed770",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch import nn\n",
+    "from skorch import NeuralNetClassifier\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "class MyModule(nn.Module):\n",
+    "    def __init__(self, num_units=10, nonlin=nn.ReLU()):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        self.dense = nn.Linear(20, num_units)\n",
+    "        self.nonlin = nonlin\n",
+    "        self.output = nn.Linear(num_units, 2)\n",
+    "        self.softmax = nn.Softmax(dim=-1)\n",
+    "\n",
+    "    def forward(self, X, **kwargs):\n",
+    "        X = self.nonlin(self.dense(X))\n",
+    "        X = self.dropout(X)\n",
+    "        X = self.softmax(self.output(X))\n",
+    "        return X\n",
+    "\n",
+    "net = NeuralNetClassifier(\n",
+    "    MyModule,\n",
+    "    max_epochs=10,\n",
+    "    lr=0.1,\n",
+    "    iterator_train__shuffle=True,\n",
+    ")\n",
+    "\n",
+    "pipe = Pipeline([\n",
+    "    ('scale', StandardScaler()),\n",
+    "    ('net', net),\n",
+    "])\n",
+    "\n",
+    "pipe.fit(X, y)\n",
+    "y_proba = pipe.predict_proba(X)"
+   ]
   }
  ],
  "metadata": {

diff --git a/_sources/book/machinelearning/timeseries.ipynb b/_sources/book/machinelearning/timeseries.ipynb
@@ -252,6 +252,61 @@
     "sf.fit(df)\n",
     "sf.predict(h=12, level=[95])"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Time Series with Polars Backend with `functime`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Fast time-series forecasting with `functime`.\n",
+    "\n",
+    "`functime` is a Python library for time series forecasting and feature extraction, built with Polars.\n",
+    "\n",
+    "Since it uses lazy Polars dataframes, `functime` speeds up forecasting and feature engineering.\n",
+    "\n",
+    "Backtesting, cross-validation splitters and metrics are included too.\n",
+    "\n",
+    "It even comes with a LLM agent to analyze and describe your forecasts.\n",
+    "\n",
+    "Check it out!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install functime"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import polars as pl\n",
+    "from functime.cross_validation import train_test_split\n",
+    "from functime.forecasting import linear_model\n",
+    "from functime.metrics import mase\n",
+    "\n",
+    "y_train, y_test = y.pipe(train_test_split(test_size=3))\n",
+    "\n",
+    "forecaster = linear_model(freq=\"1mo\", lags=24)\n",
+    "forecaster.fit(y=y_train)\n",
+    "y_pred = forecaster.predict(fh=3)\n",
+    "\n",
+    "y_pred = linear_model(freq=\"1mo\", lags=24)(y=y_train, fh=3)\n",
+    "\n",
+    "scores = mase(y_true=y_test, y_pred=y_pred, y_train=y_train)"
+   ]
   }
  ],
  "metadata": {

diff --git a/book/machinelearning/modeltraining.html b/book/machinelearning/modeltraining.html
@@ -440,6 +440,7 @@ <h2> Contents </h2>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#model-ensembling-with-combo">5.5.23. Model Ensembling with <code class="docutils literal notranslate"><span class="pre">combo</span></code></a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#residual-plots-with-yellowbrick">5.5.24. Residual Plots with <code class="docutils literal notranslate"><span class="pre">yellowbrick</span></code></a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#powerful-and-distributed-hyperparameter-optimization-with-ray-tune">5.5.25. Powerful and Distributed Hyperparameter Optimization with <code class="docutils literal notranslate"><span class="pre">ray.tune</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#use-pytorch-with-scikit-learn-api-with-skorch">5.5.26. Use PyTorch with scikit-learn API with <code class="docutils literal notranslate"><span class="pre">skorch</span></code></a></li>
 </ul>
             </nav>
         </div>
@@ -1503,6 +1504,63 @@ <h2><span class="section-number">5.5.25. </span>Powerful and Distributed Hyperpa
 </div>
 </div>
 </section>
+<section id="use-pytorch-with-scikit-learn-api-with-skorch">
+<h2><span class="section-number">5.5.26. </span>Use PyTorch with scikit-learn API with <code class="docutils literal notranslate"><span class="pre">skorch</span></code><a class="headerlink" href="#use-pytorch-with-scikit-learn-api-with-skorch" title="Permalink to this heading">#</a></h2>
+<p>PyTorch and scikit-learn are one of the most popular libraries for ML/DL.</p>
+<p>So, why not combine PyTorch with scikit-learn?</p>
+<p>Try <code class="docutils literal notranslate"><span class="pre">skorch</span></code>!</p>
+<p><code class="docutils literal notranslate"><span class="pre">skorch</span></code> is a high-level library for PyTorch that provides a scikit-learn-compatible neural network module.</p>
+<p>It allows you to use the simple scikit-learn interface for PyTorch.</p>
+<p>Therefore you can integrate PyTorch models into scikit-learn workflows.</p>
+<p>See below for an example.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>skorch
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
+<span class="kn">from</span> <span class="nn">skorch</span> <span class="kn">import</span> <span class="n">NeuralNetClassifier</span>
+<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <span class="n">Pipeline</span>
+<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
+
+<span class="k">class</span> <span class="nc">MyModule</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_units</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">nonlin</span><span class="o">=</span><span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">()):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">dense</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="n">num_units</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">nonlin</span> <span class="o">=</span> <span class="n">nonlin</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">output</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">num_units</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">softmax</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Softmax</span><span class="p">(</span><span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nonlin</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dense</span><span class="p">(</span><span class="n">X</span><span class="p">))</span>
+        <span class="n">X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+        <span class="n">X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output</span><span class="p">(</span><span class="n">X</span><span class="p">))</span>
+        <span class="k">return</span> <span class="n">X</span>
+
+<span class="n">net</span> <span class="o">=</span> <span class="n">NeuralNetClassifier</span><span class="p">(</span>
+    <span class="n">MyModule</span><span class="p">,</span>
+    <span class="n">max_epochs</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
+    <span class="n">lr</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
+    <span class="n">iterator_train__shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="n">pipe</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">([</span>
+    <span class="p">(</span><span class="s1">&#39;scale&#39;</span><span class="p">,</span> <span class="n">StandardScaler</span><span class="p">()),</span>
+    <span class="p">(</span><span class="s1">&#39;net&#39;</span><span class="p">,</span> <span class="n">net</span><span class="p">),</span>
+<span class="p">])</span>
+
+<span class="n">pipe</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="n">y_proba</span> <span class="o">=</span> <span class="n">pipe</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
 </section>
 
     <script type="text/x-thebe-config">
@@ -1597,6 +1655,7 @@ <h2><span class="section-number">5.5.25. </span>Powerful and Distributed Hyperpa
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#model-ensembling-with-combo">5.5.23. Model Ensembling with <code class="docutils literal notranslate"><span class="pre">combo</span></code></a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#residual-plots-with-yellowbrick">5.5.24. Residual Plots with <code class="docutils literal notranslate"><span class="pre">yellowbrick</span></code></a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#powerful-and-distributed-hyperparameter-optimization-with-ray-tune">5.5.25. Powerful and Distributed Hyperparameter Optimization with <code class="docutils literal notranslate"><span class="pre">ray.tune</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#use-pytorch-with-scikit-learn-api-with-skorch">5.5.26. Use PyTorch with scikit-learn API with <code class="docutils literal notranslate"><span class="pre">skorch</span></code></a></li>
 </ul>
   </nav></div>
 

diff --git a/book/machinelearning/timeseries.html b/book/machinelearning/timeseries.html
@@ -420,6 +420,7 @@ <h2> Contents </h2>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#more-cross-validation-with-tscv">5.7.3. More Cross-Validation with <code class="docutils literal notranslate"><span class="pre">tscv</span></code></a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#time-series-forecasting-with-machine-learning-with-mlforecast">5.7.4. Time Series Forecasting with Machine Learning with <code class="docutils literal notranslate"><span class="pre">mlforecast</span></code></a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#lightning-fast-time-series-forecasting-with-statsforecast">5.7.5. Lightning Fast Time Series Forecasting with <code class="docutils literal notranslate"><span class="pre">statsforecast</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#time-series-with-polars-backend-with-functime">5.7.6. Time Series with Polars Backend with <code class="docutils literal notranslate"><span class="pre">functime</span></code></a></li>
 </ul>
             </nav>
         </div>
@@ -584,6 +585,42 @@ <h2><span class="section-number">5.7.5. </span>Lightning Fast Time Series Foreca
 </div>
 </div>
 </section>
+<section id="time-series-with-polars-backend-with-functime">
+<h2><span class="section-number">5.7.6. </span>Time Series with Polars Backend with <code class="docutils literal notranslate"><span class="pre">functime</span></code><a class="headerlink" href="#time-series-with-polars-backend-with-functime" title="Permalink to this heading">#</a></h2>
+<p>Fast time-series forecasting with <code class="docutils literal notranslate"><span class="pre">functime</span></code>.</p>
+<p><code class="docutils literal notranslate"><span class="pre">functime</span></code> is a Python library for time series forecasting and feature extraction, built with Polars.</p>
+<p>Since it uses lazy Polars dataframes, <code class="docutils literal notranslate"><span class="pre">functime</span></code> speeds up forecasting and feature engineering.</p>
+<p>Backtesting, cross-validation splitters and metrics are included too.</p>
+<p>It even comes with a LLM agent to analyze and describe your forecasts.</p>
+<p>Check it out!</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span>!pip install functime
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">polars</span> <span class="k">as</span> <span class="nn">pl</span>
+<span class="kn">from</span> <span class="nn">functime.cross_validation</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">from</span> <span class="nn">functime.forecasting</span> <span class="kn">import</span> <span class="n">linear_model</span>
+<span class="kn">from</span> <span class="nn">functime.metrics</span> <span class="kn">import</span> <span class="n">mase</span>
+
+<span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">pipe</span><span class="p">(</span><span class="n">train_test_split</span><span class="p">(</span><span class="n">test_size</span><span class="o">=</span><span class="mi">3</span><span class="p">))</span>
+
+<span class="n">forecaster</span> <span class="o">=</span> <span class="n">linear_model</span><span class="p">(</span><span class="n">freq</span><span class="o">=</span><span class="s2">&quot;1mo&quot;</span><span class="p">,</span> <span class="n">lags</span><span class="o">=</span><span class="mi">24</span><span class="p">)</span>
+<span class="n">forecaster</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="n">y_train</span><span class="p">)</span>
+<span class="n">y_pred</span> <span class="o">=</span> <span class="n">forecaster</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">fh</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+
+<span class="n">y_pred</span> <span class="o">=</span> <span class="n">linear_model</span><span class="p">(</span><span class="n">freq</span><span class="o">=</span><span class="s2">&quot;1mo&quot;</span><span class="p">,</span> <span class="n">lags</span><span class="o">=</span><span class="mi">24</span><span class="p">)(</span><span class="n">y</span><span class="o">=</span><span class="n">y_train</span><span class="p">,</span> <span class="n">fh</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+
+<span class="n">scores</span> <span class="o">=</span> <span class="n">mase</span><span class="p">(</span><span class="n">y_true</span><span class="o">=</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="o">=</span><span class="n">y_pred</span><span class="p">,</span> <span class="n">y_train</span><span class="o">=</span><span class="n">y_train</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
 </section>
 
     <script type="text/x-thebe-config">
@@ -658,6 +695,7 @@ <h2><span class="section-number">5.7.5. </span>Lightning Fast Time Series Foreca
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#more-cross-validation-with-tscv">5.7.3. More Cross-Validation with <code class="docutils literal notranslate"><span class="pre">tscv</span></code></a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#time-series-forecasting-with-machine-learning-with-mlforecast">5.7.4. Time Series Forecasting with Machine Learning with <code class="docutils literal notranslate"><span class="pre">mlforecast</span></code></a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#lightning-fast-time-series-forecasting-with-statsforecast">5.7.5. Lightning Fast Time Series Forecasting with <code class="docutils literal notranslate"><span class="pre">statsforecast</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#time-series-with-polars-backend-with-functime">5.7.6. Time Series with Polars Backend with <code class="docutils literal notranslate"><span class="pre">functime</span></code></a></li>
 </ul>
   </nav></div>
 

diff --git a/searchindex.js b/searchindex.js